| Type: | Package |
| Title: | R to Symbolic Data Analysis |
| Version: | 3.2.4 |
| Date: | 2025-06-02 |
| Description: | Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| Depends: | R (≥ 3.4) |
| URL: | https://oldemarrodriguez.com/ |
| Suggests: | testthat (≥ 2.1.0), knitr, rmarkdown |
| RoxygenNote: | 7.3.2 |
| Imports: | vctrs (≥ 0.2.4), dplyr (≥ 0.8.5), forcats, scales, stringr, rlang (≥ 0.4.5), purrr, magrittr, tidyselect, tibble (≥ 3.0.0), stats, RJSONIO, XML, ggplot2, ggpolypath, reshape, glmnet, FactoMineR, princurve, nloptr, sqldf, randomcoloR, kknn, e1071, gbm, randomForest, rpart, neuralnet, umap, xtable, plotly, ggrepel |
| VignetteBuilder: | knitr |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2025-06-02 14:38:47 UTC; r583594 |
| Author: | Oldemar Rodriguez [aut, cre], Jose Emmanuel Chacon [cph], Carlos Aguero [cph], Jorge Arce [cph] |
| Maintainer: | Oldemar Rodriguez <oldemar.rodriguez@ucr.ac.cr> |
| Repository: | CRAN |
| Date/Publication: | 2025-06-02 19:10:02 UTC |
R to Symbolic Data Analysis
Description
This work is framed inside the Symbolic Data Analysis (SDA). The objective of this work is to implement in R to the symbolic case certain techniques of the automatic classification, as well as some lineal models. These implementations will always be made following two fundamental principles in Symbolic Data Analysis like they are: Classic Data Analysis should always be a case particular case of the Symbolic Data Analysis and both, the exit as the input in an Symbolic Data Analysis should be symbolic. We implement for variables of type interval the mean, the median, the mean of the extreme values, the standard deviation, the deviation quartil, the dispersion boxes and the correlation also three new methods are also presented to carry out the lineal regression for variables of type interval. We also implement in this R package the method of Principal Components Analysis in two senses: First, we propose three ways to project the interval variables in the circle of correlations in such way that is reflected the variation or the inexactness of the variables. Second, we propose an algorithm to make the Principal Components Analysis for variables of type histogram. We implement a method for multidimensional scaling of interval data, denominated INTERSCAL.
Details
| Package: | RSDA |
| Type: | Package |
| Version: | 3.2.3 |
| Date: | 2025-05-30 |
| License: | GPL (>=2) |
Most of the function of the package stars from a symbolic data table that can be store in a CSV file withe follwing forma: In the first row the labels $C means that follows a continuous variable, $I means an interval variable, $H means a histogram variables and $S means set variable. In the first row each labels should be follow of a name to variable and to the case of histogram a set variables types the names of the modalities (categories) . In data rows for continuous variables we have just one value, for interval variables we have the minimum and the maximum of the interval, for histogram variables we have the number of modalities and then the probability of each modality and for set variables we have the cardinality of the set and next the elements of the set.
Author(s)
Oldemar Rodriguez Rojas
Maintainer: Oldemar Rodriguez Rojas <oldemar.rodriguez@ucr.ac.cr>
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Billard L., Douzal-Chouakria A. and Diday E. (2011) Symbolic Principal Components For Interval-Valued Observations, Statistical Analysis and Data Mining. 4 (2), 229-246. Wiley.
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
Carvalho F., Souza R.,Chavent M., and Lechevallier Y. (2006) Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognition Letters Volume 27, Issue 3, February 2006, Pages 167-179
Cazes P., Chouakria A., Diday E. et Schektman Y. (1997). Extension de l'analyse en composantes principales a des donnees de type intervalle, Rev. Statistique Appliquee, Vol. XLV Num. 3 pag. 5-24, France.
Diday, E., Rodriguez O. and Winberg S. (2000). Generalization of the Principal Components Analysis to Histogram Data, 4th European Conference on Principles and Practice of Knowledge Discovery in Data Bases, September 12-16, 2000, Lyon, France.
Chouakria A. (1998) Extension des methodes d'analysis factorialle a des donnees de type intervalle, Ph.D. Thesis, Paris IX Dauphine University.
Makosso-Kallyth S. and Diday E. (2012). Adaptation of interval PCA to symbolic histogram variables, Advances in Data Analysis and Classification July, Volume 6, Issue 2, pp 147-159. Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.
See Also
Useful links:
$ operator for histograms
Description
$ operator for histograms
Usage
## S3 method for class 'symbolic_histogram'
x$name
Arguments
x |
..... |
name |
... |
$ operator for modals
Description
$ operator for modals
Usage
## S3 method for class 'symbolic_modal'
x$name = c("cats", "props", "counts")
Arguments
x |
..... |
name |
... |
$ operator for set
Description
$ operator for set
Usage
## S3 method for class 'symbolic_set'
x$name = c("levels", "values")
Arguments
x |
..... |
name |
... |
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
onAttach
Description
onAttach
Usage
.onAttach(...)
Cardiological data example
Description
Cardiological interval data example.
Usage
data(Cardiological)
Format
An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 11 rows and 3 columns.
References
Billard L. and Diday E. (2006).Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Examples
data(Cardiological)
res.cm <- sym.lm(formula = Pulse~Syst+Diast, sym.data = Cardiological, method = 'cm')
pred.cm <- sym.predict(res.cm, Cardiological)
RMSE.L(Cardiological$Pulse, pred.cm$Fitted)
RMSE.U(Cardiological$Pulse,pred.cm$Fitted)
R2.L(Cardiological$Pulse,pred.cm$Fitted)
R2.U(Cardiological$Pulse,pred.cm$Fitted)
deter.coefficient(Cardiological$Pulse,pred.cm$Fitted)
HistRSDAToEcdf
Description
HistRSDAToEcdf
Usage
HistRSDAToEcdf(h)
Arguments
h |
A matrix of histograms |
Value
Transformation in Ecdf object
Author(s)
Jorge Arce Garro
Examples
## Not run:
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
M<-length(Hardwood.cols)
N<-length(Hardwood.names)
BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
Hardwood.quantiles.PCA.2<-quantiles.RSDA.KS(pca.hist$sym.hist.matrix.PCA,100)
h<-Hardwood.quantiles.PCA.2[[1]][[1]]
HistRSDAToEcdf(h)
## End(Not run)
Percentil.Arrow.plot
Description
Percentil.Arrow.plot
Usage
Percentil.Arrow.plot(
quantiles.sym,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name
)
Arguments
quantiles.sym |
Matrix of Quantiles |
concept.names |
Concept Names |
var.names |
Variables to plot the arrows |
Title |
Plot title |
axes.x.label |
Label of axis X |
axes.y.label |
Label of axis Y |
label.name |
Label |
Value
Arrow Plot
Author(s)
Jorge Arce Garro
Examples
## Not run:
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
M<-length(Hardwood.cols)
N<-length(Hardwood.names)
BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
M<-length(Hardwood.cols)
N<-length(Hardwood.names)
BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
label.name<-"Hard Wood"
Title<-"First Principal Plane"
axes.x.label<- "First Principal Component (84.83%)"
axes.y.label<- "Second Principal Component (9.70%)"
concept.names<-c("ACER")
var.names<-c("PC.1","PC.2")
quantile.ACER.plot<-Percentil.Arrow.plot(Hardwood.quantiles.PCA,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name
)
quantile.ACER.plot
## End(Not run)
Lower boundary correlation coefficient.
Description
Compute the lower boundary correlation coefficient for two interval variables.
Usage
R2.L(ref, pred)
Arguments
ref |
Variable that was predicted. |
pred |
The prediction given by the model. |
Value
The lower boundary correlation coefficient.
Author(s)
Oldemar Rodriguez Rojas
References
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.
See Also
sym.glm
Examples
data(int_prost_train)
data(int_prost_test)
res.cm <- sym.lm(lpsa ~ ., sym.data = int_prost_train, method = "cm")
pred.cm <- sym.predict(res.cm, int_prost_test)
R2.L(int_prost_test$lpsa, pred.cm$Fitted)
Upper boundary correlation coefficient.
Description
Compute the upper boundary correlation coefficient for two interval variables.
Usage
R2.U(ref, pred)
Arguments
ref |
Variable that was predicted. |
pred |
The prediction given by the model. |
Value
The upper boundary correlation coefficient.
Author(s)
Oldemar Rodriguez Rojas
References
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.
See Also
sym.glm
Examples
data(int_prost_train)
data(int_prost_test)
res.cm <- sym.lm(lpsa ~ ., sym.data = int_prost_train, method = "cm")
pred.cm <- sym.predict(res.cm, int_prost_test)
R2.U(int_prost_test$lpsa, pred.cm$Fitted)
Lower boundary root-mean-square error
Description
Compute the lower boundary root-mean-square error.
Usage
RMSE.L(ref, pred)
Arguments
ref |
Variable that was predicted. |
pred |
The prediction given by the model. |
Value
The lower boundary root-mean-square error.
Author(s)
Oldemar Rodriguez Rojas.
References
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.
See Also
sym.glm
Upper boundary root-mean-square error
Description
Compute the upper boundary root-mean-square error.
Usage
RMSE.U(ref, pred)
Arguments
ref |
Variable that was predicted. |
pred |
The prediction given by the model. |
Value
The upper boundary root-mean-square error.
Author(s)
Oldemar Rodriguez Rojas
References
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.
See Also
sym.glm
RSDA.to.latex
Description
RSDA.to.latex
Usage
RSDA.to.latex(sym.data)
Author(s)
Jorge Arce Garro
SDS SODAS files to RSDA files.
Description
To convert SDS SODAS files to RSDA files.
Usage
SDS.to.RSDA(file.path, labels = FALSE)
Arguments
file.path |
Disk path where the SODAS *.SDA file is. |
labels |
If we want to include SODAS SDA files lebels in RSDA file. |
Value
A RSDA symbolic data file.
Author(s)
Olger Calderon and Roberto Zuniga.
References
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
See Also
SODAS.to.RSDA
Examples
## Not run:
# We can read the file directly from the SODAS SDA file as follows:
# We can save the file in CSV to RSDA format as follows:
setwd('C:/Program Files (x86)/DECISIA/SODAS version 2.0/bases/')
result <- SDS.to.RSDA(file.path='hani3101.sds')
# We can save the file in CSV to RSDA format as follows:
write.sym.table(result, file='hani3101.csv', sep=';',dec='.', row.names=TRUE,
## End(Not run)
XML SODAS files to RSDA files.
Description
To convert XML SODAS files to RSDA files.
Usage
SODAS.to.RSDA(XMLPath, labels = T)
Arguments
XMLPath |
Disk path where the SODAS *.XML file is. |
labels |
If we want to include SODAS XML files lebels in RSDA file. |
Value
A RSDA symbolic data file.
Author(s)
Olger Calderon and Roberto Zuniga.
References
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
See Also
SDS.to.RSDA
Examples
## Not run:
# We can read the file directly from the SODAS XML file as follows:
# abalone<-SODAS.to.RSDA('C:/Program Files (x86)/DECISIA/SODAS version 2.0/bases/abalone.xml)
# We can save the file in CSV to RSDA format as follows:
# write.sym.table(sodas.ex1, file='abalone.csv', sep=';',dec='.', row.names=TRUE,
# col.names=TRUE)
# We read the file from the CSV file,
# this is not necessary if the file is read directly from
# XML using SODAS.to.RSDA as in the first statement in this example.
data(abalone)
res <- sym.interval.pca(abalone, "centers")
sym.scatterplot(sym.var(res$Sym.Components, 1), sym.var(res$Sym.Components, 2),
labels = TRUE, col = "red", main = "PCA Oils Data"
)
sym.scatterplot3d(sym.var(res$Sym.Components, 1), sym.var(res$Sym.Components, 2),
sym.var(res$Sym.Components, 3),
color = "blue", main = "PCA Oils Data"
)
sym.scatterplot.ggplot(sym.var(res$Sym.Components, 1), sym.var(res$Sym.Components, 2),
labels = TRUE
)
sym.circle.plot(res$Sym.Prin.Correlations)
## End(Not run)
Sym.PCA.Hist.PCA.k.plot
Description
Sym.PCA.Hist.PCA.k.plot
Usage
Sym.PCA.Hist.PCA.k.plot(
data.sym.df,
title.graph,
concepts.name,
title.x,
title.y,
pca.axes
)
Arguments
data.sym.df |
Bins's projections |
title.graph |
Plot title |
concepts.name |
Concepts names |
title.x |
Label of axis X |
title.y |
Label of axis Y |
pca.axes |
Principal Component |
Value
Concepts projected onto the Principal component chosen
Author(s)
Jorge Arce Garro
Examples
## Not run:
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
M<-length(Hardwood.cols)
N<-length(Hardwood.names)
BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
ACER.p1<-Sym.PCA.Hist.PCA.k.plot(data.sym.df = pca.hist$Bins.df,
title.graph = " ",
concepts.name = c("ACER"),
title.x = "First Principal Component (84.83%)",
title.y = "Frequency",
pca.axes = 1)
ACER.p1
## End(Not run)
Us crime classic data table
Description
Us crime classic data table that can be used to generate symbolic data tables.
Usage
data(USCrime)
Format
An object of class data.frame with 1994 rows and 103 columns.
Source
http://archive.ics.uci.edu/ml/
References
HASTIE, T., TIBSHIRANI, R. and FRIEDMAN, J. (2008). The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.
Examples
## Not run:
data(USCrime)
us.crime <- USCrime
dim(us.crime)
head(us.crime)
summary(us.crime)
names(us.crime)
nrow(us.crime)
result <- classic.to.sym(us.crime,
concept = "state",
variables = c(NumInShelters, NumImmig),
variables.types = c(
NumInShelters = type.histogram(),
NumImmig = type.histogram()
)
)
result
## End(Not run)
Symbolic interval data example
Description
Symbolic data matrix with all the variables of interval type.
Usage
data(VeterinaryData)
Format
$I Height Height $I Weight Weight
1 $I 120.0 180.0 $I 222.2 354.0
2 $I 158.0 160.0 $I 322.0 355.0
3 $I 175.0 185.0 $I 117.2 152.0
4 $I 37.9 62.9 $I 22.2 35.0
5 $I 25.8 39.6 $I 15.0 36.2
6 $I 22.8 58.6 $I 15.0 51.8
7 $I 22.0 45.0 $I 0.8 11.0
8 $I 18.0 53.0 $I 0.4 2.5
9 $I 40.3 55.8 $I 2.1 4.5
10 $I 38.4 72.4 $I 2.5 6.1
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Examples
data(VeterinaryData)
VeterinaryData
Extract or replace parts of a Symbolic Data Table
Description
Extract or replace parts of a Symbolic Data Table
Usage
## S3 method for class 'sym.data.table'
x[i, j]
subset for symbolic table
Description
subset for symbolic table
Usage
## S3 method for class 'symbolic_tbl'
x[i, j, drop = FALSE, ...]
SODAS XML data file.
Description
Example of SODAS XML data file converted in a CSV file in RSDA format.
Usage
data(abalone)
Format
An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 24 rows and 7 columns.
Source
http://www.info.fundp.ac.be/asso/sodaslink.htm
References
Bock H-H. and Diday E. (eds.) (2000).Analysis of Symbolic Data. Exploratory methods for extracting statistical information fromcomplex data. Springer, Germany.
Examples
data(abalone)
res <- sym.pca(abalone, 'centers')
plot(res, choix = "ind")
plot(res, choix = "var")
a data.frame
Description
a data.frame
Usage
## S3 method for class 'symbolic_histogram'
as.data.frame(x, ...)
Arguments
x |
..... |
... |
... |
convertir a data.frame
Description
convertir a data.frame
Usage
## S3 method for class 'symbolic_interval'
as.data.frame(x, ...)
Arguments
x |
a symbolic interval vector |
... |
further arguments passed to or from other methods. |
Extract values
Description
Extract values
Usage
## S3 method for class 'symbolic_modal'
as.data.frame(x, ...)
Arguments
x |
An object to be converted |
... |
Further arguments to be passed from or to other methods. |
convertir a data.frame
Description
convertir a data.frame
Usage
## S3 method for class 'symbolic_set'
as.data.frame(x, ...)
Arguments
x |
a symbolic interval vector |
... |
further arguments passed to or from other methods. |
Burt Matrix
Description
Burt Matrix
Usage
calc.burt.sym(sym.data, pos.var)
Arguments
sym.data |
ddd |
pos.var |
ddd |
calc.k
Description
calc.k
Usage
calc.k(var.sym.X, var.sym.Y)
calc.matrix.min
Description
calc.matrix.min
Usage
calc.matrix.min(data.max)
quantiles.RSDA
Description
quantiles.RSDA
Usage
calculate.quantils.RSDA(histogram.RSDA, num.quantils)
Arguments
histogram.RSDA |
A histogram |
num.quantils |
Number of quantiles |
Value
Quantiles of a Histogram
Cardiological data example
Description
Cardiological interval data example.
Usage
data(Cardiological)
Format
An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 44 rows and 5 columns.
References
Billard L. and Diday E. (2006).Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Compute centers of the interval
Description
Compute centers of the interval
Usage
centers.interval(sym.data)
Arguments
sym.data |
Symbolic interval data table. |
Value
Centers of teh intervals.
Author(s)
Jorge Arce.
References
Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.
Hastie,T. (1984).Principal Curves and Surface. Ph.D Thesis Stanford University.
Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.
Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.
Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.
See Also
sym.interval.pc
process.histogram.variable
Description
process.histogram.variable
Usage
centers.interval.j(sym.data)
cfa.CVPRealz
Description
cfa.CVPRealz
Usage
cfa.CVPRealz(sym.data, TFilas, TColumnas, TT, z)
cfa.Czz
Description
cfa.Czz
Usage
cfa.Czz(sym.data, TFilas, TColumnas, VPRealz, d)
cfa.MatrixZ
Description
cfa.MatrixZ
Usage
cfa.MatrixZ(sym.data, TFilas, TColumnas)
cfa.minmax
Description
cfa.minmax
Usage
cfa.minmax(
sym.data,
TFilas,
TFilasMin,
TFilasMax,
TColumnas,
TColumnasMin,
TColumnasMax,
Total,
VP,
VPzz
)
cfa.minmax.new
Description
cfa.minmax.new
Usage
cfa.minmax.new(
sym.data,
TFilas,
TFilasMin,
TFilasMax,
TColumnas,
TColumnasMin,
TColumnasMax,
Total,
VP,
VPzz
)
cfa.totals
Description
cfa.totals
Usage
cfa.totals(sym.data)
Check duplicated names in a quo
Description
Check duplicated names in a quo
Usage
check_quo_duplicated_names(x)
Generate a symbolic data frame
Description
Generate a symbolic data table from a classic data table.
Usage
classic.to.sym(
x = NULL,
concept = NULL,
variables = tidyselect::everything(),
default.numeric = sym.interval,
default.categorical = sym.modal,
...
)
Arguments
x |
A data.frame. |
concept |
These are the variable that we are going to use a concepts. |
variables |
These are the variables that we want to include in the symbolic data table. |
default.numeric |
function to use for numeric variables |
default.categorical |
function to use for categorical variables |
... |
A vector with names and the type of symbolic data to use, the available types are type_histogram (), type_continuous (), type.set (), type.modal (), by default type_histogram () is used for numeric variables and type_modal () for the categorical variables. |
Value
a [tibble][tibble::tibble-package]
References
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
Generic function for the correlation
Description
This function compute the symbolic correlation
Usage
cor(x, ...)
## Default S3 method:
cor(
x,
y = NULL,
use = "everything",
method = c("pearson", "kendall", "spearman"),
...
)
## S3 method for class 'symbolic_interval'
cor(x, y, method = c("centers", "billard"), ...)
## S3 method for class 'symbolic_tbl'
cor(x, ...)
Arguments
x |
A symbolic variable. |
... |
As in R cor function. |
y |
A symbolic variable. |
use |
An optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings 'everything', 'all.obs', 'complete.obs', 'na.or.complete', or 'pairwise.complete.obs'. |
method |
The method to be use. |
Value
Return a real number in [-1,1].
Author(s)
Oldemar Rodriguez Rojas
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.
Generic function for the covariance
Description
This function compute the symbolic covariance.
Usage
cov(x, ...)
## Default S3 method:
cov(
x,
y = NULL,
use = "everything",
method = c("pearson", "kendall", "spearman"),
...
)
## S3 method for class 'symbolic_interval'
cov(x, y, method = c("centers", "billard"), na.rm = FALSE, ...)
## S3 method for class 'symbolic_tbl'
cov(x, ...)
Arguments
x |
First symbolic variables. |
... |
As in R cov function. |
y |
Second symbolic variables. |
use |
an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings 'everything', 'all.obs', 'complete.obs', 'na.or.complete', or 'pairwise.complete.obs'. |
method |
The method to be use. |
na.rm |
As in R cov function. |
Value
Return a real number.
Author(s)
Oldemar Rodriguez Rojas
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.
data.frame.to.RSDA.inteval.table
Description
data.frame.to.RSDA.inteval.table
Usage
data.frame.to.RSDA.inteval.table(data.df)
data.frame.to.RSDA.inteval.table.j
Description
data.frame.to.RSDA.inteval.table.j
Usage
data.frame.to.RSDA.inteval.table.j(data.df)
Compute the determination cosfficient
Description
The determination coefficient represents a goodness-of-fit measure commonly used in regression analysis to capture the adjustment quality of a model.
Usage
deter.coefficient(ref, pred)
Arguments
ref |
Variable that was predicted. |
pred |
The prediction given by the model. |
Value
Return the determination cosfficient.
Author(s)
Oldemar Rodriguez Rojas
References
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.
See Also
sym.glm
Examples
data(int_prost_test)
data(int_prost_train)
res.cm <- sym.lm(lpsa ~ ., sym.data = int_prost_train, method = "cm")
pred.cm <- sym.predict(res.cm, int_prost_test)
deter.coefficient(int_prost_test$lpsa, pred.cm$Fitted)
Compute a distance vector
Description
Compute a distance vector
Usage
dist.vect(vector1, vector2)
Arguments
vector1 |
First vector. |
vector2 |
Second vector. |
Value
Eclidean distance between the two vectors.
Author(s)
Jorge Arce
References
Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.
Hastie,T. (1984). Principal Curves and Surface. Ph.D. Thesis Stanford University.
Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.
Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.
Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.
See Also
sym.interval.pc
Compute the distance vector matrix
Description
Compute the distance vector matrix.
Usage
dist.vect.matrix(vector, Matrix)
Arguments
vector |
An n dimensional vector. |
Matrix |
An n x n matrix. |
Value
The distance.
Author(s)
Jorge Arce.
References
Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.
Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.
Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.
Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.
Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.
See Also
sym.interval.pc
Data example to generate symbolic objets
Description
This is a small data example to generate symbolic objets.
Usage
data(ex1_db2so)
Format
An object of class data.frame with 19 rows and 5 columns.
References
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
Examples
data(ex1_db2so)
ex1 <- ex1_db2so
result <- classic.to.sym(
x = ex1_db2so,
concept = c(state, sex),
variables = c(county, group, age),
county = mean(county),
age_hist = sym.histogram(age, breaks = pretty(ex1_db2so$age, 5))
)
result
Correspondence Analysis Example
Description
Correspondence Analysis for Symbolic MultiValued Variables example.
Usage
data(ex_cfa1)
Format
An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 4 rows and 4 columns.
References
Rodriguez, O. (2011). Correspondence Analysis for Symbolic MultiValued Variables. Workshop in Symbolic Data Analysis Namur, Belgium
Correspondence Analysis Example
Description
Correspondence Analysis for Symbolic MultiValued Variables example.
Usage
data(ex_cfa2)
Format
An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 6 rows and 5 columns.
References
Rodriguez, O. (2011). Correspondence Analysis for Symbolic MultiValued Variables. Workshop in Symbolic Data Analysis Namur, Belgium
Multiple Correspondence Analysis Example
Description
example for the sym.mcfa function.
example for the sym.mcfa function.
Usage
data(ex_mcfa1)
ex_mcfa1
Format
An object of class data.frame with 130 rows and 5 columns.
An object of class data.frame with 130 rows and 5 columns.
Examples
data("ex_mcfa1")
sym.table <- classic.to.sym(ex_mcfa1,
concept = suspect,
hair = sym.set(hair),
eyes = sym.set(eyes),
region = sym.set(region))
res <- sym.mcfa(sym.table, c(1,2))
mcfa.scatterplot(res[,1], res[,2], sym.data = sym.table, pos.var = c(1,2))
data("ex_mcfa1")
sym.table <- classic.to.sym(
x = ex_mcfa1,
concept = "suspect",
variables = c(hair, eyes, region),
hair = sym.set(hair),
eyes = sym.set(eyes),
region = sym.set(region)
)
sym.table
Multiple Correspondence Analysis Example
Description
example for the sym.mcfa function.
Usage
data(ex_mcfa2)
Format
An object of class data.frame with 130 rows and 7 columns.
Examples
data("ex_mcfa2")
ex <- classic.to.sym(ex_mcfa2,
concept = employee_id,
variables = c(employee_id, salary, region, evaluation, years_worked),
salary = sym.set(salary),
region = sym.set(region),
evaluation = sym.set(evaluation),
years_worked = sym.set(years_worked))
res <- sym.mcfa(ex, c(1,2,3,4))
mcfa.scatterplot(res[,1], res[,2], sym.data = ex, pos.var = c(1,2,3,4))
Data Example 1
Description
This a symbolic data table with variables of continuos, interval, histogram and set types.
Usage
data(example1)
Format
The labels $C means that follows a continuous variable, $I means an interval
variable, $H means a histogram variables and $S means set variable. In the
first row each labels should be follow of a name to variable and to the case
of histogram a set variables types the names of the modalities (categories).
In data rows for continuous variables we have just one value, for interval
variables we have the minimum and the maximum of the interval, for histogram
variables we have the number of modalities and then the probability of each
modality and for set variables we have the cardinality of the set and next
the elements of the set.
The format is the *.csv file is:
$C F1 $I F2 F2 $M F3 M1 M2 M3 $S F4 e a 2 3 g b 1 4 i k c d
Case1 $C 2.8 $I 1 2 $M 3 0.1 0.7 0.2 $S 12 1 0 0 0 1 0 0 0 1 1 0 0
Case2 $C 1.4 $I 3 9 $M 3 0.6 0.3 0.1 $S 12 0 1 0 0 0 1 0 0 0 0 1 1
Case3 $C 3.2 $I -1 4 $M 3 0.2 0.2 0.6 $S 12 0 0 1 0 0 1 1 0 0 0 1 0
Case4 $C -2.1 $I 0 2 $M 3 0.9 0.0 0.1 $S 12 0 1 0 1 0 0 0 1 0 0 1 0
Case5 $C -3.0 $I -4 -2 $M 3 0.6 0.0 0.4 $S 12 1 0 0 0 1 0 0 0 1 1 0 0
The internal format is:
$N
[1] 5
$M
[1] 4
$sym.obj.names
[1] 'Case1' 'Case2' 'Case3' 'Case4' 'Case5'
$sym.var.names
[1] 'F1' 'F2' 'F3' 'F4'
$sym.var.types
[1] '$C' '$I' '$H' '$S'
$sym.var.length
[1] 1 2 3 4
$sym.var.starts
[1] 2 4 8 13
$meta
$C F1 $I F2 F2 $M F3 M1 M2 M3 $S F4 e a 2 3 g b 1 4 i k c d
Case1 $C 2.8 $I 1 2 $M 3 0.1 0.7 0.2 $S 12 1 0 0 0 1 0 0 0 1 1 0 0
Case2 $C 1.4 $I 3 9 $M 3 0.6 0.3 0.1 $S 12 0 1 0 0 0 1 0 0 0 0 1 1
Case3 $C 3.2 $I -1 4 $M 3 0.2 0.2 0.6 $S 12 0 0 1 0 0 1 1 0 0 0 1 0
Case4 $C -2.1 $I 0 2 $M 3 0.9 0.0 0.1 $S 12 0 1 0 1 0 0 0 1 0 0 1 0
Case5 $C -3.0 $I -4 -2 $M 3 0.6 0.0 0.4 $S 12 1 0 0 0 1 0 0 0 1 1 0 0
$data
F1 F2 F2.1 M1 M2 M3 e a 2 3 g b 1 4 i k c d
Case1 2.8 1 2 0.1 0.7 0.2 1 0 0 0 1 0 0 0 1 1 0 0
Case2 1.4 3 9 0.6 0.3 0.1 0 1 0 0 0 1 0 0 0 0 1 1
Case3 3.2 -1 4 0.2 0.2 0.6 0 0 1 0 0 1 1 0 0 0 1 0
Case4 -2.1 0 2 0.9 0.0 0.1 0 1 0 1 0 0 0 1 0 0 1 0
Case5 -3.0 -4 -2 0.6 0.0 0.4 1 0 0 0 1 0 0 0 1 1 0 0
References
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
Examples
data(example1)
example1
Data Example 2
Description
This a symbolic data table with variables of continuos, interval, histogram and set types.
Usage
data(example2)
Format
$C F1 $I F2 F2 $M F3 M1 M2 M3 $C F4 $S F5 e a 2 3 g b 1 4 i k c d
Case1 $C 2.8 $I 1 2 $M 3 0.1 0.7 0.2 $C 6.0 $S 12 1 0 0 0 1 0 0 0 1 1 0 0
Case2 $C 1.4 $I 3 9 $M 3 0.6 0.3 0.1 $C 8.0 $S 12 0 1 0 0 0 1 0 0 0 0 1 1
Case3 $C 3.2 $I -1 4 $M 3 0.2 0.2 0.6 $C -7.0 $S 12 0 0 1 0 0 1 1 0 0 0 1 0
Case4 $C -2.1 $I 0 2 $M 3 0.9 0.0 0.1 $C 0.0 $S 12 0 1 0 1 0 0 0 1 0 0 1 0
Case5 $C -3.0 $I -4 -2 $M 3 0.6 0.0 0.4 $C -9.5 $S 12 1 0 0 0 1 0 0 0 1 1 0 0
Examples
data(example2)
example2
Data Example 3
Description
This a symbolic data table with variables of continuos, interval, histogram and set types.
Usage
data(example3)
Format
$C F1 $I F2 F2 $M F3 M1 M2 M3 $C F4 $S F5 e a 2 3 g b 1 4 i k c d $I F6 F6 $I F7 F7 Case1 $C 2.8 $I 1 2 $M 3 0.1 0.7 0.2 $C 6.0 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 $I 0.00 90.00 $I 9 24 Case2 $C 1.4 $I 3 9 $M 3 0.6 0.3 0.1 $C 8.0 $S 12 0 1 0 0 0 1 0 0 0 0 1 1 $I -90.00 98.00 $I -9 9 Case3 $C 3.2 $I -1 4 $M 3 0.2 0.2 0.6 $C -7.0 $S 12 0 0 1 0 0 1 1 0 0 0 1 0 $I 65.00 90.00 $I 65 70 Case4 $C -2.1 $I 0 2 $M 3 0.9 0.0 0.1 $C 0.0 $S 12 0 1 0 1 0 0 0 1 0 0 1 0 $I 45.00 89.00 $I 25 67 Case5 $C -3.0 $I -4 -2 $M 3 0.6 0.0 0.4 $C -9.5 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 $I 20.00 40.00 $I 9 40 Case6 $C 0.1 $I 10 21 $M 3 0.0 0.7 0.3 $C -1.0 $S 12 1 0 0 0 0 0 1 0 1 0 0 0 $I 5.00 8.00 $I 5 8 Case7 $C 9.0 $I 4 21 $M 3 0.2 0.2 0.6 $C 0.5 $S 12 1 1 1 0 0 0 0 0 0 0 0 0 $I 3.14 6.76 $I 4 6
Examples
data(example3)
example3
Data Example 4
Description
data(example4) example4
Usage
data(example4)
Format
$C 2.8 $I 1 2 $M 3 0.1 0.7 0.2 $C 6 $S F4 e a 2 3 g b 1 4 i k c d $I 0 90 Case2 $C 1.4 $I 3 9 $M 3 0.6 0.3 0.1 $C 8.0 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 $I -90.00 98.00 Case3 $C 3.2 $I -1 4 $M 3 0.2 0.2 0.6 $C -7.0 $S 12 0 1 0 0 0 1 0 0 0 0 1 1 $I 65.00 90.00 Case4 $C -2.1 $I 0 2 $M 3 0.9 0.0 0.1 $C 0.0 $S 12 0 0 1 0 0 1 1 0 0 0 1 0 $I 45.00 89.00 Case5 $C -3.0 $I -4 -2 $M 3 0.6 0.0 0.4 $C -9.5 $S 12 0 1 0 1 0 0 0 1 0 0 1 0 $I 90.00 990.00 Case6 $C 0.1 $I 10 21 $M 3 0.0 0.7 0.3 $C -1.0 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 $I 5.00 8.00 Case7 $C 9.0 $I 4 21 $M 3 0.2 0.2 0.6 $C 0.5 $S 12 1 1 0 0 0 0 1 0 0 0 0 1 $I 3.14 6.76
Examples
data(example4)
example4
Data Example 5
Description
This a symbolic data matrix wint continuos, interval, histograma a set data types.
Usage
data(example5)
Format
$H F0 M01 M02 $C F1 $I F2 F2 $H F3 M1 M2 M3 $S F4 E1 E2 E3 E4
Case1 $H 2 0.1 0.9 $C 2.8 $I 1 2 $H 3 0.1 0.7 0.2 $S 4 e g k i
Case2 $H 2 0.7 0.3 $C 1.4 $I 3 9 $H 3 0.6 0.3 0.1 $S 4 a b c d
Case3 $H 2 0.0 1.0 $C 3.2 $I -1 4 $H 3 0.2 0.2 0.6 $S 4 2 1 b c
Case4 $H 2 0.2 0.8 $C -2.1 $I 0 2 $H 3 0.9 0.0 0.1 $S 4 3 4 c a
Case5 $H 2 0.6 0.4 $C -3.0 $I -4 -2 $H 3 0.6 0.0 0.4 $S 4 e i g k
Examples
data(example5)
example5
Data Example 6
Description
This a symbolic data matrix wint continuos, interval, histograma a set data types.
Usage
data(example6)
Format
$C F1 $M F2 M1 M2 M3 M4 M5 $I F3 F3 $M F4 M1 M2 M3 $C F5 $S F4 e a 2 3 g b 1 4 i k c d Case1 $C 2.8 $M 5 0.1 0.1 0.1 0.1 0.6 $I 1 2 $M 3 0.1 0.7 0.2 $C 6.0 $S 12 1 0 0 0 1 0 0 0 1 1 0 0 Case2 $C 1.4 $M 5 0.1 0.1 0.1 0.1 0.6 $I 3 9 $M 3 0.6 0.3 0.1 $C 8.0 $S 12 0 1 0 0 0 1 0 0 0 0 1 1 Case3 $C 3.2 $M 5 0.1 0.1 0.1 0.1 0.6 $I -1 4 $M 3 0.2 0.2 0.6 $C -7.0 $S 12 0 0 1 0 0 1 1 0 0 0 1 0 Case4 $C -2.1 $M 5 0.1 0.1 0.1 0.1 0.6 $I 0 2 $M 3 0.9 0.0 0.1 $C 0.0 $S 12 0 1 0 1 0 0 0 1 0 0 1 0 Case5 $C -3.0 $M 5 0.1 0.1 0.1 0.1 0.6 $I -4 -2 $M 3 0.6 0.0 0.4 $C -9.5 $S 12 1 0 0 0 1 0 0 0 1 1 0 0
Examples
data(example6)
example6
Data Example 7
Description
This a symbolic data matrix wint continuos, interval, histograma a set data types.
Usage
data(example6)
Format
$C F1 $H F2 M1 M2 M3 M4 M5 $I F3 F3 $H F4 M1 M2 M3 $C F5
Case1 $C 2.8 $H 5 0.1 0.2 0.3 0.4 0.0 $I 1 2 $H 3 0.1 0.7 0.2 $C 6.0
Case2 $C 1.4 $H 5 0.2 0.1 0.5 0.1 0.2 $I 3 9 $H 3 0.6 0.3 0.1 $C 8.0
Case3 $C 3.2 $H 5 0.1 0.1 0.2 0.1 0.5 $I -1 4 $H 3 0.2 0.2 0.6 $C -7.0
Case4 $C -2.1 $H 5 0.4 0.1 0.1 0.1 0.3 $I 0 2 $H 3 0.9 0.0 0.1 $C 0.0
Case5 $C -3.0 $H 5 0.6 0.1 0.1 0.1 0.1 $I -4 -2 $H 3 0.6 0.0 0.4 $C -9.5
Examples
data(example7)
example7
Extract data
Description
Extract data
Usage
extract_data(x, name = NA)
Extract meta data
Description
Extract meta data
Usage
extract_meta(x, name = NA)
Face Data Example
Description
Symbolic data matrix with all the variables of interval type.
Usage
data('facedata')
Format
$I;AD;AD;$I;BC;BC;.........
HUS1;$I;168.86;172.84;$I;58.55;63.39;.........
HUS2;$I;169.85;175.03;$I;60.21;64.38;.........
HUS3;$I;168.76;175.15;$I;61.4;63.51;.........
INC1;$I;155.26;160.45;$I;53.15;60.21;.........
INC2;$I;156.26;161.31;$I;51.09;60.07;.........
INC3;$I;154.47;160.31;$I;55.08;59.03;.........
ISA1;$I;164;168;$I;55.01;60.03;.........
ISA2;$I;163;170;$I;54.04;59;.........
ISA3;$I;164.01;169.01;$I;55;59.01;.........
JPL1;$I;167.11;171.19;$I;61.03;65.01;.........
JPL2;$I;169.14;173.18;$I;60.07;65.07;.........
JPL3;$I;169.03;170.11;$I;59.01;65.01;.........
KHA1;$I;149.34;155.54;$I;54.15;59.14;.........
KHA2;$I;149.34;155.32;$I;52.04;58.22;.........
KHA3;$I;150.33;157.26;$I;52.09;60.21;.........
LOT1;$I;152.64;157.62;$I;51.35;56.22;.........
LOT2;$I;154.64;157.62;$I;52.24;56.32;.........
LOT3;$I;154.83;157.81;$I;50.36;55.23;.........
PHI1;$I;163.08;167.07;$I;66.03;68.07;.........
PHI2;$I;164;168.03;$I;65.03;68.12;.........
PHI3;$I;161.01;167;$I;64.07;69.01;.........
ROM1;$I;167.15;171.24;$I;64.07;68.07;.........
ROM2;$I;168.15;172.14;$I;63.13;68.07;.........
ROM3;$I;167.11;171.19;$I;63.13;68.03;.........
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Examples
## Not run:
data(facedata)
res.vertex.ps <- sym.interval.pc(facedata,'vertex',150,FALSE,FALSE,TRUE)
class(res.vertex.ps$sym.prin.curve) <- c('sym.data.table')
sym.scatterplot(res.vertex.ps$sym.prin.curve[,1], res.vertex.ps$sym.prin.curve[,2],
labels=TRUE,col='red',main='PSC Face Data')
## End(Not run)
fixed.pca.j.new
Description
fixed.pca.j.new
Usage
fixed.pca.j.new(sym.data, fixed.matrix)
Author(s)
Jorge Arce Garro
Symbolic modal conversion functions to and from Character
Description
Symbolic modal conversion functions to and from Character
Usage
## S3 method for class 'symbolic_histogram'
format(x, ...)
Arguments
x |
An object to be converted |
... |
Further arguments to be passed from or to other methods. |
Symbolic interval conversion functions to and from Character
Description
Symbolic interval conversion functions to and from Character
Usage
## S3 method for class 'symbolic_interval'
format(x, ...)
Arguments
x |
An object to be converted |
... |
Further arguments to be passed from or to other methods. |
Symbolic modal conversion functions to and from Character
Description
Symbolic modal conversion functions to and from Character
Usage
## S3 method for class 'symbolic_modal'
format(x, ...)
Arguments
x |
An object to be converted |
... |
Further arguments to be passed from or to other methods. |
Symbolic set conversion functions to and from Character
Description
Symbolic set conversion functions to and from Character
Usage
## S3 method for class 'symbolic_set'
format(x, ...)
Arguments
x |
An object to be converted |
... |
Further arguments to be passed from or to other methods. |
generate.columns.interval
Description
generate.columns.interval
Usage
generate.columns.interval(data)
generate.columns.multivalued
Description
generate.columns.multivalued
Usage
generate.columns.multivalued(data)
Author(s)
Jorge Arce Garro
generate.columns.set
Description
generate.columns.set
Usage
generate.columns.set(data)
Author(s)
Jorge Arce Garro
generate.sym.table
Description
generate.sym.table
Usage
generate.sym.table(sym.data)
Author(s)
Jorge Arce Garro
Projections onto PCA
Description
Calculate the interval projection onto the principal components
Usage
get.limits.PCA(sym.data, matrix.stan, min.stan, max.stan, svd, nn, mm)
Arguments
sym.data |
An interval matrix |
matrix.stan |
A standardized matrix |
min.stan |
A matrix of minimum values standardized for each interval |
max.stan |
A matrix of maximum values standardized for each interval |
svd |
An eigen vectors matrix |
nn |
Number of concepts |
mm |
Number of variables |
Value
Concept Projections onto the principal components and correlation circle
Projections onto PCA
Description
Calculate the interval projection onto the principal components
Usage
get.limits.PCA.indivduals(
sym.data,
matrix.stan,
min.stan,
max.stan,
svd,
nn,
mm
)
Arguments
sym.data |
An interval matrix |
matrix.stan |
A standardized matrix |
min.stan |
A matrix of minimum values standardized for each interval |
max.stan |
A matrix of maximum values standardized for each interval |
svd |
An eigen vectors matrix |
nn |
Number of concepts |
mm |
Number of variables |
Value
Concept Projections onto the principal components
Interval Matrix associated to a Histogram Matrix
Description
Interval Matrix associated to a Histogram Matrix
Usage
get.sym.interval.limits(x)
Arguments
x |
A Histogram matrix |
Value
An Interval Matrix
Author(s)
Jorge Arce Garro
Extract categories
Description
Extract categories
Usage
get_cats(x, ...)
Arguments
x |
An object to be converted |
... |
Further arguments to be passed from or to other methods. |
Extract prop
Description
Extract prop
Usage
get_props(x, ...)
Arguments
x |
An object to be converted |
... |
Further arguments to be passed from or to other methods. |
Hard Wood Data Example
Description
Symbolic Histogram matrix.
Usage
data('hardwoodBrito')
Format
An object of class symbolic_tbl (inherits from symbolic_tbl, symbolic_tbl, symbolic_tbl, tbl_df, tbl, data.frame) with 5 rows and 4 columns.
References
Brito P. and Dias S. (2022). Analysis of Distributional Data. CRC Press, United States of America.
Examples
## Not run:
data(hardwoodBrito)
hardwoodBrito
## End(Not run)
Linear regression model data example.
Description
Linear regression model interval-valued data example.
Usage
data(int_prost_test)
Format
An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 30 rows and 9 columns.
References
HASTIE, T., TIBSHIRANI, R. and FRIEDMAN, J. (2008). The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.
Linear regression model data example.
Description
Linear regression model interval-valued data example.
Usage
data(int_prost_train)
Format
An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 67 rows and 9 columns.
References
HASTIE, T., TIBSHIRANI, R. and FRIEDMAN, J. (2008). The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.
generate.columns.interval
Description
generate.columns.interval
Usage
intersection.interval(Interval.1, Interval.2)
Author(s)
Jorge Arce Garro
calcula centros
Description
calcula centros
Usage
interval.centers(x)
Arguments
x |
tabla simbolica todos intervalos |
Histogram plot for an interval variable
Description
Histogram plot for an interval variable
Usage
interval.histogram.plot(x, n.bins, ...)
Arguments
x |
An symbolic data table. |
n.bins |
Numbers of breaks of the histogram. |
... |
Arguments to be passed to the barplot method. |
Value
A list with componets : frequency and histogram
Examples
data(oils)
res <- interval.histogram.plot(x = oils[, 3], n.bins = 3)
res
Calculate the large of each interval
Description
Calculate the large of each interval
Usage
interval.large(x)
Arguments
x |
An interval matrix |
Value
A matrix with the large of each interval.
Examples
## Not run:
data(oils)
interval.large(oils)
## End(Not run)
Lenght for interval
Description
Calculate the large of each interval
Usage
interval.length(x)
Arguments
x |
An interval matrix |
Value
A matrix with the length of each interval.
Examples
## Not run:
data(oils)
interval.length(oils)
## End(Not run)
calcula maximos
Description
calcula maximos
Usage
interval.max(x)
Arguments
x |
tabla simbolica todos intervalos |
calcula minimos
Description
calcula minimos
Usage
interval.min(x)
Arguments
x |
tabla simbolica todos intervalos |
calcula rangos
Description
calcula rangos
Usage
interval.ranges(x)
Arguments
x |
tabla simbolica todos intervalos |
Symbolic histogram
Description
Symbolic histogram
Usage
is.sym.histogram(x)
Arguments
x |
an object to be tested |
Value
returns TRUE if its argument's value is a symbolic_histogram and FALSE otherwise.
Examples
x <- sym.histogram(iris$Sepal.Length)
is.sym.histogram(x)
Symbolic interval
Description
Symbolic interval
Usage
is.sym.interval(x)
Arguments
x |
an object to be tested |
Value
returns TRUE if its argument's value is a symbolic_vector and FALSE otherwise.
Examples
x <- sym.interval(1:10)
is.sym.interval(x)
is.sym.interval("d")
Symbolic modal
Description
Symbolic modal
Usage
is.sym.modal(x)
Arguments
x |
an object to be tested |
Value
returns TRUE if its argument's value is a symbolic_modal and FALSE otherwise.
Examples
x <- sym.modal(factor(c("a", "b", "b", "l")))
is.sym.modal(x)
Symbolic set
Description
Symbolic set
Usage
is.sym.set(x)
Arguments
x |
an object to be tested |
Value
returns TRUE if its argument's value is a symbolic_set and FALSE otherwise.
Examples
x <- sym.set(factor(c("a", "b", "b", "l")))
is.sym.set(x)
limits.histogram.disjoint.pca.variable
Description
limits.histogram.disjoint.pca.variable
Usage
limits.histogram.disjoint.pca.variable(df.histogram, BIN.Matrix)
Arguments
df.histogram |
Bin's Projections onto principal components |
BIN.Matrix |
Number of Bins for each histogram projections |
Value
Histogram Projection onto principal components
Author(s)
Jorge Arce Garro
limits.histogram.pca
Description
limits.histogram.pca
Usage
limits.histogram.pca(sym.hist.matrix, pca.sym.interval)
Arguments
sym.hist.matrix |
A Histogram Matrix |
pca.sym.interval |
PCA result of Interval's PCA |
Value
Bin's Projections onto principal components
Symbolic interval data example.
Description
Symbolic data matrix with all the variables of interval type.
Usage
data(lynne1)
Format
An object of class tbl_df (inherits from tbl, data.frame) with 10 rows and 4 columns.
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Examples
data(lynne1)
lynne1
map function over symbolic table
Description
map function over symbolic table
Usage
map_symbolic_tbl(.x = NULL, .f = NULL, ...)
Plot Interval Scatterplot
Description
Plot Interval Scatterplot
Usage
mcfa.scatterplot(x, y, sym.data, pos.var)
Arguments
x |
symbolic table with only one column. |
y |
symbolic table with only one column. |
sym.data |
original symbolic table. |
pos.var |
column number of the variables to be plotted. |
Examples
data("ex_mcfa1")
sym.table <- classic.to.sym(ex_mcfa1,
concept = suspect,
hair = sym.set(hair),
eyes = sym.set(eyes),
region = sym.set(region)
)
res <- sym.mcfa(sym.table, c(1, 2))
mcfa.scatterplot(res[, 2], res[, 3], sym.data = sym.table, pos.var = c(1, 2))
Symbolic mean for intervals
Description
This function compute the symbolic mean for intervals
Usage
## S3 method for class 'symbolic_interval'
mean(x, method = c("centers", "interval"), trim = 0, na.rm = F, ...)
## S3 method for class 'symbolic_tbl'
mean(x, ...)
Arguments
x |
A symbolic interval. |
method |
The method to be use. |
trim |
As in R mean function. |
na.rm |
As in R mean function. |
... |
As in R mean function. |
Author(s)
Oldemar Rodriguez Rojas
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.
Symbolic Median
Description
This function compute the median for symbolic intervals.
Usage
## S3 method for class 'symbolic_interval'
median(x, na.rm = FALSE, method = c("centers", "interval"), ...)
## S3 method for class 'symbolic_tbl'
median(x, ...)
Arguments
x |
A symbolic interval. |
na.rm |
As in R median function. |
method |
The method to be use. |
... |
As in R median function. |
Author(s)
Oldemar Rodriguez Rojas
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.
Summary method to CM and CRM regression model
Description
Summary method to CM and CRM regression model
Usage
method_summary(ref, pred)
Arguments
ref |
Real values |
pred |
Predicted values |
Maxima and Minima
Description
Maxima and Minima
Usage
## S3 method for class 'symbolic_interval'
min(x, ...)
## S3 method for class 'symbolic_interval'
max(x, ...)
## S3 method for class 'symbolic_interval'
x$name = c("min", "max", "mean", "median")
Arguments
x |
symbolic interval vector |
... |
further arguments passed to or from other methods. |
name |
... |
Value
a new symbolic interval with the minimum of the minima and the minimum of the maxima
Compute neighbors vertex
Description
Compute neighbors vertex
Usage
neighbors.vertex(vertex, Matrix, num.neig)
Arguments
vertex |
Vertes of the hipercube |
Matrix |
Interval Data Matrix. |
num.neig |
Number of vertices. |
Author(s)
Jorge Arce
References
Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.
Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.
Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.
Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.
Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.
See Also
sym.interval.pc
Create an symbolic_histogram type object
Description
Create an symbolic_histogram type object
Usage
new.sym.histogram(x = double(), breaks = NA_real_)
Create an symbolic_interval type object
Description
Create an symbolic_interval type object
Usage
new.sym.intreval(min = numeric(), max = numeric())
Create an symbolic_modal type object
Description
Create an symbolic_modal type object
Usage
new.sym.modal(x = character())
Create an symbolic_set type object
Description
Create an symbolic_set type object
Usage
new.sym.set(x = NA)
newSobject
Description
newSobject
Usage
newSobject(meta.data)
Compute the norm of a vector.
Description
Compute the norm of a vector.
Usage
norm.vect(vector1)
Arguments
vector1 |
An n dimensional vector. |
Value
The L2 norm of the vector.
Author(s)
Jorge Arce
References
Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.
Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.
Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.
Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.
Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.
See Also
sym.interval.pc
Ichino Oils example data.
Description
Symbolic data matrix with all the variables of interval type.
Usage
data(oils)
Format
$I GRA GRA $I FRE FRE $I IOD IOD $I SAP SAP
L $I 0.930 0.935 $I -27 -18 $I 170 204 $I 118 196
P $I 0.930 0.937 $I -5 -4 $I 192 208 $I 188 197
Co $I 0.916 0.918 $I -6 -1 $I 99 113 $I 189 198
S $I 0.920 0.926 $I -6 -4 $I 104 116 $I 187 193
Ca $I 0.916 0.917 $I -25 -15 $I 80 82 $I 189 193
O $I 0.914 0.919 $I 0 6 $I 79 90 $I 187 196
B $I 0.860 0.870 $I 30 38 $I 40 48 $I 190 199
H $I 0.858 0.864 $I 22 32 $I 53 77 $I 190 202
References
Cazes P., Chouakria A., Diday E. et Schektman Y. (1997). Extension de l'analyse en composantes principales a des donnees de type intervalle, Rev. Statistique Appliquee, Vol. XLV Num. 3 pag. 5-24, France.
Examples
data(oils)
oils
optim.desv.fun.interval
Description
optim.desv.fun.interval
Usage
optim.pca.distance.j(sym.data)
Optimized PCA Distance
Description
Optimized PCA Distance
Usage
optim.pca.distance.j.new(sym.data)
Arguments
sym.data |
An Interval Matrix |
Value
Concept Projections onto the principal components, Classical PCA, Variance Best Matrix
optim.pca.variance.j
Description
optim.pca.variance.j
Usage
optim.pca.variance.j(sym.data, num.dimension)
Optimized PCA Variance
Description
Optimized PCA Variance
Usage
optim.pca.variance.j.new(sym.data, num.dimension)
Arguments
sym.data |
An Interval Matrix |
num.dimension |
Number of dimensions |
Value
Concept Projections onto the principal components, Classical PCA, Variance Best Matrix
pca.supplementary.vertex.fun.j
Description
pca.supplementary.vertex.fun.j
Usage
pca.supplementary.vertex.fun.j(
x,
N,
M,
sym.var.names,
sym.data.vertex.matrix,
tot.individuals
)
Calculate the distance
Description
Calculate the distance
Usage
pca.supplementary.vertex.fun.j.new(
x,
N,
M,
sym.var.names,
sym.data.vertex.matrix,
tot.individuals
)
Arguments
x |
A Matrix |
N |
Number of concepts |
M |
Number of variables |
sym.var.names |
Names of concepts |
sym.data.vertex.matrix |
Vertex Matrix |
tot.individuals |
Number of individuals |
Value
Distance
Calculate the variance
Description
Calculate the variance
pca.supplementary.vertex.lambda.fun.j
Usage
pca.supplementary.vertex.lambda.fun.j(
x,
M,
N,
sym.var.names,
sym.data.vertex.matrix,
tot.individuals,
num.dimen.aux
)
pca.supplementary.vertex.lambda.fun.j(
x,
M,
N,
sym.var.names,
sym.data.vertex.matrix,
tot.individuals,
num.dimen.aux
)
Arguments
x |
A Matrix |
M |
Number of variables |
N |
Number of concepts |
sym.var.names |
Names of concepts |
sym.data.vertex.matrix |
Vertex Matrix |
tot.individuals |
Number of individuals |
num.dimen.aux |
Number of dimensions |
Value
Cumulative variance
Plot UMAP for symbolic data tables
Description
Plot UMAP for symbolic data tables
Usage
## S3 method for class 'sym_umap'
plot(x, ...)
Arguments
x |
sym_umap object |
... |
params for plot |
Plot symbolic PCA
Description
Plot symbolic PCA
Usage
## S3 method for class 'symbolic_pca'
plot(x, choix = c("ind", "var"), axes = c(1, 2), labels = TRUE, ...)
Function for plotting a symbolic object
Description
Function for plotting a symbolic object
Usage
## S3 method for class 'symbolic_tbl'
plot(
x,
col = NA,
matrix.form = NA,
border = FALSE,
size = 1,
title = TRUE,
show.type = FALSE,
font.size = 1,
reduce = FALSE,
hist.angle.x = 60,
...
)
Arguments
x |
The symbolic object. |
col |
A specification for the default plotting color. |
matrix.form |
A vector of the form c(num.rows,num.columns). |
border |
A logical value indicating whether border should be plotted. |
size |
The magnification to be used for each graphic. |
title |
A logical value indicating whether title should be plotted. |
show.type |
A logical value indicating whether type should be plotted. |
font.size |
The font size of graphics. |
reduce |
A logical value indicating whether values different from zero should be plotted in modal and set graphics. |
hist.angle.x |
The angle of labels in y axis. Only for histogram plot |
... |
Arguments to be passed to methods. |
Value
A plot of the symbolic data table.
Author(s)
Andres Navarro
Examples
## Not run:
data(oils)
plot(oils)
plot(oils, border = T, size = 1.3)
## End(Not run)
plotX.slice
Description
plotX.slice
Usage
plotX.slice(xx1, yy1, xx2, yy2, vv, vvars, kk)
process.continue.variable
Description
process.continue.variable
Usage
process.continue.variable(
number.of.rows,
parsed.xml,
variable.index,
variable.name
)
process.inter.cont.variable
Description
process.inter.cont.variable
Usage
process.inter.cont.variable(
number.of.rows,
parsed.xml,
variable.index,
variable.name
)
process.mult.nominal.modif.variable
Description
process.mult.nominal.modif.variable
Usage
process.mult.nominal.modif.variable(
labels,
number.of.rows,
parsed.xml,
variable.index,
variable.name
)
process.mult.nominal.variable
Description
process.mult.nominal.variable
Usage
process.mult.nominal.variable(
labels,
number.of.rows,
parsed.xml,
variable.index,
variable.name
)
process.nominal.variable
Description
process.nominal.variable
Usage
process.nominal.variable(
labels,
number.of.rows,
parsed.xml,
variable.index,
variable.name
)
quantiles.RSDA
Description
quantiles.RSDA
Usage
quantiles.RSDA(histogram.matrix, num.quantiles)
Arguments
histogram.matrix |
A matrix of histograms |
num.quantiles |
Number of quantiles |
Value
Quantiles of a Histogram Matrix
Author(s)
Jorge Arce Garro
Examples
## Not run:
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
M<-length(Hardwood.cols)
N<-length(Hardwood.names)
BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
## End(Not run)
quantiles.RSDA.KS
Description
quantiles.RSDA.KS
Usage
quantiles.RSDA.KS(histogram.matrix, num.quantiles)
Arguments
histogram.matrix |
A matrix of histograms |
num.quantiles |
Number of quantiles |
Value
Quantiles of a Histogram Matrix
Author(s)
Jorge Arce Garro
Examples
## Not run:
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
M<-length(Hardwood.cols)
N<-length(Hardwood.names)
BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
quantiles.RSDA.KS<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,100)
## End(Not run)
Read a Symbolic Table
Description
It reads a symbolic data table from a CSV file.
Usage
read.sym.table(file, header = TRUE, sep, dec, row.names = NULL)
Arguments
file |
The name of the CSV file. |
header |
As in R function read.table |
sep |
As in R function read.table |
dec |
As in R function read.table |
row.names |
As in R function read.table |
Details
The labels $C means that follows a continuous variable, $I means an interval variable, $H means a histogram variables and $S means set variable. In the first row each labels should be follow of a name to variable and to the case of histogram a set variables types the names of the modalities (categories) . In data rows for continuous variables we have just one value, for interval variables we have the minimum and the maximum of the interval, for histogram variables we have the number of modalities and then the probability of each modality and for set variables we have the cardinality of the set and next the elements of the set.
The format is the CSV file should be like:
$C F1 $I F2 F2 $H F3 M1 M2 M3 $S F4 E1 E2 E3 E4
Case1 $C 2.8 $I 1 2 $H 3 0.1 0.7 0.2 $S 4 e g k i
Case2 $C 1.4 $I 3 9 $H 3 0.6 0.3 0.1 $S 4 a b c d
Case3 $C 3.2 $I -1 4 $H 3 0.2 0.2 0.6 $S 4 2 1 b c
Case4 $C -2.1 $I 0 2 $H 3 0.9 0.0 0.1 $S 4 3 4 c a
Case5 $C -3.0 $I -4 -2 $H 3 0.6 0.0 0.4 $S 4 e i g k
The internal format is:
$N
[1] 5
$M
[1] 4
$sym.obj.names
[1] 'Case1' 'Case2' 'Case3' 'Case4' 'Case5'
$sym.var.names
[1] 'F1' 'F2' 'F3' 'F4'
$sym.var.types
[1] '$C' '$I' '$H' '$S'
$sym.var.length
[1] 1 2 3 4
$sym.var.starts
[1] 2 4 8 13
$meta
$C F1 $I F2 F2 $H F3 M1 M2 M3 $S F4 E1 E2 E3 E4
Case1 $C 2.8 $I 1 2 $H 3 0.1 0.7 0.2 $S 4 e g k i
Case2 $C 1.4 $I 3 9 $H 3 0.6 0.3 0.1 $S 4 a b c d
Case3 $C 3.2 $I -1 4 $H 3 0.2 0.2 0.6 $S 4 2 1 b c
Case4 $C -2.1 $I 0 2 $H 3 0.9 0.0 0.1 $S 4 3 4 c a
Case5 $C -3.0 $I -4 -2 $H 3 0.6 0.0 0.4 $S 4 e i g k
$data
F1 F2 F2.1 M1 M2 M3 E1 E2 E3 E4
Case1 2.8 1 2 0.1 0.7 0.2 e g k i
Case2 1.4 3 9 0.6 0.3 0.1 a b c d
Case3 3.2 -1 4 0.2 0.2 0.6 2 1 b c
Case4 -2.1 0 2 0.9 0.0 0.1 3 4 c a
Case5 -3.0 -4 -2 0.6 0.0 0.4 e i g k
Value
Return a symbolic data table structure.
Author(s)
Oldemar Rodriguez Rojas
References
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
See Also
display.sym.table
Examples
## Not run:
data(example1)
write.sym.table(example1,
file = "temp4.csv", sep = "|", dec = ".", row.names = TRUE,
col.names = TRUE
)
ex1 <- read.sym.table("temp4.csv", header = TRUE, sep = "|", dec = ".", row.names = 1)
## End(Not run)
Generic function for the standard desviation
Description
Compute the symbolic standard desviation.
Usage
sd(x, ...)
## Default S3 method:
sd(x, na.rm = FALSE, ...)
## S3 method for class 'symbolic_interval'
sd(x, method = c("centers", "interval", "billard"), na.rm = FALSE, ...)
## S3 method for class 'symbolic_tbl'
sd(x, ...)
Arguments
x |
A symbolic variable. |
... |
As in R sd function. |
na.rm |
As in R sd function. |
method |
The method to be use. |
Value
return a real number.
Author(s)
Oldemar Rodriguez Rojas
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.
Standardized Intervals
Description
Standardized Intervals
Usage
stand.data(sym.data, data.mean, data.stan, nn, mm)
Arguments
sym.data |
An Interval Matrix |
data.mean |
A vector of means |
data.stan |
A vector of standard deviation |
nn |
Number of concepts |
mm |
Number of variables |
Value
Standardized intervals
Compute the distance between two rows
Description
Compute the distance between two rows
Usage
sym.Interval.distance(
sym.data,
variable,
w1,
w2,
gamma = 0.5,
method = "Minkowski",
normalize = TRUE
)
sym.all.quantiles.mesh3D.plot
Description
sym.all.quantiles.mesh3D.plot
Usage
sym.all.quantiles.mesh3D.plot(
quantiles.sym,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name
)
Arguments
quantiles.sym |
A quantile matrix |
concept.names |
Concept Names |
var.names |
Variables to plot |
Title |
Plot title |
axes.x.label |
Label of axis X |
axes.y.label |
Label of axis Y |
label.name |
Concept Variable |
Value
3D Mesh Plot
Author(s)
Jorge Arce Garro
Examples
## Not run:
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
M<-length(Hardwood.cols)
N<-length(Hardwood.names)
BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
label.name<-"Hard Wood"
Title<-"First Principal Plane"
axes.x.label<- "First Principal Component (84.83%)"
axes.y.label<- "Second Principal Component (9.70%)"
concept.names<-c("ACER")
var.names<-c("PC.1","PC.2")
concept.names<-row.names(Hardwood.quantiles.PCA)
sym.all.quantiles.mesh3D.plot(Hardwood.quantiles.PCA,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name)
## End(Not run)
sym.all.quantiles.plot
Description
sym.all.quantiles.plot
Usage
sym.all.quantiles.plot(
quantiles.sym,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name
)
Arguments
quantiles.sym |
A quantile matrix |
concept.names |
Concept Names |
var.names |
Variables to plot |
Title |
Plot title |
axes.x.label |
Label of axis X |
axes.y.label |
Label of axis Y |
label.name |
Concept Variable |
Value
3D Scatter Plot
Author(s)
Jorge Arce Garro
Examples
## Not run:
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
M<-length(Hardwood.cols)
N<-length(Hardwood.names)
BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
label.name<-"Hard Wood"
Title<-"First Principal Plane"
axes.x.label<- "First Principal Component (84.83%)"
axes.y.label<- "Second Principal Component (9.70%)"
concept.names<-c("ACER")
var.names<-c("PC.1","PC.2")
concept.names<-row.names(Hardwood.quantiles.PCA)
sym.all.quantiles.plot(Hardwood.quantiles.PCA,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name)
## End(Not run)
Symbolic Circle of Correlations
Description
Plot the symbolic circle of correlations.
Usage
sym.circle.plot(prin.corre)
Arguments
prin.corre |
A symbolic interval data matrix with correlations between the variables and the principals componets, both of interval type. |
Value
Plot the symbolic circle
Author(s)
Oldemar Rodriguez Rojas
References
Rodriguez O. (2012). The Duality Problem in Interval Principal Components Analysis. The 3rd Workshop in Symbolic Data Analysis, Madrid.
Examples
data(oils)
res <- sym.pca(oils, "centers")
sym.circle.plot(res$Sym.Prin.Correlations)
sym.continuos.plot
Description
sym.continuos.plot
Usage
sym.continuos.plot(info, col = c("blue"), border = FALSE, show.type = TRUE)
Distance for Symbolic Interval Variables.
Description
This function computes and returns the distance matrix by using the specified distance measure to compute distance between symbolic interval variables.
Usage
sym.dist.interval(
sym.data,
gamma = 0.5,
method = "Minkowski",
normalize = TRUE,
SpanNormalize = FALSE,
q = 1,
euclidea = TRUE,
pond = rep(1, length(variables))
)
Arguments
sym.data |
A symbolic object |
gamma |
gamma value for the methods ichino and minkowski. |
method |
Method to use (Gowda.Diday, Ichino, Minkowski, Hausdorff) |
normalize |
A logical value indicating whether normalize the data in the ichino or hausdorff method. |
SpanNormalize |
A logical value indicating whether |
q |
q value for the hausdorff method. |
euclidea |
A logical value indicating whether use the euclidean distance. |
pond |
A numeric vector |
variables |
Numeric vector with the number of the variables to use. |
Value
An object of class 'dist'
Generalized Boosted Symbolic Regression
Description
Generalized Boosted Symbolic Regression
Usage
sym.gbm(
formula,
sym.data,
method = c("cm", "crm"),
distribution = "gaussian",
interaction.depth = 1,
n.trees = 500,
shrinkage = 0.1
)
Arguments
formula |
A symbolic description of the model to be fit. The formula may include an offset term (e.g. y~offset(n)+x). If keep.data = FALSE in the initial call to gbm then it is the user's responsibility to resupply the offset to gbm.more. |
sym.data |
symbolic data table |
method |
cm crm |
distribution |
distribution |
interaction.depth |
Integer specifying the maximum depth of each tree (i.e., the highest level of variable interactions allowed). A value of 1 implies an additive model, a value of 2 implies a model with up to 2-way interactions, etc. Default is 1. |
n.trees |
Integer specifying the total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion. Default is 100. |
shrinkage |
A shrinkage parameter applied to each tree in the expansion. Also known as the learning rate or step-size reduction; 0.001 to 0.1 usually work, but a smaller learning rate typically requires more trees. Default is 0.1. |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Lasso, Ridge and and Elastic Net Linear regression model to interval variables
Description
Execute Lasso, Ridge and and Elastic Net Linear regression model to interval variables.
Usage
sym.glm(sym.data, response = 1, method = c('cm', 'crm'),
alpha = 1, nfolds = 10, grouped = TRUE)
Arguments
sym.data |
Should be a symbolic data table read with the function read.sym.table(...). |
response |
The number of the column where is the response variable in the interval data table. |
method |
'cm' to generalized Center Method and 'crm' to generalized Center and Range Method. |
alpha |
alpha=1 is the lasso penalty, and alpha=0 the ridge penalty. 0<alpha<1 is the elastic net method. |
nfolds |
Number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds=3 |
grouped |
This is an experimental argument, with default TRUE, and can be ignored by most users. |
Value
An object of class 'cv.glmnet' is returned, which is a list with the ingredients of the cross-validation fit.
Author(s)
Oldemar Rodriguez Rojas
References
Rodriguez O. (2013). A generalization of Centre and Range method for fitting a linear regression model to symbolic interval data using Ridge Regression, Lasso and Elastic Net methods. The IFCS2013 conference of the International Federation of Classification Societies, Tilburg University Holland.
See Also
sym.lm
sym.hist.plot
Description
sym.hist.plot
Usage
sym.hist.plot(
info,
col = c("blue"),
border = FALSE,
show.type = TRUE,
angle = 60
)
Create an symbolic_histogram type object
Description
Create an symbolic_histogram type object
Usage
sym.histogram(x = double(), breaks = NA_real_)
Arguments
x |
character vector |
breaks |
a vector giving the breakpoints between histogram cells |
Value
a symbolic histogram
Examples
sym.histogram(iris$Sepal.Length)
sym.histogram.pca
Description
sym.histogram.pca
Usage
sym.histogram.pca(sym.hist.matrix, BIN.Matrix, method = NULL)
Arguments
sym.hist.matrix |
A Histogram matrix |
BIN.Matrix |
A matrix with the number of bins for each individual and variable |
method |
Weigthed Method |
Value
Histogram PCA
Author(s)
Jorge Arce Garro
Examples
## Not run:
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
weighted.center<-weighted.center.Hist.RSDA(Hardwood.histogram)
M<-length(Hardwood.cols)
N<-length(Hardwood.names)
BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
pca.hist
## End(Not run)
optim.desv.fun.interval
Description
optim.desv.fun.interval
Usage
sym.histogram.to.latex(datasym.V3.histogram)
Create an symbolic_interval type object
Description
Create an symbolic_interval type object
Usage
sym.interval(x = numeric(), .min = min, .max = max)
Arguments
x |
numeric vector |
.min |
function that will be used to calculate the minimum interval |
.max |
function that will be used to calculate the maximum interval |
Value
a symbolic interval
Examples
sym.interval(c(1, 2, 4, 5))
sym.interval(1:10)
Compute a symbolic interval principal components curves
Description
Compute a symbolic interval principal components curves
Usage
sym.interval.pc(sym.data, method = c('vertex', 'centers'), maxit, plot, scale, center)
Arguments
sym.data |
Shoud be a symbolic data table read with the function read.sym.table(...) |
method |
It should be 'vertex' or 'centers'. |
maxit |
Maximum number of iterations. |
plot |
TRUE to plot immediately, FALSE if you do not want to plot. |
scale |
TRUE to standardize the data. |
center |
TRUE to center the data. |
Value
prin.curve: This a symbolic data table with the interval principal components. As this is a symbolic data table we can apply over this table any other symbolic data analysis method (symbolic propagation).
cor.ps: This is the interval correlations between the original interval variables and the interval principal components, it can be use to plot the symbolic circle of correlations.
Author(s)
Jorge Arce.
References
Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.
Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.
Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.
Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.
Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.
See Also
sym.interval.pca
Examples
## Not run:
data(oils)
res.vertex.ps <- sym.interval.pc(oils, "vertex", 150, FALSE, FALSE, TRUE)
class(res.vertex.ps$sym.prin.curve) <- c("sym.data.table")
sym.scatterplot(res.vertex.ps$sym.prin.curve[, 1], res.vertex.ps$sym.prin.curve[, 2],
labels = TRUE, col = "red", main = "PSC Oils Data"
)
data(facedata)
res.vertex.ps <- sym.interval.pc(facedata, "vertex", 150, FALSE, FALSE, TRUE)
class(res.vertex.ps$sym.prin.curve) <- c("sym.data.table")
sym.scatterplot(res.vertex.ps$sym.prin.curve[, 1], res.vertex.ps$sym.prin.curve[, 2],
labels = TRUE, col = "red", main = "PSC Face Data"
)
## End(Not run)
Symbolic interval principal curves limits
Description
Symbolic interval principal curves limits.
Usage
sym.interval.pc.limits(sym.data, prin.curve, num.vertex, lambda, var.ord)
Arguments
sym.data |
Symbolic interval data table. |
prin.curve |
Principal curves. |
num.vertex |
Number of vertices of the hipercube. |
lambda |
Lambda. |
var.ord |
Order of the variables. |
Author(s)
Jorge Arce.
References
Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.
Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.
Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.
Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.
Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.
See Also
sym.interval.pc
sym.interval.pca.limits.new.j
Description
sym.interval.pca.limits.new.j
Usage
sym.interval.pca.limits.new.j(sym.data, prin.comp, num.vertex)
sym.interval.plot
Description
sym.interval.plot
Usage
sym.interval.plot(info, col = c("blue"), border = FALSE, show.type = TRUE)
sym.interval.vertex.pca.j
Description
sym.interval.vertex.pca.j
Usage
sym.interval.vertex.pca.j(data.sym)
Symbolic k-Means
Description
This is a function is to carry out a k-means overs a interval symbolic data matrix.
Usage
sym.kmeans(sym.data, k = 3, iter.max = 10, nstart = 1,
algorithm = c('Hartigan-Wong', 'Lloyd', 'Forgy', 'MacQueen'))
Arguments
sym.data |
Symbolic data table. |
k |
The number of clusters. |
iter.max |
Maximun number of iterations. |
nstart |
As in R kmeans function. |
algorithm |
The method to be use, as in kmeans R function. |
Value
This function return the following information:
K-means clustering with 3 clusters of sizes 2, 2, 4
Cluster means:
GRA FRE IOD SAP
1 0.93300 -13.500 193.500 174.75
2 0.86300 30.500 54.500 195.25
3 0.91825 -6.375 95.375 191.50
Clustering vector:
L P Co S Ca O B H
1 1 3 3 3 3 2 2
Within cluster sum of squares by cluster:
[1] 876.625 246.125 941.875
(between_SS / total_SS = 92.0
Available components:
[1] 'cluster' 'centers' 'totss' 'withinss' 'tot.withinss' 'betweenss'
[7] 'size'
Author(s)
Oldemar Rodriguez Rojas
References
Carvalho F., Souza R.,Chavent M., and Lechevallier Y. (2006) Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognition Letters Volume 27, Issue 3, February 2006, Pages 167-179
See Also
sym.hclust
Examples
data(oils)
sk <- sym.kmeans(oils, k = 3)
sk$cluster
Symbolic k-Nearest Neighbor Regression
Description
Symbolic k-Nearest Neighbor Regression
Usage
sym.knn(
formula,
sym.data,
method = c("cm", "crm"),
scale = TRUE,
kmax = 20,
kernel = "triangular"
)
Arguments
formula |
a formula object. |
sym.data |
symbolc data.table |
method |
cm or crm |
scale |
logical, scale variable to have equal sd. |
kmax |
maximum number of k, if ks is not specified. |
kernel |
kernel to use. Possible choices are "rectangular" (which is standard unweighted knn), "triangular", "epanechnikov" (or beta(2,2)), "biweight" (or beta(3,3)), "triweight" (or beta(4,4)), "cos", "inv", "gaussian" and "optimal". |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
CM and CRM Linear regression model.
Description
To execute the Center Method (CR) and Center and Range Method (CRM) to Linear regression.
Usage
sym.lm(formula, sym.data, method = c('cm', 'crm'))
Arguments
formula |
An object of class 'formula' (or one that can be coerced to that class): a symbolic description of the model to be fitted. |
sym.data |
Should be a symbolic data table read with the function read.sym.table(...). |
method |
'cm' to Center Method and 'crm' to Center and Range Method. |
Details
Models for lm are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.
Value
sym.lm returns an object of class 'lm' or for multiple responses of class c('mlm', 'lm')
Author(s)
Oldemar Rodriguez Rojas
References
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.
Examples
data(int_prost_train)
data(int_prost_test)
res.cm <- sym.lm(lpsa ~ ., sym.data = int_prost_train, method = "cm")
res.cm
sym.mcfa
Description
This function executes a Multiple Correspondence Factor Analysis for variables of set type.
Usage
sym.mcfa(sym.data, pos.var)
Arguments
sym.data |
A symbolic data table containing at least two set type variables. |
pos.var |
Column numbers in the symbolic data table that contain the set type variables. |
Author(s)
Jorge Arce
References
Arce J. and Rodriguez, O. (2018). Multiple Correspondence Analysis for Symbolic Multi–Valued Variables. On the Symbolic Data Analysis Workshop SDA 2018.
Benzecri, J.P. (1973). L' Analyse des Données. Tomo 2: L'Analyse des Correspondances. Dunod, Paris.
Castillo, W. and Rodriguez O. (1997). Algoritmo e implementacion del analisis factorial de correspondencias. Revista de Matematicas: Teoria y Aplicaciones, 24-31.
Takagi I. and Yadosiha H. (2011). Correspondence Analysis for symbolic contingency tables base on interval algebra. Elsevier Procedia Computer Science, 6, 352-357.
Rodriguez, O. (2007). Correspondence Analysis for Symbolic Multi–Valued Variables. CARME 2007 (Rotterdam, The Netherlands), http://www.carme-n.org/carme2007.
Examples
data("ex_mcfa1")
sym.table <- classic.to.sym(ex_mcfa1,
concept = suspect,
hair = sym.set(hair),
eyes = sym.set(eyes),
region = sym.set(region)
)
sym.table
Create an symbolic_modal type object
Description
Create an symbolic_modal type object
Usage
sym.modal(x = character())
Arguments
x |
character vector |
Value
a symbolic modal
Examples
sym.modal(factor(c("a", "b", "b", "l")))
sym.modal.plot
Description
sym.modal.plot
Usage
sym.modal.plot(
info,
col = c("blue"),
border = FALSE,
show.type = TRUE,
reduce = FALSE
)
Symbolic neural networks regression
Description
Symbolic neural networks regression
Usage
sym.nnet(
formula,
sym.data,
method = c("cm", "crm"),
hidden = c(10),
threshold = 0.05,
stepmax = 1e+05
)
Arguments
formula |
a symbolic description of the model to be fitted. |
sym.data |
symbolic data.table |
method |
cm crm |
|
a vector of integers specifying the number of hidden neurons (vertices) in each layer. | |
threshold |
a numeric value specifying the threshold for the partial derivatives of the error function as stopping criteria. |
stepmax |
the maximum steps for the training of the neural network. Reaching this maximum leads to a stop of the neural network's training process. |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Interval Principal Components Analysis.
Description
Cazes, Chouakria, Diday and Schektman (1997) proposed the Centers and the Tops Methods to extend the well known principal components analysis method to a particular kind of symbolic objects characterized by multi–values variables of interval type.
Usage
sym.pca(sym.data, ...)
## S3 method for class 'symbolic_tbl'
sym.pca(
sym.data,
method = c("classic", "tops", "centers", "principal.curves", "optimized.distance",
"optimized.variance", "fixed"),
fixed.matrix = NULL,
...
)
Arguments
sym.data |
Shoud be a symbolic data table |
... |
further arguments passed to or from other methods. |
method |
It is use so select the method, 'classic' execute a classical principal component analysis over the centers of the intervals, 'tops' to use the vertices algorithm and 'centers' to use the centers algorithm. |
fixed.matrix |
Classic Matrix. It is use when the method chosen is "fixed". |
Value
Sym.Components: This a symbolic data table with the interval principal components. As this is a symbolic data table we can apply over this table any other symbolic data analysis method (symbolic propagation).
Sym.Prin.Correlations: This is the interval correlations between the original interval variables and the interval principal components, it can be use to plot the symbolic circle of correlations.
Author(s)
Oldemar Rodriguez Rojas
References
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
Cazes P., Chouakria A., Diday E. et Schektman Y. (1997). Extension de l'analyse en composantes principales a des donnees de type intervalle, Rev. Statistique Appliquee, Vol. XLV Num. 3 pag. 5-24, France.
Chouakria A. (1998) Extension des methodes d'analysis factorialle a des donnees de type intervalle, Ph.D. Thesis, Paris IX Dauphine University.
Makosso-Kallyth S. and Diday E. (2012). Adaptation of interval PCA to symbolic histogram variables, Advances in Data Analysis and Classification July, Volume 6, Issue 2, pp 147-159.
Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.
See Also
sym.histogram.pca
Examples
## Not run:
data(oils)
res <- sym.pca(oils, "centers")
sym.scatterplot(res$Sym.Components[, 1], res$Sym.Components[, 1],
labels = TRUE, col = "red", main = "PCA Oils Data"
)
sym.scatterplot3d(res$Sym.Components[, 1], res$Sym.Components[, 2],
res$Sym.Components[, 3],
color = "blue", main = "PCA Oils Data"
)
sym.scatterplot.ggplot(res$Sym.Components[, 1], res$Sym.Components[, 2],
labels = TRUE
)
sym.circle.plot(res$Sym.Prin.Correlations)
res <- sym.pca(oils, "classic")
plot(res, choix = "ind")
plot(res, choix = "var")
data(lynne2)
res <- sym.pca(lynne2, "centers")
sym.scatterplot(res$Sym.Components[, 1], res$Sym.Components[, 2],
labels = TRUE, col = "red", main = "PCA Lynne Data"
)
sym.scatterplot3d(res$Sym.Components[, 1], res$Sym.Components[, 2],
res$Sym.Components[, 3],
color = "blue", main = "PCA Lynne Data"
)
sym.scatterplot.ggplot(res$Sym.Components[, 1], res$Sym.Components[, 2],
labels = TRUE
)
sym.circle.plot(res$Sym.Prin.Correlations)
data(StudentsGrades)
st <- StudentsGrades
s.pca <- sym.pca(st)
plot(s.pca, choix = "ind")
plot(s.pca, choix = "var")
## End(Not run)
Predict method to CM and CRM regression model
Description
To execute predict method the Center Method (CR) and Center and Range Method (CRM) to Linear regression.
Usage
sym.predict(model, ...)
## S3 method for class 'symbolic_lm_cm'
sym.predict(model, new.sym.data, ...)
## S3 method for class 'symbolic_lm_crm'
sym.predict(model, new.sym.data, ...)
## S3 method for class 'symbolic_glm_cm'
sym.predict(model, new.sym.data, response, ...)
## S3 method for class 'symbolic_glm_crm'
sym.predict(model, new.sym.data, response, ...)
Arguments
model |
The output of lm method. |
... |
additional arguments affecting the predictions produced. |
new.sym.data |
Should be a symbolic data table read with the function read.sym.table(...). |
response |
The number of the column where is the response variable in the interval data table. |
Value
sym.predict produces a vector of predictions or a matrix of predictions and bounds with column names fit, lwr, and upr if interval is set. For type = 'terms' this is a matrix with a column per term and may have an attribute 'constant'
Author(s)
Oldemar Rodriguez Rojas
References
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.
LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.
See Also
sym.glm
Examples
data(int_prost_train)
data(int_prost_test)
model <- sym.lm(lpsa ~ ., sym.data = int_prost_train, method = "cm")
pred.cm <- sym.predict(model, int_prost_test)
pred.cm
Predict model_gbm_cm model
Description
Predict model_gbm_cm model
Usage
## S3 method for class 'symbolic_gbm_cm'
sym.predict(model, new.sym.data, n.trees = 500, ...)
Arguments
model |
model |
new.sym.data |
new data |
n.trees |
Integer specifying the total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion. Default is 100. |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict model_gbm_crm model
Description
Predict model_gbm_crm model
Usage
## S3 method for class 'symbolic_gbm_crm'
sym.predict(model, new.sym.data, n.trees = 500, ...)
Arguments
model |
model |
new.sym.data |
new data |
n.trees |
Integer specifying the total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion. Default is 100. |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict model_knn_cm model
Description
Predict model_knn_cm model
Usage
## S3 method for class 'symbolic_knn_cm'
sym.predict(model, new.sym.data, ...)
Arguments
model |
model |
new.sym.data |
new data |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict model_knn_crm model
Description
Predict model_knn_crm model
Usage
## S3 method for class 'symbolic_knn_crm'
sym.predict(model, new.sym.data, ...)
Arguments
model |
model |
new.sym.data |
new data |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict nnet_cm model
Description
Predict nnet_cm model
Usage
## S3 method for class 'symbolic_nnet_cm'
sym.predict(model, new.sym.data, ...)
Arguments
model |
model |
new.sym.data |
new data |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict nnet_crm model
Description
Predict nnet_crm model
Usage
## S3 method for class 'symbolic_nnet_crm'
sym.predict(model, new.sym.data, ...)
Arguments
model |
model |
new.sym.data |
new data |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict rf_cm model
Description
Predict rf_cm model
Usage
## S3 method for class 'symbolic_rf_cm'
sym.predict(model, new.sym.data, ...)
Arguments
model |
model |
new.sym.data |
new data |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict rf_crm model
Description
Predict rf_crm model
Usage
## S3 method for class 'symbolic_rf_crm'
sym.predict(model, new.sym.data, ...)
Arguments
model |
model |
new.sym.data |
new data |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict rt_cm model
Description
Predict rt_cm model
Usage
## S3 method for class 'symbolic_rt_cm'
sym.predict(model, new.sym.data, ...)
Arguments
model |
a model_rt_crm object |
new.sym.data |
new data |
... |
arguments to predict.rpart |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict rt_crm model
Description
Predict rt_crm model
Usage
## S3 method for class 'symbolic_rt_crm'
sym.predict(model, new.sym.data, ...)
Arguments
model |
a model_rt_crm object |
new.sym.data |
new data |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict model_svm_cm model
Description
Predict model_svm_cm model
Usage
## S3 method for class 'symbolic_svm_cm'
sym.predict(model, new.sym.data, ...)
Arguments
model |
model |
new.sym.data |
new data |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Predict model_svm_crm model
Description
Predict model_svm_crm model
Usage
## S3 method for class 'symbolic_svm_crm'
sym.predict(model, new.sym.data, ...)
Arguments
model |
model |
new.sym.data |
new data |
... |
optional parameters |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
sym.quantiles.PCA.plot
Description
sym.quantiles.PCA.plot
Usage
sym.quantiles.PCA.plot(
histogram.PCA.r,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name
)
Arguments
histogram.PCA.r |
A quantil matrix |
concept.names |
Concept Name |
var.names |
Variables to plot |
Title |
Plot title |
axes.x.label |
Label of axis X |
axes.y.label |
Label of axis Y |
label.name |
Concept Variable |
Value
3D plot
Author(s)
Jorge Arce Garro
Examples
## Not run:
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
M<-length(Hardwood.cols)
N<-length(Hardwood.names)
BIN.Matrix<-matrix(rep(3,N*M),nrow = N)
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
label.name<-"Hard Wood"
Title<-"First Principal Plane"
axes.x.label<- "PC 1 (84.83%)"
axes.y.label<- "PC 2 (9.70%)"
concept.names<-c("ACER")
var.names<-c("PC.1","PC.2")
plot.3D.HW<-sym.quantiles.PCA.plot(Hardwood.quantiles.PCA,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name)
plot.3D.HW
## End(Not run)
Internal sym.radar.data
Description
Internal sym.radar.data
Usage
sym.radar.data(dat, nom.vars, use.pct = F)
Arguments
dat |
The symbolic data. |
nom.vars |
Names of the variables. |
use.pct |
a logical value indicating if use percentage or real distance for plot. |
Value
A molten data.
Internal sym.radar.plot the distence between two rows
Description
Internal sym.radar.plot the distence between two rows
Usage
sym.radar.plot(
dat,
indivs,
vars,
rad.main = "",
rad.legend = "Individuals",
use.pct = F
)
Arguments
dat |
The symbolic data. |
indivs |
an array that indicates which individuals to use (optional). |
vars |
an array that indicates which variables to use (optional). |
rad.main |
the title of the final plot (optional). |
use.pct |
a logical value indicating if use percentage or real distance for plot. |
Value
A radar plot.
Symbolic Regression with Random Forest
Description
Symbolic Regression with Random Forest
Usage
sym.rf(formula, sym.data, method = c("cm", "crm"), ntree = 500)
Arguments
formula |
a formula, with a response but no interaction terms. If this a a data frame, that is taken as the model frame (see model.frame). |
sym.data |
symbolic data table |
method |
cm crm |
ntree |
Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times. |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
Symbolic Regression Trees
Description
Symbolic Regression Trees
Usage
sym.rt(
formula,
sym.data,
method = c("cm", "crm"),
minsplit = 20,
maxdepth = 10
)
Arguments
formula |
a formula, with a response but no interaction terms. If this a a data frame, that is taken as the model frame (see model.frame). |
sym.data |
a symbolic data table |
method |
cm crm |
minsplit |
the minimum number of observations that must exist in a node in order for a split to be attempted. |
maxdepth |
Set the maximum depth of any node of the final tree, with the root node counted as depth 0. Values greater than 30 rpart will give nonsense results on 32-bit machines. |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
sym.scale.interval
Description
sym.scale.interval
Usage
sym.scale.interval(sym.data, mean.var, desv.var)
sym.scale.matrix.j
Description
sym.scale.matrix.j
Usage
sym.scale.matrix.j(data, data.mean, data.sd, nn, mm)
Arguments
data |
A symbolic data |
data.mean |
A vector of means |
data.sd |
A vector of deviation standard |
nn |
Number of concepts |
mm |
Number of variables |
Value
Standardized Data
Symbolic Scatter Plot
Description
This function could be use to plot two symbolic variables in a X-Y plane.
Usage
sym.scatterplot(sym.var.x, sym.var.y, labels = FALSE, ...)
Arguments
sym.var.x |
First symbolic variable |
sym.var.y |
Second symbolic variable. |
labels |
As in R plot function. |
... |
As in R plot function. |
Value
Return a graphics.
Author(s)
Oldemar Rodriguez Rojas
References
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.
See Also
sym.scatterplot3d
Examples
## Not run:
data(example3)
sym.data <- example3
sym.scatterplot(sym.data[, 3], sym.data[, 7], col = "blue", main = "Main Title")
sym.scatterplot(sym.data[, 1], sym.data[, 4],
labels = TRUE, col = "blue",
main = "Main Title"
)
sym.scatterplot(sym.data[, 2], sym.data[, 6],
labels = TRUE,
col = "red", main = "Main Title", lwd = 3
)
data(oils)
sym.scatterplot(oils[, 2], oils[, 3],
labels = TRUE,
col = "red", main = "Oils Data"
)
data(lynne1)
sym.scatterplot(lynne1[, 2], lynne1[, 1],
labels = TRUE,
col = "red", main = "Lynne Data"
)
## End(Not run)
Create an symbolic_set type object
Description
Create an symbolic_set type object
Usage
sym.set(x = NA)
Arguments
x |
character vector |
Value
a symbolic set
Examples
sym.set(factor(c("a", "b", "b", "l")))
sym.set.plot
Description
sym.set.plot
Usage
sym.set.plot(
info,
col = c("blue"),
border = FALSE,
show.type = TRUE,
reduce = FALSE
)
Symbolic Support Vector Machines Regression
Description
Symbolic Support Vector Machines Regression
Usage
sym.svm(
formula,
sym.data,
method = c("cm", "crm"),
scale = TRUE,
kernel = "radial"
)
Arguments
formula |
a symbolic description of the model to be fit. |
sym.data |
symbolic data.table |
method |
method |
scale |
A logical vector indicating the variables to be scaled. If scale is of length 1, the value is recycled as many times as needed. Per default, data are scaled internally (both x and y variables) to zero mean and unit variance. The center and scale values are returned and used for later predictions. |
kernel |
the kernel used in training and predicting. You might consider changing some of the following parameters, depending on the kernel type. |
References
Lima-Neto, E.A., De Carvalho, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis52, 1500-1515
Lima-Neto, E.A., De Carvalho, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347
Lima Neto, E.d.A., de Carvalho, F.d.A.T. Nonlinear regression applied to interval-valued data. Pattern Anal Applic 20, 809–824 (2017). https://doi.org/10.1007/s10044-016-0538-y
Rodriguez, O. (2018). Shrinkage linear regression for symbolic interval-valued variables.Journal MODULAD 2018, vol. Modulad 45, pp.19-38
UMAP for Symbolic Data
Description
This function applies the UMAP algorithm to a symbolic data table.
Usage
sym.umap(sym.data, ...)
## S3 method for class 'symbolic_tbl'
sym.umap(
sym.data = NULL,
config = umap::umap.defaults,
method = c("naive", "umap-learn"),
preserve.seed = TRUE,
...
)
Arguments
sym.data |
symbolic data table |
... |
list of settings; values overwrite defaults from config; see documentation of umap.default for details about available settings |
config |
object of class umap.config |
method |
character, implementation. Available methods are 'naive' (an implementation written in pure R) and 'umap-learn' (requires python package 'umap-learn') |
preserve.seed |
logical, leave TRUE to insulate external code from randomness within the umap algorithms; set FALSE to allow randomness used in umap algorithms to alter the external random-number generator |
Symbolic Variable
Description
This function get a symbolic variable from a symbolic data table.
Usage
sym.var(sym.data, number.sym.var)
Arguments
sym.data |
The symbolic data table |
number.sym.var |
The number of the column for the variable (feature) that we want to get. |
Value
Return a symbolic data variable with the following structure:
$N
[1] 7
$var.name
[1] 'F6'
$var.type
[1] '$I'
$obj.names
[1] 'Case1' 'Case2' 'Case3' 'Case4' 'Case5' 'Case6' 'Case7'
$var.data.vector
F6 F6.1
Case1 0.00 90.00
Case2 -90.00 98.00
Case3 65.00 90.00
Case4 45.00 89.00
Case5 20.00 40.00
Case6 5.00 8.00
Case7 3.14 6.76
Author(s)
Oldemar Rodriguez Rojas
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
See Also
sym.obj
tbl_subset_col
Description
tbl_subset_col
Usage
tbl_subset_col(x, j, j_arg)
to.v2
Description
to.v2
Usage
to.v2(x)
to.v3
Description
to.v3
Usage
to.v3(x)
Us crime interval data table.
Description
Us crime classic data table genetated from uscrime data.
Usage
data(uscrime_int)
Format
An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 46 rows and 102 columns.
References
Rodriguez O. (2013). A generalization of Centre and Range method for fitting a linear regression model to symbolic interval data using Ridge Regression, Lasso and Elastic Net methods. The IFCS2013 conference of the International Federation of Classification Societies, Tilburg University Holland.
Examples
data(uscrime_int)
car.data <- uscrime_int
res.cm.lasso <- sym.glm(
sym.data = car.data, response = 102, method = "cm", alpha = 1,
nfolds = 10, grouped = TRUE
)
plot(res.cm.lasso)
plot(res.cm.lasso$glmnet.fit, "norm", label = TRUE)
plot(res.cm.lasso$glmnet.fit, "lambda", label = TRUE)
pred.cm.lasso <- sym.predict(res.cm.lasso, response = 102, car.data)
RMSE.L(car.data$ViolentCrimesPerPop, pred.cm.lasso)
RMSE.U(car.data$ViolentCrimesPerPop, pred.cm.lasso)
R2.L(car.data$ViolentCrimesPerPop, pred.cm.lasso)
R2.U(car.data$ViolentCrimesPerPop, pred.cm.lasso)
deter.coefficient(car.data$ViolentCrimesPerPop, pred.cm.lasso)
Us crime interval data table.
Description
Us crime classic data table genetated from uscrime data.
Usage
data(uscrime_int)
Format
An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 46 rows and 102 columns.
References
Rodriguez O. (2013). A generalization of Centre and Range method for fitting a linear regression model to symbolic interval data using Ridge Regression, Lasso and Elastic Net methods. The IFCS2013 conference of the International Federation of Classification Societies, Tilburg University Holland.
Symbolic Variance
Description
Compute the symbolic variance.
Usage
var(x, ...)
## Default S3 method:
var(x, y = NULL, na.rm = FALSE, use, ...)
## S3 method for class 'symbolic_interval'
var(x, method = c("centers", "interval", "billard"), na.rm = FALSE, ...)
## S3 method for class 'symbolic_tbl'
var(x, ...)
Arguments
x |
A symbolic interval. |
... |
As in R median function. |
y |
NULL (default) or a vector, matrix or data frame with compatible dimensions to x. The default is equivalent to y = x (but more efficient). |
na.rm |
logical. Should missing values be removed? |
use |
an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings 'everything', 'all.obs', 'complete.obs', 'na.or.complete', or 'pairwise.complete.obs'. |
method |
The method to be use. |
Author(s)
Oldemar Rodriguez Rojas
References
Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.
Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.
Extract length
Description
Extract length
Usage
## S3 method for class 'length'
var(x, ...)
Variance of the principal curve
Description
Variance of the principal curve
Usage
variance.princ.curve(data,curve)
Arguments
data |
Classic data table. |
curve |
The principal curve. |
Value
The variance of the principal curve.
Author(s)
Jorge Arce.
References
Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.
Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.
Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.
Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.
Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.
See Also
sym.interval.pc
abbr for symbolic modal
Description
abbr for symbolic modal
Usage
vec_ptype_abbr.symbolic_histogram(x)
abbr for symbolic interval
Description
abbr for symbolic interval
Usage
vec_ptype_abbr.symbolic_interval(x)
abbr for symbolic modal
Description
abbr for symbolic modal
Usage
vec_ptype_abbr.symbolic_modal(x)
abbr for symbolic set
Description
abbr for symbolic set
Usage
vec_ptype_abbr.symbolic_set(x)
full name for symbolic modal
Description
full name for symbolic modal
Usage
vec_ptype_full.symbolic_histogram(x)
full name for symbolic interval
Description
full name for symbolic interval
Usage
vec_ptype_full.symbolic_interval(x)
full name for symbolic set
Description
full name for symbolic set
Usage
vec_ptype_full.symbolic_modal(x)
full name for symbolic set
Description
full name for symbolic set
Usage
vec_ptype_full.symbolic_set(x)
Vertex of the intervals
Description
Vertex of the intervals
Usage
vertex.interval(sym.data)
Arguments
sym.data |
Symbolic interval data table. |
Value
Vertices of the intervals.
Author(s)
Jorge Arce.
References
Arce J. and Rodriguez O. (2015) 'Principal Curves and Surfaces to Interval Valued Variables'. The 5th Workshop on Symbolic Data Analysis, SDA2015, Orleans, France, November.
Hastie,T. (1984). Principal Curves and Surface. Ph.D Thesis Stanford University.
Hastie,T. & Weingessel,A. (2014). princurve - Fits a Principal Curve in Arbitrary Dimension.R package version 1.1–12 http://cran.r-project.org/web/packages/princurve/index.html.
Hastie,T. & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association, Vol. 84-406, 502–516.
Hastie, T., Tibshirani, R. & Friedman, J. (2008). The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, New York.
See Also
sym.interval.pc
vertex.interval.new.j
Description
vertex.interval.new.j
Usage
vertex.interval.new.j(sym.data)
vertex.pca.j
Description
vertex.pca.j
Usage
vertex.pca.j(data.sym)
weighted.center.Hist.RSDA
Description
weighted.center.Hist.RSDA
Usage
weighted.center.Hist.RSDA(sym.histogram)
Arguments
sym.histogram |
A Histogram matrix |
Value
Matrix of Weighted Centers
Author(s)
Jorge Arce Garro
Examples
## Not run:
data(hardwoodBrito)
weighted.center.Hist.RSDA(hardwoodBrito)
## End(Not run)
Write Symbolic Data Table
Description
This function write (save) a symbolic data table from a CSV data file.
Usage
write.sym.table(sym.data, file, sep, dec, row.names = NULL, col.names = NULL)
Arguments
sym.data |
Symbolic data table |
file |
The name of the CSV file. |
sep |
As in R function read.table |
dec |
As in R function read.table |
row.names |
As in R function read.table |
col.names |
As in R function read.table |
Value
Write in CSV file the symbolic data table.
Author(s)
Oldemar Rodriguez Rojas
References
Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.
See Also
read.sym.table
Examples
## Not run:
data(example1)
write.sym.table(example1, file = "temp4.csv", sep = "|",
dec = ".", row.names = TRUE, col.names = TRUE)
ex1 <- read.sym.table("temp4.csv", header = TRUE,
sep = "|", dec = ".", row.names = 1)
## End(Not run)