IC.weights: Calculating IC weights based on IC values (AIC, ORIC,...

In rebeccakuiper/GoricEvSyn: GORIC(A) evidence synthesis (GoricEvSyn) aggregates the evidence for theory-based hypotheses from multiple studies that may use diverse designs to investigate the same central theory.

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

IC.weights(IC, Name_Hypo = NULL)

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

Value

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

Examples

IC <- myIC # Example based on 3 hypotheses.
IC.weights(IC)

# Change labels of hypotheses #
# For example, let us say that we tested a linear model vs quadratic vs cubic.
Name_Hypo <- c("Linear", "Quadratic", "Cubic")
IC.weights(IC, Name_Hypo)

rebeccakuiper/GoricEvSyn documentation built on July 3, 2023, 6:41 a.m.