update() inside a function only searches the global environment?

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()

Although I really like @Hadley's answer (and will likely use that function myself), you can also specify a data argument in the update function. (Here, I assumed you wanted to pass temp to your models.)

model1a <- update(model1, ~.-factor1:factor2, data = temp)

EDIT

If you're looking to compare models with anova, update will mung up the name of the data argument and "trick" anova into believing that the two models were fit using two different datasets. Updating only the formula and creating a new model will avoid this:

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