ExtractCI: Calculates the Wald or a parametric bootstrap confidence...
In postHoc: Tools for Post-Hoc Analysis

Description Usage Arguments Details Value Author(s) Examples

posthoc is used to group or cluster the effects of liner, generalised linear and generalised linear mixed models according to significance of pairwise tests comparing the levels of the effects.

ExtractCI (Model, EffectIndices = NULL, EffectLabels = NULL,
         ParBootstrap = FALSE, Nboots = 999, digits = 4, coverage = 0.95,
         UpperBound = Inf, SignificanceLevel =  1-coverage,
         EffectsMatrix = NULL, Scale = 1.0, Location = 0.0,
         isBinomialModel = FALSE, BackTransform = TRUE)

`Model`	a model of class lm, glm, glmerMod, lme or gls.
`EffectIndices`	a vector containing the indices of the effects to be analysed (default = NULL, indicating that all the levels are used).
`EffectLabels`	a character vector with the labels of the effects (default = NULL, which implies that the corresponding labels of the model coefficient are used).
`ParBootstrap`	logic flag indicating whether the confidence intervals should be calculated with parametric bootstrap (default is false, i.e. the Wald confidence interval is used).
`Nboots`	number of bootstrap samples used for the confidence interval. (default = 999).
`digits`	number of digits used when reporting the results
`coverage`	the coverage of the confidence intervals (default = 0.95)
`UpperBound`	an upper bound to the confidence intervals (default = Inf)
`SignificanceLevel`	the significance level of the pairwise comparisons (default = 0.05).
`EffectsMatrix`	matrix defining contrasts to be compared (bypasses the EffectIndices, default is NULL, meaning that standard inference is performed).
`Scale`	a scaling factor multiplying the output table (default = 1, i.e., no scaling is used).
`Location`	a location term added to the output table (default = 0, i.e., no location shift is performed).
`isBinomialModel`	a logical flag indicating whther the model is a binomial model different than the Bernoulli (default = FALSE, i.e. not a binomial model).
`BackTransform`	should the parameters and CIs be back transformed by applying the inverse link function (default = TRUE)