countRSquare: Count pseudo r-squared for logistic and other binary outcome...

In rcompanion: Functions to Support Extension Education Program Evaluation

Count pseudo r-squared for logistic and other binary outcome models

Description

Produces the count pseudo r-squared measure for models with a binary outcome.

Usage

countRSquare(
  fit,
  digits = 3,
  suppressWarnings = TRUE,
  plotit = FALSE,
  jitter = FALSE,
  pch = 1,
  ...
)

Arguments

fit	The fitted model object for which to determine pseudo r-squared. glm and glmmTMB are supported. Others may work as well.
digits	The number of digits in the outputted values.
suppressWarnings	If TRUE, suppresses warning messages.
plotit	If TRUE, produces a simple plot of actual vs. predicted values.
jitter	If TRUE, jitters the "actual" values in the plot.
pch	Passed to plot.
...	Additional arguments.

Details

The count pseudo r-squared is simply the number of correctly predicted observations divided the total number of observations.

This version is appropriate for models with a binary outcome.

The adjusted value deducts the count of the most frequent outcome from both the numerator and the denominator.

It is recommended that the model is fit on data in long format. That is, that the weight option not be used in the model.

The function makes no provisions for NA values. It is recommended that NA values be removed before the determination of the model.

Value

A list including a description of the submitted model, a data frame with the pseudo r-squared results, and a confusion matrix of the results.

Author(s)

Salvatore Mangiafico, [e-mail]

References

https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds/, https://rcompanion.org/handbook/H_08.html, https://rcompanion.org/rcompanion/e_06.html

See Also

nagelkerke, efronRSquared, accuracy

Examples

data(AndersonBias)

### Covert data to long format

Long = AndersonBias[rep(row.names(AndersonBias), AndersonBias$Count),
                    c("Result", "County", "Gender")]
rownames(Long) = seq(1:nrow(Long))
str(Long)

### Fit model and determine count r-square

model = glm(Result ~ County + Gender + County:Gender,
            data = Long,
            family = binomial())

countRSquare(model)

rcompanion documentation built on Sept. 17, 2023, 5:07 p.m.