s_GLM: Generalized Linear Model (C, R)

In egenn/rtemis: Machine Learning and Visualization

Generalized Linear Model (C, R)

Description

Train a Generalized Linear Model for Regression or Classification (i.e. Logistic Regression) using stats::glm. If outcome y has more than two classes, Multinomial Logistic Regression is performed using nnet::multinom

Usage

s_GLM(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  family = NULL,
  interactions = NULL,
  class.method = NULL,
  weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  intercept = TRUE,
  polynomial = FALSE,
  poly.d = 3,
  poly.raw = FALSE,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  na.action = na.exclude,
  removeMissingLevels = TRUE,
  question = NULL,
  verbose = TRUE,
  trace = 0,
  outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
  ...
)

Arguments

x	Numeric vector or matrix / data frame of features i.e. independent variables
y	Numeric vector of outcome, i.e. dependent variable
x.test	Numeric vector or matrix / data frame of testing set features Columns must correspond to columns in x
y.test	Numeric vector of testing set outcome
x.name	Character: Name for feature set
y.name	Character: Name for outcome
family	Error distribution and link function. See stats::family
interactions	List of character pairs denoting column names in x that should be entered as interaction terms in the GLM formula
class.method	Character: Define "logistic" or "multinom" for classification. The only purpose of this is so you can try nnet::multinom instead of glm for binary classification
weights	Numeric vector: Weights for cases. For classification, weights takes precedence over ifw, therefore set weights = NULL if using ifw. Note: If weight are provided, ifw is not used. Leave NULL if setting ifw = TRUE.
ifw	Logical: If TRUE, apply inverse frequency weighting (for Classification only). Note: If weights are provided, ifw is not used.
ifw.type	Integer 0, 1, 2 1: class.weights as in 0, divided by min(class.weights) 2: class.weights as in 0, divided by max(class.weights)
upsample	Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Note: upsample will randomly sample with replacement if the length of the majority class is more than double the length of the class you are upsampling, thereby introducing randomness
downsample	Logical: If TRUE, downsample majority class to match size of minority class
resample.seed	Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)
intercept	Logical: If TRUE, fit an intercept term.
polynomial	Logical: if TRUE, run lm on poly(x, poly.d) (creates orthogonal polynomials)
poly.d	Integer: degree of polynomial.
poly.raw	Logical: if TRUE, use raw polynomials. Default, which should not really be changed is FALSE
print.plot	Logical: if TRUE, produce plot using mplot3 Takes precedence over plot.fitted and plot.predicted.
plot.fitted	Logical: if TRUE, plot True (y) vs Fitted
plot.predicted	Logical: if TRUE, plot True (y.test) vs Predicted. Requires x.test and y.test
plot.theme	Character: "zero", "dark", "box", "darkbox"
na.action	How to handle missing values. See ?na.fail
removeMissingLevels	Logical: If TRUE, finds factors in x.test that contain levels not present in x and substitutes with NA. This would result in error otherwise and no predictions would be made, ending s_GLM prematurely
question	Character: the question you are attempting to answer with this model, in plain language.
verbose	Logical: If TRUE, print summary to screen.
trace	Integer: If higher than 0, will print more information to the console.
outdir	Path to output directory. If defined, will save Predicted vs. True plot, if available, as well as full model output, if save.mod is TRUE
save.mod	Logical: If TRUE, save all output to an RDS file in outdir save.mod is TRUE by default if an outdir is defined. If set to TRUE, and no outdir is defined, outdir defaults to paste0("./s.", mod.name)
...	Additional arguments

Details

A common problem with glm arises when the testing set containts a predictor with more levels than those in the same predictor in the training set, resulting in error. This can happen when training on resamples of a data set, especially after stratifying against a different outcome, and results in error and no prediction. s_GLM automatically finds such cases and substitutes levels present in x.test and not in x with NA.

Value

rtMod

Author(s)

E.D. Gennatas

See Also

train_cv for external cross-validation

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GAM(), s_GBM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2ODL(), s_H2OGBM(), s_H2ORF(), s_HAL(), s_KNN(), s_LDA(), s_LM(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MARS(), s_MLRF(), s_NBayes(), s_NLA(), s_NLS(), s_NW(), s_PPR(), s_PolyMARS(), s_QDA(), s_QRNN(), s_RF(), s_RFSRC(), s_Ranger(), s_SDA(), s_SGD(), s_SPLS(), s_SVM(), s_TFN(), s_XGBoost(), s_XRF()

Other Interpretable models: s_AddTree(), s_C50(), s_CART(), s_GLMNET(), s_GLMTree(), s_LMTree()

Examples

x <- rnorm(100)
y <- .6 * x + 12 + rnorm(100) / 2
mod <- s_GLM(x, y)

egenn/rtemis documentation built on Oct. 28, 2024, 6:30 a.m.