American Business Cycle

Description

The 22 variables are quarterly data from 1919-1941 and 1947-1983 of the variables GNP72, CPRATE, CORPYIELD, M1, M2, BASE, CSTOCK, WRICE67, PRODUR72, NONRES72, IRES72, DBUSI72, CDUR72, CNDUR72, XPT72, MPT72, GOVPUR72,NCSPDE72, NCSBS72, NCSCON72, CCSPDE72 and CCSBS72.

Usage

abcq

Format

A matrix of size 240 x 22

Source

http://data.nber.org/data/abc/

Boston data

Description

This data set is part of the MASS package. The 14 columns are:

crim per capita crime rate by town

zn proportion of residential land zoned for lots over 25.000 sq.ft.

indus proportion of non-residential business acres per town

chas Charles River dummy variable (=1 if tract bounds rive; 0 otherwise)

nox nitrogen oxides concentration (parts per 10 million)

rm average number of rooms per dwelling

age proportion of owner-occupied units built prior to 1940

dis weighted mean of distances to five Boston employment centres

rad index of accessibility to radial highways

tax full-value property-tax rate per $10,000

ptration pupil-teacher ration by town

black 100(Bk-0.63)^2 where Bk is the proportion of blacks by town

lstat lower status of the population (percent)

medv median value of owner occupies homes in $1000s.

Usage

boston

Format

A 506 x 14 matrix.

Source

R package MASS https://cran.r-project.org/web/packages/available_packages_by_name.html

References

MASS Support Functions and Datasets for Venables and Ripley's MASS

Decodes the number of a subset selected by fasb.R to give the covariates

Description

Decodes the number of a subset selected by fasb.R to give the covariates

Usage

decode(ns, k)

Arguments

Value

ind The list of covariates

set A binary vector giving the covariates

Examples

a<- decode(19,8)

Stepwise selection of interval covariates in non-paametric regression

Description

Stepwise selection of interval covariates in non-paametric regression

Usage

f1bsf(y,k,lam,pr=0.5,mm=20,p0=0.01)

Arguments

Value

pv ff The approximation

res The residuals

Examples

data(vardata)
a<-f1bsf(vardata[,70],4,1.1,pr=0,mm=10)

Stepwise selection of covariates

Description

Stepwise selection of covariates

Usage

f1st(y,x,p0=0.01,kmn=0,kmx=0,kex=0,mx=21,sub=T,inr=T,xinr=F,qq=-1)

Arguments

Value

pv In order the included covariates, the regression coefficient values, the Gaussian P-values, the standard P-values.

res The residuals

Examples

data(boston)
bostint<-fgeninter(boston[,1:13],2)[[1]]
a<-f1st(boston[,14],bostint,kmn=10,sub=TRUE)

Repeated stepwise selection of covariates

Description

Repeated stepwise selection of covariates

Usage

f2st(y,x,p0=0.01,kmn=0,kmx=0,kex=0,mx=21,lm=9^9,sub=T,inr=T,xinr=F,qq=-1)

Arguments

Value

pv In order the linear approximation, the included covariates, the Gaussian P-values.

Examples

data(boston)
bostint<-fgeninter(boston[,1:13],2)[[1]]
a<-f2st(boston[,14],bostint,lm=3,sub=FALSE)

Stepwise selection of covariates

Description

Stepwise selection of covariates

Usage

f3st(y,x,m,p0=0.01,kmn=0,kmx=0,kex=0,mx=21,sub=T,inr=T,xinr=F,qq=-1,kexmx=100)

Arguments

Value

covch The sum of squared residuals and the selected covariates ordered in increasing size of sum of squared residuals.

lai The number of rows of covch

Examples

data(leukemia)
a<-f3st(leukemia[[1]],leukemia[[2]],m=2,kmn=5,sub=TRUE,kexmx=10)

Selection of covariates with given excluded covariates

Description

Selection of covariates with given excluded covariates

Usage

f3sti(y,x,covch,ind,m,p0=0.01,kmn=0,kmx=0, kex=0,mx=21,sub=T,inr=F,xinr=F,qq=-1,kexmx=100)

Arguments

Value

ind1 The excluded covariates

covch The sum of squared residuals and the selected covariates ordered in increasing size of sum of squared residuals

Examples

data(leukemia)
covch=c(2.023725,1182,1219,2888,0)
covch<-matrix(covch,nrow=1,ncol=5)
ind<-c(1182,1219,2888)
ind<-matrix(ind,nrow=3,ncol=1)
m<-1
a<-f3sti(leukemia[[1]],leukemia[[2]],covch,ind,m,kexmx=5)

Calculates all subsets where each included covariate is significant.

Description

The subset are ordered according to the sum of squared residuals. Subsets can be decoded with decode.R.

Usage

fasb(y,x,p0=0.01,ind=0,inr=T,xinr=F,qq=-1)

Arguments

Value

nv Coded List of subsets with number of covariates and sum of squared residuals

Examples

data(redwine)
nvv<-fasb(redwine[,12],redwine[,1:11])

Decodes the number of a subset selected by fasb.R to give the covariates

Description

Decodes the number of a subset selected by fasb.R to give the covariates

Usage

fdecode(ns, k)

Arguments

Value

ind The list of covariates

set A binary vector giving the covariates

Examples

a<- fdecode(19,8)

Generates basis functions on disjoint intervals

Description

Generation of basic functions on intervals

Usage

fgenbsf(n,k,lam)

Arguments

Value

x the matrix of the intervals

Examples

a<-fgenbsf(100,4,1.2)

Generation of interactions

Description

Generates all interactions of degree at most ord

Usage

fgeninter(x,ord)

Arguments

Value

xx All interactions of order at most ord.

intx Decomposes a given interaction covariate of xx

Examples

data(boston)
bostint<-fgeninter(boston[,1:13],2)[[1]]

Generation of sine and cosine functions

Description

Generates sin(pi*j*(1:n)/n) (odd) and cos(pi*j*(1:n)/n) (even) for j=1,...,m for a given sample size n.

Usage

fgentrig(n,m)

Arguments

Value

x The functions sin(pi*j*(1:n)/n) (odd) and cos(pi*j*(1:n)/n) (even) for j=1,...,m.

Examples

trig<-fgentrig(36,36)

Calculates a dependence graph using Gaussian stepwise selection

Description

Calculates an independence graph using Gaussian stepwise selection

Usage

fgr1st(x,p0=0.01,ind=0,kmn=0,kmx=0,mx=21,nedge=10^5,inr=T,xinr=F,qq=-1)

Arguments

Value

ned Number of edges

edg List of edges together with P-values for each edge and proportional reduction of sum of squared residuals.

Examples

data(boston)
a<-fgr1st(boston[,1:13],ind=3:6)

Calculation of lagged covariates

Description

Calculation of lagged covariates

Usage

flag(x,n,i,lag)

Arguments

Value

y The ith covariate of x without a lag, the dependent variable.

xl The covariates with lags from 1 :lag starting with the first covariate.

Examples

data(abcq)
abcql<-flag(abcq,240,1,16)
a<-f1st(abcql[[1]],abcql[[2]])

Calculates the regression coefficients, the P-values and the standard P-values for the chosen subset ind

Description

Calculates the regression coefficients, the P-values and the standard P-values for the chosen subset ind.

Usage

fpval(y,x,ind,inr=T,xinr=F,qq=-1)

Arguments

Value

apv In order the subset ind, the regression coefficients, the Gaussian P-values, the standard P-values and the proportion of sum of squares explained.

res The residuals

Examples

data(boston)
a<-fpval(boston[,14],boston[,1:13],c(1,2,4:6,8:13))

Selects the subsets specified by fasb.R and frasb.R.

Description

All subsets which are a subset of a specified subset are removed. The remaining subsets are ordered by the sum of squares of the residuals (fasb.R) or the scale (frasb.R)

Usage

fselect(nv, k)

Arguments

Value

ind The selected subsets.

Examples

b<-fasb(redwine[,12],redwine[,1:5 ])[[1]]
a<-fselect(b,11)[[1]]
b[a,]

Converts directed into an undirected graph

Description

Conversion of a directed graph into an undirected graph

Usage

fundr(gr)

Arguments

Value

gr The undirected graph

Examples

data(boston)
grb<-fgr1st(boston[,1:13])
grbu<-fundr(grb[[2]][,1:2])

Leukemia data set

Description

Dataset of n=72 persons indicating presence or absence of leukemia (variable 3572) and q=3571 gene expressions of the 72 persons (variables 1 to 3571)

Usage

data(leukemia)

Format

y

0-1 data of individuals with and without leukemia.

x

covariates of the level of 3571 genes.

Source

http://stat.ethz.ch/~dettling/bagboost.html

References

Boosting for tumor classification with gene expression data. Dettling, M. and Buehlmann, P. Bioinformatics, 2003,19(9):1061–1069.

Lymphoma data set

Description

Dataset of n=62 persons with one of three different forms of lymphoma and q=4026 gene expressions of the 62 persons

Usage

data(leukemia)

Format

y

0-1-2 data of individuals with lymphoma.

x

covariates of the level of 4062 genes.

Source

R Package ‘spls'

References

Sparse partial least squares classification for high dimensional data. Dongjun Chung and Sunduz Keles. Stat. Appl. Genet. Mol. Biol., (2010;9)

m15005m data

Description

Angle and photon counts in thin film x-ray refraction.

Usage

m15005m

Format

A matrix of size 7001 x 2, first component angle, second photon count

Source

The data were provided by Professor Dieter Mergel, Faculty of Physics, University of Duisburg-Essen, Essen, Germany

Melbourne minimum temperature

Description

The daily minimum temperature in Melbourne for the years 1981-1990.

Usage

mel_temp

Format

A vector of length 3650

Source

https://www.kaggle.com/paulbrabban/daily-minimum-temperatures-in-melbourne

Redwine data

Description

The subjective quality of wine on an integer scale from 1-10 (variable 12) together with 11 physicochemical properties

Usage

redwine

Format

A matrix of size 1599 x 12

Source

https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/

References

Modeling wine preferences by data mining from physicochemical properties, Cortez, P., Cerdeira, A., Almeida, F., Matos, T., and Reis, J., Decision Support Systems, Elsevier, 2009,47(4):547–553.

Sunspot data

Description

The average number of sunspots each month from January 1749 to January 2020

Usage

snspt

Format

A vector of size 3253

Source

WDC-SILSO, Royal Observatory of Belgium, Brussels

USA economics data

Description

United States economic data taken from the FRED-MD macroeconomic database with the NAs removed.182 indices each of length 256

Usage

vardata

Format

A matrix of size 256 X 182

Source

https://research.stlouisfed.org/econ/mccracken/fred-databases