drglm.multinom {drglm}

Fitting Multinomial Logistic Regression model in "Divide and Recombine" approach to Large Data Sets

Description

Function drglm.multinom fits multinomial logistic regressiosn model to big data sets in divide and recombine approach.

Usage

drglm.multinom(formula, data, k)

Arguments

formula

An entity belonging to the "formula" class (or one that can be transformed into that class) represents a symbolic representation of the model that needs to be adjusted. Specifics about how the model is defined can be found in the 'Details' section.

data

A data frame, list, or environment that is not required but can be provided if available.

k

Number of subsets to be used.

Value

A "Multinomial (Polytomous) Logistic Regression Model" is fitted in "Divide and Recombine" approach.

Author(s)

MH Nayem

References

Karim, M. R., & Islam, M. A. (2019). Reliability and Survival Analysis. In Reliability and Survival Analysis. Venables WN, Ripley BD (2002). Modern Applied Statistics with S, Fourth edition. Springer, New York. ISBN 0-387-95457-0, https://www.stats.ox.ac.uk/pub/MASS4/.

See Also

big.drglm, drglm

Examples

set.seed(123)
#Number of rows to be generated
n <- 10000
#creating dataset
dataset <- data.frame( pred_1 = round(rnorm(n, mean = 50, sd = 10)),
pred_2 = round(rnorm(n, mean = 7.5, sd = 2.1)),
pred_3 = as.factor(sample(c("0", "1"), n, replace = TRUE)),
pred_4 = as.factor(sample(c("0", "1", "2"), n, replace = TRUE)),
pred_5 = as.factor(sample(0:15, n, replace = TRUE)),
pred_6 = round(rnorm(n, mean = 60, sd = 5)))
#fitting multinomial logistic regression model
mmodel=drglm::drglm.multinom(
pred_4~ pred_1+ pred_2+ pred_3+ pred_5+ pred_6, data=dataset, k=10)
#Output
mmodel