testModelFit: Regression models, reporting overall significance of the...

In myominnoo/mStats_beta: A tool for data management and statistical analysis

Description

testModelFit overall significance of the model and interpretation

Usage

testModelFit(model)

Arguments

model

object glm or lm type

Details

This test asks whether the model with predictors fits significantly better than a model with just an intercept (i.e., a null model).

The test statistic is the difference between the residual deviance for the model with predictors and the null model. The test statistic is distributed chi-squared with degrees of freedom equal to the differences in degrees of freedom between the current and the null model (i.e., the number of predictor variables in the model).

Reference:

1. LOGIT REGRESSION | R DATA ANALYSIS EXAMPLES. UCLA: Statistical Consulting Group. from https://stats.idre.ucla.edu/r/dae/logit-regression/ (accessed September 27, 2019)

Author(s)

Myo Minn Oo (Email: [e-mail] | Website: https://myominnoo.github.io/)

See Also

fModelOutput

Examples

## Not run: 
## example from IRDE website:
## https://stats.idre.ucla.edu/r/dae/logit-regression/
mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
codebook(mydata)
tab(admit, mydata)
tab(rank, mydata)

mylogit <- glm(admit ~ gre + gpa + factor(rank), data = mydata, family = "binomial")
summary(mylogit)

fModelOutput(mylogit) # generates parameters
testModelFit(mylogit) # test overall significant of the model

## End(Not run)

myominnoo/mStats_beta documentation built on Feb. 29, 2020, 8:17 a.m.