model.fit: Regression models, reporting overall significance of the...

In myominnoo/stats2: Tools of statistical analysis and data management

Description

model.fit overall significance of the model and interpretation

Usage

model.fit(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, IDRE https://stats.idre.ucla.edu/r/dae/logit-regression/

Author(s)

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

See Also

model.output

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")
summarize(mydata)
tab(admit, mydata)
tab(rank, mydata)
mydata$rank <- label(mydata$rank)

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

model.output(mylogit)
model.output(mylogit, raw = TRUE)
model.fit(mylogit)

## End(Not run)

myominnoo/stats2 documentation built on Nov. 4, 2019, 8:33 p.m.