s_LM: Linear model

In egenn/rtemis: Machine Learning and Visualization

Linear model

Description

Fit a linear model and validate it. Options include base lm(), robust linear model using MASS:rlm(), generalized least squares using nlme::gls, or polynomial regression using stats::poly to transform features

Usage

s_LM(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  weights = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  intercept = TRUE,
  robust = FALSE,
  gls = FALSE,
  polynomial = FALSE,
  poly.d = 3,
  poly.raw = FALSE,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  na.action = na.exclude,
  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
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.
robust	Logical: if TRUE, use MASS::rlm() instead of base lm()
gls	Logical: if TRUE, use nlme::gls
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
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 as 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 to be passed to MASS::rlm if robust = TRUE or MASS::lm.gls if gls = TRUE

Details

GLS can be useful in place of a standard linear model, when there is correlation among the residuals and/or they have unequal variances. Warning: nlme's implementation is buggy, and predict will not work because of environment problems, which means it fails to get predicted values if x.test is provided. robut = TRUE trains a robust linear model using MASS::rlm. gls = TRUE trains a generalized least squares model using nlme::gls.

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_GAM.default(), s_GAM.formula(), s_GBM(), s_GLM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2ODL(), s_H2OGBM(), s_H2ORF(), s_HAL(), s_KNN(), s_LDA(), 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()

Examples

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

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.