pmut.edap: Create Exploratory Visualization for A Vector of Features

In chengjunhou/pmut: Predictive Modeling Utility-function Toolkit

Description

This function creates visualization for a vector of features, using either pmut.edap.disc or pmut.edap.cont, depending on the feature class. Features of class factor, character, and logical will use pmut.edap.disc; Feature of class numeric will use pmut.edap.cont; Feature of class integer with unique values smaller than number of bins specified by meta will use pmut.edap.disc, otherwise use pmut.edap.cont. Some progression information will be printed on console.

Usage

pmut.edap(datatable, varvec, targetstring, meta = c(50, 4, 0.01, 0.99),
  qbin = FALSE, pred.df = NULL)

Arguments

datatable	Object of class data.frame or data.table
varvec	Vector of character indicating the column names inside datatable to productionalize the visulization
targetstring	Single character string indicating the column name inside datatable for the response
meta	Numeric vector with length of 4 (default is c(50,4,0.01,0.99)): 1st indicates number of bins, 2nd indicates bin rounding digits, 3rd and 4th indicate the outlier percentile
qbin	Logical (default is FALSE), FALSE indicates equal length bins, TRUE indicates equal weight bins (quantile view)
pred.df	Object of class data.frame (optional), with column being prediction from each model

Value

Views of line plot stacked above the histogram varied by the feature

Examples

# Output the plots into a pdf file
pdf("EDA_Diamonds.pdf", width=12, height=10)
df = data.frame(ggplot2::diamonds)
pmut.edap(df, names(df)[-7], "price")
dev.off()

chengjunhou/pmut documentation built on May 23, 2019, 4:24 p.m.