Currently I am working with the dataset predictions. In this data I have converted clear character type variables into factors because I think factors work better than characters for glmtree() code (tell me if I am wrong with this):
> str(predictions)
'data.frame': 43804 obs. of 14 variables:
$ month : Factor w/ 7 levels "01","02","03",..: 6 6 6 6 1 1 2 2 3 3 ...
$ pred : num 0.21 0.269 0.806 0.945 0.954 ...
$ treatment : Factor w/ 2 levels "0","1": 1 1 2 2 2 2 2 2 2 2 ...
$ type : Factor w/ 4 levels "S","MS","ML",..: 1 1 4 4 4 4 4 4 4 4 ...
$ i_mode : Factor w/ 143 levels "AAA","ABC","CBB",..: 28 28 104 104 104 104 104 104 104 104 ...
$ r_mode : Factor w/ 29 levels "0","5","8","11",..: 4 4 2 2 2 2 2 2 2 2 ...
$ in_mode: Factor w/ 22 levels "XY",..: 11 11 6 6 6 6 6 6 6 6 ...
$ v_mode : Factor w/ 5 levels "1","3","4","7",..: 1 1 1 1 1 1 1 1 1 1 ...
$ di : num 1157 1157 1945 1945 1945 ...
$ cont : Factor w/ 5 levels "AN","BE",..: 2 2 2 2 2 2 2 2 2 2 ...
$ hk : num 0.512 0.512 0.977 0.977 0.941 ...
$ np : num 2 2 2 2 2 2 2 2 2 2 ...
$ hd : num 1 1 0.408 0.408 0.504 ...
$ nd : num 1 1 9 9 9 9 7 7 9 9 ...
I want to estimate a recursive partitioning model of this kind:
library("partykit")
glmtr <- glmtree(formula = pred ~ treatment + 1 | (month+type+i_mode+r_mode+in_mode+v_mode+di+cont+np+nd+hd+hk),
data = predictions,
maxdepth=6,
family = quasibinomial)
My data does not have any NA. However, the following error arises (even after changing characters by factors):
Error in matrix(0, nrow = mi, ncol = nl) :
invalid 'nrow' value (too large or NA)
In addition: Warning message:
In matrix(0, nrow = mi, ncol = nl) :
NAs introduced by coercion to integer range
Any clue?
Thank you
You are right that glmtree() and the underlying mob() function expect the split variables to be factors in case of nominal information. However, computationally this is only feasible for factors that have either a limited number of levels because the algorithm will try all possible partitions of the number of levels into two groups. Thus, for your i_mode factor this necessitates going through nl levels and mi splits into two groups with:
nl <- 143
mi <- 2^(nl - 1L) - 1L
mi
## [1] 5.575186e+42
Internally, mob() tries to create a matrix for storing all log-likelihoods associated with the corresponding partitioned models. And this is not possible because such a matrix cannot be represented. (And even if you could, then you wouldn't finish fitting all the associated models.) Admittedly, the error message is not very useful and should be improved. We will look into that for the next revision of the package.
For solving the problem, I would recommend to turn the variables i_mode, r_mode, and in_mode into variables that are more suitable for binary splitting with exhaustive search. Maybe, some of the variables are actually ordinal? If so, I would recommend to turn them into ordinal factors or in case of i_mode even into a numeric variable because the number of levels is large enough. Alternatively, you can maybe create several factors with different properties about the different levels that could then be used for partitioning.