rtemis (version 0.79)

s.LM: 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, intercept = TRUE, robust = FALSE,
  gls = FALSE, polynomial = FALSE, poly.d = 3, poly.raw = FALSE,
  print.plot = TRUE, plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"),
  na.action = na.exclude, question = NULL, rtclass = 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 ipw, therefore set weights = NULL if using ipw. Note: If weight are provided, ipw is not used. Leave NULL if setting ipw = TRUE. Default = NULL

intercept

Logical: If TRUE, fit an intercept term. Default = TRUE

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

String: "zero", "dark", "box", "darkbox"

na.action

How to handle missing values. See ?na.fail

question

String: the question you are attempting to answer with this model, in plain language.

rtclass

String: Class type to use. "S3", "S4", "RC", "R6"

verbose

Logical: If TRUE, print summary to screen.

trace

Integer: If higher than 0, will print more information to the console. Default = 0

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

Value

rtMod

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.

See Also

elevate for external cross-validation

Other Supervised Learning: s.ADABOOST, s.ADDTREE, s.BART, s.BAYESGLM, s.BRUTO, s.C50, s.CART, s.CTREE, s.DA, s.ET, s.EVTREE, s.GAM.default, s.GAM.formula, s.GAMSEL, s.GAM, s.GBM3, s.GBM, s.GLMNET, s.GLM, s.GLS, s.H2ODL, s.H2OGBM, s.H2ORF, s.IRF, s.KNN, s.LDA, s.MARS, s.MLRF, s.MXN, s.NBAYES, s.NLA, s.NLS, s.NW, s.POLYMARS, s.PPR, s.PPTREE, s.QDA, s.QRNN, s.RANGER, s.RFSRC, s.RF, s.SGD, s.SPLS, s.SVM, s.TFN, s.XGBLIN, s.XGB

Examples

# NOT RUN {
x <- rnorm(100)
y <- .6 * x + 12 + rnorm(100)/2
mod <- s.LM(x, y)
# }