consmixed: Automated model selection process for the Consumer data

In alku86/SensMixed: Tests in Linear Mixed Effects Models

Description

Constructs the biggest possible model and reduces it to the best by principle of parcimony. First elimination of random effects is performed following by elimination of fixed effects. The LRT test is used for testing random terms, F-type hypothesis test is used for testing fixed terms. The post-hoc and plots are provided

Usage

consmixed(response, Prod_effects, Cons_effects=NULL,
Cons, data, structure = 3, alpha.random = 0.1, alpha.fixed = 0.05, ...)

Arguments

response	name of the liking variable in the Consumer data
Prod_effects	vector with names of the variables associated with products
Cons_effects	vector with names of the effects associated with consumers
Cons	name of the column in the data that represents consumers
data	data frame (data from consumer studies)
structure	one of the values in c(1,2,3). 1:Analysis of main effects, Random consumer effect AND interaction between consumer and the main effects(Automized reduction in random part, NO reduction in fixed part). 2: Main effects AND all 2-factor interactions. Random consumer effect AND interaction between consumer and all fixed effects (both main and interaction ones). (Automized reduction in random part, NO reduction in fixed part). 3: Full factorial model with ALL possible fixed and random effects. (Automized reduction in random part, AND automized reduction in fixed part).
alpha.random	significance level for elimination of the random part (for LRT test)
alpha.fixed	significance level for elimination of the fixed part (for F test)
...	other potential arguments.

Value

rand.table	table with value of Chi square test, p-values e t.c. for the random effects
anova.table	table which tests whether the model fixed terms are significant (Analysis of Variance)
model	Final model - object of class lmer or gls (after all the required reduction has been performed)

Author(s)

Alexandra Kuznetsova, Per Bruun Brockhoff, Rune Haubo Bojesen Christensen

Examples

library(SensMixed)
data(ham)

consmixed(response="Informed.liking", 
Prod_effects= c("Product","Information"), 
Cons_effects=c("Gender","Age"), Cons = "Consumer", data =ham, structure=1)

alku86/SensMixed documentation built on May 10, 2019, 9:21 a.m.