Description Usage Arguments Examples
plot_infotable
creates WOE or NWOE bar charts for one or more variables.
For multi-variable plots, bar charts will be displayed in a grid for comparison.
1 2 | plot_infotables(information_object = NULL, variables = NULL,
same_scales = FALSE, show_values = FALSE)
|
information_object |
object generated by the information package. |
variables |
vector of one more variables. For multi-variable plots, bar charts will be displayed in a grid. |
same_scales |
if set to TRUE, all plots will have the same limits on the y-axes (default is FALSE). |
show_values |
if set to TRUE, values will be displayed on the bar chart (default is FALSE). |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 |
##------------------------------------------------------------
## WOE plots
##------------------------------------------------------------
library(Information)
data(train, package="Information")
train <- subset(train, TREATMENT==1)
IV <- Information::create_infotables(data=train, y="PURCHASE", parallel=FALSE)
# Plotting a single variable
Information::plot_infotables(IV, "N_OPEN_REV_ACTS")
# Plotting multiple variables in a grid for comparison
Information::plot_infotables(IV, IV$Summary$Variable[1:4], same_scale=TRUE)
# If the goal is to plot multiple variables individually, as opposed to a comparison-grid, we can
# loop through the variable names and create individual plots
## Not run:
names <- names(IV$Tables)
plots <- list()
for (i in 1:length(names)){
plots[[i]] <- plot_infotables(IV, names[i])
}
# Showing the top 18 variables
plots[1:18]
## End(Not run)
# We can speed up the creation of the information tables by invoking the parallel option (default)
# If we leave ncore as the default, create_infotables() will set ncore to available clusters - 1
## Not run:
train <- subset(train, TREATMENT==1)
IV <- Information::create_infotables(data=train, y="PURCHASE")
## End(Not run)
closeAllConnections()
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