http://r.789695.n4.nabble.com/lmer-causes-segfault-td2335407.html

LmerExamples

in lmer this is pretty crucial if you have factors that are identified by numerical values:

{{{
>dummy.data = read.csv("~/tmp/tmp.csv")
>dummy.data$c <- factor(dummy.data$c)


> str(dummy.data)
'data.frame':   4800 obs. of  5 variables:
 $ g : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
 $ s : Factor w/ 24 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ c : Factor w/ 100 levels "1","2","3","4",..: 1 1 2 2 3 3 4 4 5 5 ...
 $ pc: Factor w/ 2 levels " c"," p": 1 2 1 2 1 2 1 2 1 2 ...
 $ x : num  0.175 0.511 0.237 0.572 0.306 ...

}}}

ezANOVA converts numeric values to factors because it knows that  everything but the dependent variable must be a factor.

ezANOVA is limited in the number of levels of a factor (roughly 50?)

lmer can take multiple levels, but doesn't give you standard stats.

{{{

> library(ez)
>ezPrecis(dummy.data)
Data frame dimensions: 4800 rows, 5 columns
      type missing values min max
g   factor     1.       1.   1   2
s   factor     1.     24    1.  24
c   factor     1.    100    1. 100
pc  factor     1.       1.   c   p
x  numeric     1.   3848    1.   1

}}}



{{{
The factor (ordered) function creates a factor (ordered
factor) from a vector. Factor labels can be speci
ed in the
optional labels argument.
Suppose the spray variable in the InsectSprays data was
stored as numeric values 1; 2; : : : ; 6. We convert it back to a
factor with factor.
> str(sprays <- within(InsectSprays, spray <- as.integer(spray))'data.frame': 72 obs. of 2 variables:
$ count: num 10 7 20 14 14 12 10 23 17 20 ...
$ spray: int 1 1 1 1 1 1 1 1 1 1 ...
> str(sprays <- within(sprays, spray <- factor(spray,
+ labels = LETTERS[1:6])))
'data.frame': 72 obs. of 2 variables:
$ count: num 10 7 20 14 14 12 10 23 17 20 ...
$ spray: Factor w/ 6 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...


}}}

-- Main.DennisNorris - 27 Oct 2010