Calculate R Squared for Averaged Model(s)

This function is used to calculate the multiple and adjusted R squared values for a set of model estimates.

r_squared(estimates, data, response, x_vars, display = TRUE)

Arguments

estimates

A data frame with 2 columns. The first column must hold the variable names and if there is an intercept, the intercept should also have a name in this column. The second column should hold the estimates

data

The data frame used in the model

response

A character equal to the name of the column in data that holds the response variable

x_vars

A character or vector of the explanatory variable names. Should also equal the column names in data that holds the explanatory variable data. The name of the intercept should NOT be included in this vector.

display

Logical. If TRUE, then the R Squared values are printed.

Value

A data frame with 1 row and 2 columns. The columns hold the Multiple R Squared and the Adjusted R Squared, respectively.

Examples

if (FALSE) {

library(MuMIn)
library(AICcmodavg)

## lm.sr is an object returned from sr.LM()
dat <- lm.sr[['Data']][['Anolis.N']]
out <- AIC_avg(lm.sr[['Models']][['Anolis.N']],
               data = dat,
               response = 'Anolis.N',
               x_vars = c(vars, 'sq_Area'),
               groups = data.frame(Genus = 'Anolis',
                                   Status = 'N'),
               cum.weight = 0.95,
               table = FALSE)

vars <- attr(lm.sr[['Models']][['Anolis.N']]$terms,'term.labels')

tmp <- r_squared(out[, c('Variable', 'Estimate')],
                  data = dat,
                  response = 'Anolis.N',
                  x_vars = vars,
                  display = FALSE)

}