function to create minimal adequate model w/ interaction variables

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ekbrown77

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Jun 30, 2016, 11:26:05 PM6/30/16
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I want to look at both the maximal model and minimal adequate model of 48 mixed effects linear regressions (with random intercepts only) created with lme4::lmer. Manually creating the minimal adequate model for these 48 models would be laborious.

Has anyone written a function to automate the creation of the minimal adequate model using a backward selection procedure (as described in SFLWR, 2nd ed. p. 260, using car::Anova(type = 3)) that correctly deals with interaction variables. I don't want the function to delete non-significant main effect variables if they are involved in a significant interaction variable. 

I've written a function that correctly creates the minimal adequate model of models without interaction variables, but I can't figure out a reasonably succinct way to do so when the model has interaction variable. I don't want to reinvent the wheel if someone is willing to share their wheel with me, that is, a function or script.

Thanks in advance if you are willing to share your function or script.

For what it's worth, here's what I've written to get the minimal adequate model w/o interaction variables:

library(dplyr)
library(stringr)
get_min_adequate <- function(input) {
  # input <- model1
  output <- input
  ar <- car::Anova(output, type = 3)
  ar <- broom::tidy(ar) %>% rename(p_value = `Pr..Chisq.`)
  top_row <- ar %>% arrange(desc(p_value)) %>% slice(1)
  highest_p_value <- top_row[1, 'p_value']
  if (highest_p_value > 0.05) {
    while (highest_p_value > 0.05) {
      cat("removing ", top_row[1, 'term'], "\n")
      new_formula <- str_c("update(output, ~. -", top_row[1, 'term'], ")")
      output <- eval(parse(text = new_formula))
      top_row <- car::Anova(output, type = 3) %>% broom::tidy() %>% rename(p_value = `Pr..Chisq.`) %>% arrange(desc(p_value)) %>% slice(1)
      highest_p_value <- top_row[1, 'p_value']
    }  # next iteration
  }  # end if p value if > 0.05
  else {
    output <- input
  }
  final_formula <- summary(output) %>% .$call
  final_formula <- as.character(final_formula)
  final_formula <- str_c(
    str_replace(final_formula[1], regex("^lme4"), "lmerTest"),
    "(",
    final_formula[2],
    ", data = ",
    final_formula[3],
    ")"
  )
  output <- eval(parse(text = final_formula))
  return(output)
}  # end function definition

Stefan Th. Gries

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Jun 30, 2016, 11:28:53 PM6/30/16
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I think the package lmerTest has such a function. I'll look it up and will let you know if you don't already have it by then.

STG
--
Stefan Th. Gries
----------------------------------
Univ. of California, Santa Barbara
http://tinyurl.com/stgries
----------------------------------

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Stefan Th. Gries

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Jun 30, 2016, 11:32:07 PM6/30/16
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Yes, it's just lmerTest::step.

ekbrown77

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Jul 1, 2016, 12:17:42 AM7/1/16
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Sweet! This is exactly what I needed. Thanks for pointing it out to me. Gotta love the open source R community!

BTW, lmerTest::step throws an error if you log a frequency measure in the formula to lme4::lmer, for example,
lme4::lmer(dep_var ~ log(lex_freq + 1), ...)

Error in mat %*% rho$fixEffs : non-conformable arguments

You simply need to log any frequency measurements beforehand, for example:
library(dplyr)
input_df
<-
  input_df
%>%
  mutate
(lex_freq_log = log(.$lex_freq + 1))

model1
<- lme4::lmer(dep_var ~ lex_freq_log, ...)
lmerTest
::step(model1)

Thanks again (to Stefan for pointing out this function to me and to Alexandra Kuznetsova and collaborators for the lmerTest package).

Stefan Th. Gries

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Jul 1, 2016, 12:20:47 AM7/1/16
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> BTW, lmerTest::step throws an error if you log a frequency measure in the formula to lme4::lmer

Several functions do that, some of the effects package functions react similarly so it's always best to create those things before the model fitting (and only then attach).

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