RDocumentation: Logit

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if(!require('lessR')) {
    install.packages('lessR')
    library('lessR')
}

# Gender has values of "M" and "F"
d <- Read("Employee", quiet=TRUE)
# logit regression, rely upon default parameter value: data=d
Logit(Gender ~ Years)

# short name
lr(Gender ~ Years)

# Modify the default settings as specified
Logit(Gender ~ Years, res_row=8, res_sort="rstudent", digits_d=8, pred=FALSE)

Logit(Gender ~ Years)

# Multiple logistic regression model with specified probability thresholds
#  for classification into the reference group
# just for employees who have worked more than 5 years at the firm
Logit(Gender ~ Years + Salary, prob_cut=c(.4, .7), rows=(Years > 3))

# Custom contrasts for categorical predictor with glm parameter contrasts
cnt <- contr.treatment(n=3, base=2)
Logit(Gender ~ JobSat, contrasts=list(JobSat=cnt))


# Compare nested models
# easier and better treatment of missing data with lessR function:  Nest
full_model <- Logit(Gender ~ Years + Salary)
reduced_model <- Logit(Gender ~ Years)
anova(reduced_model, full_model)

# Save the three plots as pdf files 4 inches square, gray scale
#Logit(Gender ~ Years, pdf_file="MyModel.pdf",
#      width=4, height=4, colors="gray")

# Specify new values of the predictor variables to calculate
#  forecasted values
d <- Read("Cars93")
Logit(Source ~ HP + MidPrice, X1_new=seq(100,250,50), X2_new=c(10,60,10))