HelpersMG (version 6.2)

compare_AICc: Compares the AICc of several outputs

Description

This function is used to compare the AICc of several outputs obtained with the same data but with different set of parameters.
Each object must have associated logLik() method with df and nobs attributes.
AICc for object x will be calculated as 2*factor.value*logLik(x)+(2*attributes(logLik(x))$df*(attributes(logLik(x))$df+1)/(attributes(logLik(x))$nobs-attributes(logLik(x))$df-1).

Usage

compare_AICc(
  ...,
  factor.value = -1,
  silent = FALSE,
  FUN = function(x) specify_decimal(x, decimals = 2)
)

Value

A list with DeltaAICc and Akaike weight for the models.

Arguments

...

Successive results to be compared as lists.

factor.value

The $value of the list object is multiplied by factor.value to calculate BIC.

silent

If TRUE, nothing is displayed.

FUN

Function used to show values

Author

Marc Girondot

Details

compare_AICc compares the AICc of several outputs obtained with the same data.

See Also

Other AIC: ExtractAIC.glm(), FormatCompareAIC(), compare_AIC(), compare_BIC()

Examples

if (FALSE) {
library("HelpersMG")
# Here two different models are fitted
x <- 1:30
y <- rnorm(30, 10, 2)+log(x)
plot(x, y)
d <- data.frame(x=x, y=y)
m1 <- lm(y ~ x, data=d)
m2 <- lm(y ~ log(x), data=d)
compare_BIC(linear=m1, log=m2, factor.value=-1)
# Here test if two datasets can be modeled with a single model
x2 <- 1:30
y2 <- rnorm(30, 15, 2)+log(x2)
plot(x, y, ylim=c(5, 25))
plot_add(x2, y2, col="red")
d2 <- data.frame(x=x2, y=y2)
m1_2 <- lm(y ~ x, data=d2)
x_grouped <- c(x, x2)
y_grouped <- c(y, y2)
d_grouped <- data.frame(x=x_grouped, y=y_grouped)
m1_grouped <- lm(y ~ x, data=d_grouped)
compare_AICc(separate=list(m1, m1_2), grouped=m1_grouped, factor.value=-1)
# Or simply
compare_AICc(m1=list(AICc=100), m2=list(AICc=102))
}