Regression with NA Values in Unordered Factors


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Documentation for package ‘nauf’ version 1.1.0

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nauf-package Regression with NA values in unordered factors.
anova.nauf.glmerMod Type III anovas for mixed effects 'nauf' models.
anova.nauf.lmerMod Type III anovas for mixed effects 'nauf' models.
anova.nauf.merMod Type III anovas for mixed effects 'nauf' models.
as.array.nauf.pmm.stan Posterior samples of marginal means from Bayesian 'nauf' models.
as.data.frame.nauf.pmm.stan Posterior samples of marginal means from Bayesian 'nauf' models.
as.data.frame.summ.nauf.pmm.stan Posterior samples of marginal means from Bayesian 'nauf' models.
as.matrix.nauf.pmm.stan Posterior samples of marginal means from Bayesian 'nauf' models.
as.shinystan-method Create a shinystan object for posterior marginal means from nauf models.
as.shinystan-method Create a shinystan object from a nauf.stanreg model.
fricatives Catalan and Spanish intervocalic alveolar fricatives.
nauf Regression with NA values in unordered factors.
nauf-pmmeans Predicted marginal means for 'nauf' models.
nauf.glmerMod Class for fitted mixed effects models with 'nauf' contrasts.
nauf.glmerMod-class Class for fitted mixed effects models with 'nauf' contrasts.
nauf.lmerMod Class for fitted mixed effects models with 'nauf' contrasts.
nauf.lmerMod-class Class for fitted mixed effects models with 'nauf' contrasts.
nauf.merMod Class for fitted mixed effects models with 'nauf' contrasts.
nauf.pmm.list List of predicted marginal means objects for 'nauf' models.
nauf.pmm.stan Posterior samples of marginal means from Bayesian 'nauf' models.
nauf.stanreg Class for fitted Bayesian models with 'nauf' contrasts.
nauf.terms Class for 'terms' objects which contain information about 'nauf' contrasts.
nauf_contrasts Not applicable unordered factor contrasts.
nauf_glFormula Create a model frame and fixed and random effects model matrices using 'nauf' contrasts.
nauf_glm Fit a fixed effects regression using 'nauf' contrasts.
nauf_glm.nb Fit a fixed effects regression using 'nauf' contrasts.
nauf_glmer Fit a mixed effects regression using 'nauf' contrasts.
nauf_glmer.nb Fit a mixed effects regression using 'nauf' contrasts.
nauf_kfold Cross-validation for 'nauf.stanreg' models.
nauf_lFormula Create a model frame and fixed and random effects model matrices using 'nauf' contrasts.
nauf_lm Fit a fixed effects regression using 'nauf' contrasts.
nauf_lmer Fit a mixed effects regression using 'nauf' contrasts.
nauf_model.frame Create a model frame using 'nauf' contrasts.
nauf_model.matrix Create a fixed effects model matrix using 'nauf' contrasts.
nauf_pmmeans Predicted marginal means for 'nauf' models.
nauf_ref.grid Predicted marginal means for 'nauf' models.
nauf_stan_glm Fit a Bayesian fixed effects regression with 'nauf' contrasts.
nauf_stan_glm.nb Fit a Bayesian fixed effects regression with 'nauf' contrasts.
nauf_stan_glmer Fit a Bayesian mixed effects regression with 'nauf' contrasts.
nauf_stan_glmer.nb Fit a Bayesian mixed effects regression with 'nauf' contrasts.
nauf_stan_lm Fit a Bayesian fixed effects regression with 'nauf' contrasts.
nauf_stan_lmer Fit a Bayesian mixed effects regression with 'nauf' contrasts.
plosives Spanish intervocalic plosives.
predict.nauf.glmerMod Predictions from a mixed effects 'nauf' model at new data values.
predict.nauf.lmerMod Predictions from a mixed effects 'nauf' model at new data values.
predict.nauf.merMod Predictions from a mixed effects 'nauf' model at new data values.
print.nauf.pmm.stan Posterior samples of marginal means from Bayesian 'nauf' models.
print.summ.nauf.pmm.stan Posterior samples of marginal means from Bayesian 'nauf' models.
summary.nauf.pmm.stan Posterior samples of marginal means from Bayesian 'nauf' models.