In brian-j-smith/MachineShop: Machine Learning Models and Tools

Model Constructor Functions

library(MachineShop)

info <- modelinfo()
types <- c("binary" = "b", "factor" = "f", "matrix" = "m", "numeric" = "n",
           "ordered" = "o", "Surv" = "S")
x <- lapply(names(info), function(modelname) {
  c(modelname, ifelse(names(types) %in% info[[modelname]]$response_types, types, NA))
})
df <- as.data.frame(do.call(rbind, x), stringsAsFactors = FALSE)
names(df) <- c("Function", names(types))
rdoc_names <- sapply(df$Function, function(name) {
  switch(name,
    "CoxStepAICModel" = "CoxModel",
    "GLMStepAICModel" = "GLMModel",
    "PDAModel" = "FDAModel",
    "SurvRegStepAICModel" = "SurvRegModel",
    "SVMANOVAModel" = "SVMModel",
    "SVMBesselModel" = "SVMModel",
    "SVMLaplaceModel" = "SVMModel",
    "SVMLinearModel" = "SVMModel",
    "SVMPolyModel" = "SVMModel",
    "SVMRadialModel" = "SVMModel",
    "SVMSplineModel" = "SVMModel",
    "SVMTanhModel" = "SVMModel",
    "XGBDARTModel" = "XGBModel",
    "XGBLinearModel" = "XGBModel",
    "XGBTreeModel" = "XGBModel",
    name
  )
})

toString2 <- function(x) toString(na.omit(x))
df_classes <- data.frame(
  Function = rdoc_url(df$Function, rdoc_names),
  Label = sapply(info, getElement, name = "label"),
  Categorical = apply(df[c("binary", "factor", "ordered")], 1, toString2),
  Continuous = apply(df[c("matrix", "numeric")], 1, toString2),
  Survival = apply(df["Surv"], 1, toString2)
)
names(df_classes)[3:5] <- paste0(names(df_classes)[3:5], footnote_marker_number(1:3))

kable(df_classes,
      caption = "Package-supplied model constructor functions and supported response variable types.",
      align = c("l", "l", "c", "c", "c"), row.names = FALSE,
      escape = FALSE) %>%
  kable_styling(c("striped", "condensed"), full_width = FALSE, position = "center") %>%
  add_header_above(c(" " = 1, " " = 1, "Response Variable Types" = 3)) %>%
  footnote(number = c("b = binary factor, f = factor, o = ordered factor",
                      "m = matrix, n = numeric",
                      "S = Surv"))