| Type: | Package |
| Title: | Tune Random Forests Based on Variable Importance & Plot Results |
| Version: | 0.1.3 |
| Description: | Functions for assessing variable relations and associations prior to modeling with a Random Forest algorithm (although these are relevant for any predictive model). Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) https://digitalcommons.usu.edu/etd/8587/. |
| License: | GPL-3 |
| URL: | https://github.com/KelvynBladen/randomForestVIP |
| Depends: | R (≥ 4.0.0) |
| Imports: | car, dplyr, ggplot2, gridExtra, minerva, randomForest, stats, tidyr |
| Suggests: | EZtune, e1071, knitr, MASS, rmarkdown, rpart, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2023-07-18 16:31:50 UTC; kelvy |
| Author: | Kelvyn Bladen [aut, cre], D. Richard Cutler [aut] |
| Maintainer: | Kelvyn Bladen <kelvyn.bladen@usu.edu> |
| Repository: | CRAN |
| Date/Publication: | 2023-07-19 11:20:02 UTC |
Housing Values in Suburbs of Boston
Description
The Boston data frame has 506 rows and 14 columns.
Usage
boston
Format
This data frame contains the following columns:
- crim
per capita crime rate by town.
- zn
proportion of residential land zoned for lots over 25,000 sq.ft.
- indus
proportion of non-retail business acres per town.
- chas
Charles River dummy variable (= 1 if tract bounds river; 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.
- ptratio
pupil-teacher ratio by town.
- black
1000(Bk-0.63)^2whereBkis the proportion of blacks by town.- lstat
lower status of the population (percent).
- medv
median value of owner-occupied homes in $1000s.
Source
https://www.stats.ox.ac.uk/pub/MASS4/
Variable Importance GGPlot
Description
A ggplot of variable importance as measured by a Random Forest.
Usage
ggvip(x, scale = FALSE, sqrt = TRUE, type = "both", num_var)
Arguments
x |
An object of class randomForest. |
scale |
For permutation based measures such as MSE or Accuracy, should the measures be divided by their "standard errors"? Default is False. |
sqrt |
Boolean value indicating whether importance metrics should be adjusted via a square root transformation. Default is True. |
type |
either 1 or 2, specifying the type of importance measure (1=mean decrease in accuracy or node impurity or mean decrease in gini). Default is "both". |
num_var |
Optional argument for reducing the number of variables to the top 'num_var'. Must be an integer between 1 and the total number of predictor variables in the model. |
Value
A ggplot dotchart showing the importance of the variables that were plotted.
Examples
rf <- randomForest::randomForest(factor(Species) ~ .,
importance = TRUE,
data = iris
)
ggvip(rf, scale = FALSE, sqrt = TRUE, type = "both")
Lichen data from the Current Vegetation Survey
Description
Data were collected between 1993 and 1999 as part of the Lichen Air Quality surveys on public lands in Oregon and southern Washington. Observations were obtained from 1-acre (0.4 ha) plots at Current Vegetation Survey (CVS) sites. Indicator variables denote the presences and absences of 7 lichen species. Data for each sampled plot include the topographic variables elevation, aspect, and slope; bioclimatic predictors including maximum, minimum, daily, and average temperatures, relative humidity precipitation, evapotranspiration, and vapor pressure; and vegetation variables including the average age of the dominant conifer and percent conifer cover. The data in lichenTest were collected from half-acre plots at CVS sites in the same geographical region and contains many of the same variables, including presences and absences for the 7 lichen species. As such, it is a good test dataset for predictive methods applied to the Lichen Air Quality data.
Usage
lichen
Format
A data frame with 840 observations and 40 variables. One variable is a location identifier, 7 (coded as 0 and 1) identify the presence or absence of a type of lichen species, and 32 are characteristics of the survey site where the data were collected.
There were 12 monthly values in the original data for each of the bioclimatic predictors. Principal components analyses suggested that for each of these predictors 2 principal components explained the vast majority (95.0%-99.5%) of the total variability. Based on these analyses, indices were created for each set of bioclimatic predictors. The variables with the suffix Ave in the variable name are the average of 12 monthly variables. The variables with the suffix Diff are contrasts between the sum of the April-September monthly values and the sum of the October-December and January-March monthly values, divided by 12. Roughly speaking, these are summer-to-winter contrasts.
The variables are summarized as follows:
- LobaOreg
Lobaria oregana (Absent = 0, Present = 1)
- EvapoTransAve
Average monthly potential evapotranspiration in mm
- EvapoTransDiff
Summer-to-winter difference in monthly potential evapotranspiration in mm
- MoistIndexAve
Average monthly moisture index in cm
- MoistIndexDiff
Summer-to-winter difference in monthly monthly moisture index in cm
- PrecipAve
Average monthly precipitation in cm
- PrecipDiff
Summer-to-winter difference in monthly precipitation in cm
- RelHumidAve
Average monthly relative humidity in percent
- RelHumidDiff
Summer-to-winter difference in monthly relative humidity in percent
- PotGlobRadAve
Average monthly potential global radiation in kJ
- PotGlobRadDiff
Summer-to-winter difference in monthly potential global radiation in kJ
- AveTempAve
Average monthly average temperature in degrees Celsius
- AveTempDiff
Summer-to-winter difference in monthly average temperature in degrees Celsius
- MaxTempAve
Average monthly maximum temperature in degrees Celsius
- MaxTempDiff
Summer-to-winter difference in monthly maximum temperature in degrees Celsius
- MinTempAve
Average monthly minimum temperature in degrees Celsius
- MinTempDiff
Summer-to-winter difference in monthly minimum temperature in degrees Celsius
- DayTempAve
Mean average daytime temperature in degrees Celsius
- DayTempDiff
Summer-to-winter difference in average daytime temperature in degrees Celsius
- AmbVapPressAve
Average monthly average ambient vapor pressure in Pa
- AmbVapPressDiff
Summer-to-winter difference in monthly average ambient vapor pressure in Pa
- SatVapPressAve
Average monthly average saturated vapor pressure in Pa
- SatVapPressDiff
Summer-to-winter difference in monthly average saturated vapor pressure in Pa
- Aspect
Aspect in degrees
- TransAspect
Transformed Aspect: TransAspect=(1-cos(Aspect))/2
- Elevation
Elevation in meters
- Slope
Percent slope
- ReserveStatus
Reserve Status (Reserve, Matrix)
- StandAgeClass
Stand Age Class (< 80 years, 80+ years)
- ACONIF
Average age of the dominant conifer in years
- PctVegCov
Percent vegetation cover
- PctConifCov
Percent conifer cover
- PctBroadLeafCov
Percent broadleaf cover
- TreeBiomass
Live tree (> 1inch DBH) biomass, above ground, dry weight
Source
Cutler, D. Richard., Thomas C. Edwards Jr., Karen H. Beard, Adele Cutler, Kyle T. Hess, Jacob Gibson, and Joshua J. Lawler. 2007. Random Forests for Classification in Ecology. Ecology 88(11): 2783-2792.
https://CRAN.R-project.org/package=EZtune/
Mtry Tune via VIPs
Description
A list of data.frames and useful plots for user evaluations of the randomForest hyperparameter mtry.
Usage
mtry_compare(
formula,
data = NULL,
scale = FALSE,
sqrt = TRUE,
num_var,
mvec,
...
)
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. |
data |
an optional data frame containing the variables in the model. By default the variables are taken from the environment which randomForest is called from. |
scale |
For permutation based measures such as MSE or Accuracy, should the measures be divided by their "standard errors"? Default is False. |
sqrt |
Boolean value indicating whether importance metrics should be adjusted via a square root transformation. Default is True. |
num_var |
Optional integer argument for reducing the number of variables to the top 'num_var'. Should be an integer between 1 and the total number of predictor variables in the model or it should be a positive proportion of variables desired. |
mvec |
Optional vector argument for defining choices of mtry to have the function consider. Should be a vector of integers between 1 and the total number of predictor variables in the model. Or it can be a vector of proportions (strictly less than 1) of the number of predictor variables. |
... |
Other parameters to pass to the randomForest function. |
Value
A list of data.frames, useful plots, and forest objects for user evaluations of the randomForest hyperparameter mtry.
Examples
m <- mtry_compare(factor(Species) ~ ., data = iris, sqrt = TRUE)
m
Partial Correlations
Description
A list of data.frames and useful plots for user evaluations of correlations and partial correlations of predictors with a given response.
Usage
partial_cor(formula, data = NULL, model = lm, num_var, ...)
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. |
data |
a data frame containing the variables in the model. By default the variables are taken from the environment which the model is called from. |
model |
Model to use for extraction partial correlations. Possible model choices are lm, rpart, randomForest, and svm. Default is lm. |
num_var |
Optional integer argument for reducing the number of variables to the top 'num_var'. Should be an integer between 1 and the total number of predictor variables in the model or it should be a positive proportion of variables desired. |
... |
Additional arguments to be passed to model as needed. |
Value
A list of data.frames and useful plots for user evaluations of partial correlations.
Examples
pcs <- partial_cor(Petal.Length ~ ., data = iris[-5], model = lm)
pcs$plot_y_part_cors
Non-linear Variance Inflation Factors
Description
A list of data.frames and useful plots for user evaluations of the randomForest hyperparameter mtry.
Usage
robust_vifs(formula, data, model = randomForest, log10 = TRUE, num_var, ...)
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. |
data |
an optional data frame containing the variables in the model. By default the variables are taken from the environment which the model is called from. |
model |
Model to use for extraction partial correlations. Possible model choices are rpart. |
log10 |
Applies a log10 transformation to VIFs when True. Default is True. |
num_var |
Optional integer argument for reducing the number of variables to the top 'num_var'. Should be an integer between 1 and the total number of predictor variables in the model or it should be a positive proportion of variables desired. |
... |
Additional arguments to be passed to models as needed. |
Value
A list of data.frames and useful plots for user evaluations of VIFs.
Examples
rv <- robust_vifs(Petal.Length ~ ., data = iris[-5], model = lm)
rv