calculate_IC_weights {restriktor}

Calculating IC weights based on IC values (AIC, ORIC, GORIC(A), BIC, SIC, ...)

Description

This function transforms IC values into IC weights: IC values denote the ordering of hypotheses/models, while IC weights quantify the relative strength of hypotheses/models.

Usage

calculate_IC_weights(IC, hypo_names = NULL)
calc_ICweights(IC, hypo_names = NULL)

## S3 method for class 'goric_ICw'
print(x, digits = max(3, getOption("digits") - 4), ...)

Arguments

IC

A vector or one-column matrix with information criteria (AIC, ORIC, GORIC(A), BIC, SIC, ...) values of length 'NrHypos', where 'NrHypos' stands for the number of hypotheses/ models.

x

an object of class con_goric.

hypo_names

Optional. Vector containing 'NrHypos' characters which will be used for labeling the hypothesis. Default: H1, H2, ...

digits

the number of significant digits to use when printing.

...

no additional arguments for now.

Value

IC weights, which quantify the relative strength of hypotheses/models.

Examples

IC <- c(1,2,3)
calculate_IC_weights(IC)


## PT weights 
# This examples shows how to calculate PT weights.
# Notably, one is interested in PT weights when the log likelihood for two or more
# hypotheses are (approximately) equal.
# Then, the comparison between those hypotheses is solely based on the PT values.
# The IC weights will then equal the PT weights.
# In that case, there is support for the overlap (boundary) of these hypotheses.
# Thus, when the IC weights equal the PT weights for a (sub)set of hypotheses,
# then there is support for the overlap (boundary) of these hypotheses.

y <- rnorm(30)
group <- factor(rep(c("A","B","C"), each = 10))
fit.lm <- lm(y ~ -1 + group)
est <- coef(fit.lm)
VCOV_est <- vcov(fit.lm)
H1 <- "groupA < groupB < groupC"
results <- goric(est, VCOV = VCOV_est, hypotheses = list(H1), 
                 comparison = "complement", type = "gorica")
calculate_IC_weights(results$result[,3])