Question

I tried to write a wrapper function to do likelihood ratio tests in batches. I tried to include update() to update the initial model. However, it seems that instead of looking for objects inside the function, it searches for objects in the global environment.

fake <- data.frame(subj= rep(1:5, 4), 
                   factor1 = rep(LETTERS[c(1,2,1,2)], each=5), 
                   factor2 = rep(letters[1:2], each=10), 
                   data=sort(rlnorm(20)))

foo <- function(){
                  temp <- fake
                  model1 <- lmer(data~factor1*factor2 + (1 |subj), temp)
                  model1a <- update(model1, ~.-factor1:factor2)
                  model1a}

And it gives an error message below:

Error in eval(expr, envir, enclos) : object 'factor1' not found

Is there anyway to make update() search within the function? Thank you!

EDIT:

I made a mistake. I wanted to pass “temp” to lmer, not “fake”.

EDIT2:
One convenient solution suggested is to simply specify the data object. Although update() now has no problem with this, anova() seems to think that the models I am trying to compare are based on different data objects

 foo <- function(){
                  temp <- fake
                  model1 <- lmer(data~factor1*factor2 + (1 |subj), data=temp)
                  model1a <- update(model1, ~.-factor1:factor2, data=temp)
                  anova(model1, model1a)
            }
 foo()

I get an error message:

 Error in anova(model1, model1b) : 
   all models must be fit to the same data object

I suppose this error goes beyond update(). But I wonder if anyone knows how this can be resolved. Note that if I write the function without using update() and instead spell out the models (see below), the error above goes away:

 foo <- function(){
                  temp <- fake
                  model1 <- lmer(data~factor1*factor2 + (1 |subj), data=temp)
                  model1a <- lmer(data~factor1 + factor2 + (1 |subj), data=temp)
                  anova(model1, model1a)
            }
 foo()

 Data: temp
 Models:
 model1a: data ~ factor1 + factor2 + (1 | subj)
 model1: data ~ factor1 * factor2 + (1 | subj)
         Df     AIC    BIC  logLik  Chisq Chi Df Pr(>Chisq)  
 model1a  5 -4.6909 3.7535  7.3454                           
 model1   6 -8.8005 1.3327 10.4003 6.1097      1    0.01344 *
 ---
 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

EDIT 3:
It seems that the issue is with anova(). I also tried the suggestion by @hadley

foo2 <- function(){
  my_update <- function(mod, formula = NULL, data = NULL) {
  call <- getCall(mod)
  if (is.null(call)) {
    stop("Model object does not support updating (no call)", call. = FALSE)
  }
  term <- terms(mod)
  if (is.null(term)) {
    stop("Model object does not support updating (no terms)", call. = FALSE)
  }
  if (!is.null(data)) call$data <- data
  if (!is.null(formula)) call$formula <- update.formula(call$formula, formula)
  env <- attr(term, ".Environment")
  eval(call, env, parent.frame())}

      model1 <- lmer(data~factor1*factor2 + (1 |subj), temp)
      model1a <- my_update(model1, ~.-factor1:factor2)
      anova(model1, model1a)
 }
 foo2()

I got an error message as shown below:

 Error in as.data.frame.default(data) : 
   cannot coerce class 'structure("mer", package = "lme4")' into a data.frame

Answer 1

I’ve been bitten by this behaviour before too, so I wrote my own version of update. It evaluates everything in the environment of the formula, so it should be fairly robust.

my_update <- function(mod, formula = NULL, data = NULL) {
  call <- getCall(mod)
  if (is.null(call)) {
    stop("Model object does not support updating (no call)", call. = FALSE)
  }
  term <- terms(mod)
  if (is.null(term)) {
    stop("Model object does not support updating (no terms)", call. = FALSE)
  }

  if (!is.null(data)) call$data <- data
  if (!is.null(formula)) call$formula <- update.formula(call$formula, formula)
  env <- attr(term, ".Environment")

  eval(call, env, parent.frame())
}

library(nlme4)

fake <- data.frame(
  subj = rep(1:5, 4), 
  factor1 = rep(LETTERS[c(1,2,1,2)], each = 5), 
  factor2 = rep(letters[1:2], each = 10), 
  data = sort(rlnorm(20)))

foo <- function() {
  temp <- fake
  model1 <- lmer(data ~ factor1 * factor2 + (1 | subj), fake)
  model1a <- my_update(model1, ~ . - factor1:factor2)
  model1a
}
foo()