s.BAYESGLM: Bayesian GLM

Description

Train a bayesian GLM using arm::bayesglm

Usage

s.BAYESGLM(x, y = NULL, x.test = NULL, y.test = NULL,
  family = NULL, prior.mean = 0, prior.scale = NULL, prior.df = 1,
  prior.mean.for.intercept = 0, prior.scale.for.intercept = NULL,
  prior.df.for.intercept = 1, min.prior.scale = 1e-12, scaled = TRUE,
  keep.order = TRUE, drop.baseline = TRUE, maxit = 100,
  x.name = NULL, y.name = NULL, weights = NULL, ipw = TRUE,
  ipw.type = 2, upsample = FALSE, upsample.seed = NULL,
  metric = NULL, maximize = NULL, print.plot = TRUE,
  plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"), question = NULL,
  grid.verbose = TRUE, verbose = TRUE, outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE), ...)

Arguments

Numeric vector or matrix / data frame of features i.e. independent variables

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

family

Error distribution and link function. See stats::family

prior.mean

Float, vector: Prior mean for the coefficients. If scalar, it will be replicated to length N features. Default = 0

prior.scale

Float, vector: Prior scale for the coefficients. Default = NULL, which results in 2.5 for logit, 2.5*1.6 for probit. If scalar, it will be replicated to length N features.

prior.df

Float: Prior degrees of freedom for the coefficients. Set to 1 for t distribution; set to Inf for normal prior distribution. If scalar, it will be replicated to length N features. Default = 1

prior.mean.for.intercept

Float: Default = 0

prior.scale.for.intercept

Float: Default = NULL, which results in 10 for a logit model, and 10*1.6 for probit model

prior.df.for.intercept

Float: Default = 1

min.prior.scale

Float: Minimum prior scale for the coefficients. Default = 1e-12

scaled

Logical: If TRUE, the scale for the prior distributions are: For feature with single value, use prior.scale, for predictor with two values, use prior.scale/range(x), for more than two values, use prior.scale/(2*sd(x)). If response is gaussian, prior.scale is multiplied by 2 * sd(y). Default = TRUE

keep.order

Logical: If TRUE, the feature positions are maintained, otherwise they are reordered: main effects, interactions, second-order, third-order, etc. Default = TRUE

drop.baseline

Logical: If TRUE, drop the base level of factor features. Default = TRUE

maxit

Integer: Maximum number of iterations

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

ipw

Logical: If TRUE, apply inverse probability weighting (for Classification only). Note: If weights are provided, ipw is not used. Default = TRUE

ipw.type

Integer 0, 1, 2 1: class.weights as in 0, divided by max(class.weights) 2: class.weights as in 0, divided by min(class.weights) Default = 2

upsample

Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Caution: 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

upsample.seed

Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)

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"

question

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

verbose

Logical: If TRUE, print summary to screen.

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 parameters to pass to arm::bayesglm

Description

Usage

Arguments

See Also