scalling: Converting coefficients of logistic regression into scores...

In scorecardModelUtils: Credit Scorecard Modelling Utils

Description

The function takes a logistic model as input and scales the coefficients into scores to be used for scorecard generation. The

Usage

scalling(base, target, model, point = 15, factor = 2, setscore = 660)

Arguments

base	base input dataframe
target	column / field name for the target variable to be passed as string (must be 0/1 type)
model	input logistic model from which the coefficients are to be picked
point	(optional) points after which the log odds will get multiplied by "factor" (default value is 15)
factor	(optional) factor by which the log odds must get multiplied after a step of "points" (default value is 2)
setscore	(optional) input for setting offset (default value is 660)

Value

The function returns a dataframe with the coefficients and scalled scores for each class of all explanatory variables of the model.

Author(s)

Arya Poddar <[e-mail]>

Examples

data <- iris
suppressWarnings(RNGversion('3.5.0'))
set.seed(11)
data$Y <- sample(0:1,size=nrow(data),replace=TRUE)
x <- c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")
iv_table_list <- iv_table(base = data,target = "Y",num_var_name = x,cat_var_name = "Species")
num_cat <- num_to_cat(base = data,num_woe_table = iv_table_list$num_woe_table)
log_model <- glm(Y ~ ., data = num_cat, family = "binomial")
scaling_tab <- scalling(base = num_cat,target = "Y",model = log_model)

Example output

scorecardModelUtils documentation built on May 2, 2019, 9:59 a.m.