s_BayesGLM: Bayesian GLM

In egenn/rtemis: Machine Learning and Visualization

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,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  metric = NULL,
  maximize = NULL,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  grid.verbose = verbose,
  verbose = TRUE,
  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
family	Character or function for the error distribution and link function to be used. See arm::bayesglm for details.
prior.mean	Numeric, vector: Prior mean for the coefficients. If scalar, it will be replicated to length N features.
prior.scale	Numeric, 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	Numeric: 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.
prior.mean.for.intercept	Numeric: Prior mean for the intercept.
prior.scale.for.intercept	Numeric: Default = NULL, which results in 10 for a logit model, and 10*1.6 for probit model.
prior.df.for.intercept	Numeric: Prior df for the intercept.
min.prior.scale	Numeric: Minimum prior scale for the coefficients.
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.
drop.baseline	Logical: If TRUE, drop the base level of factor features.
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 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)
metric	Character: Metric to minimize, or maximize if maximize = TRUE during grid search. Default = NULL, which results in "Balanced Accuracy" for Classification, "MSE" for Regression, and "Coherence" for Survival Analysis.
maximize	Logical: If TRUE, metric will be maximized if grid search is run.
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"
question	Character: the question you are attempting to answer with this model, in plain language.
grid.verbose	Logical: Passed to gridSearchLearn
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 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 parameters to pass to arm::bayesglm

Author(s)

E.D. Gennatas

See Also

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), 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_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()

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