How to debug "contrasts can be applied only to factors with 2 or more levels" error?

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Here are all the variables I'm working with:

str(ad.train)
$ Date                : Factor w/ 427 levels "2012-03-24","2012-03-29",..: 4 7 12 14 19 21 24 29 31 34 ...
 $ Team                : Factor w/ 18 levels "Adelaide","Brisbane Lions",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Season              : int  2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ...
 $ Round               : Factor w/ 28 levels "EF","GF","PF",..: 5 16 21 22 23 24 25 26 27 6 ...
 $ Score               : int  137 82 84 96 110 99 122 124 49 111 ...
 $ Margin              : int  69 18 -56 46 19 5 50 69 -26 29 ...
 $ WinLoss             : Factor w/ 2 levels "0","1": 2 2 1 2 2 2 2 2 1 2 ...
 $ Opposition          : Factor w/ 18 levels "Adelaide","Brisbane Lions",..: 8 18 10 9 13 16 7 3 4 6 ...
 $ Venue               : Factor w/ 19 levels "Adelaide Oval",..: 4 7 10 7 7 13 7 6 7 15 ...
 $ Disposals           : int  406 360 304 370 359 362 365 345 324 351 ...
 $ Kicks               : int  252 215 170 225 221 218 224 230 205 215 ...
 $ Marks               : int  109 102 52 41 95 78 93 110 69 85 ...
 $ Handballs           : int  154 145 134 145 138 144 141 115 119 136 ...
 $ Goals               : int  19 11 12 13 16 15 19 19 6 17 ...
 $ Behinds             : int  19 14 9 16 11 6 7 9 12 6 ...
 $ Hitouts             : int  42 41 34 47 45 70 48 54 46 34 ...
 $ Tackles             : int  73 53 51 76 65 63 65 67 77 58 ...
 $ Rebound50s          : int  28 34 23 24 32 48 39 31 34 29 ...
 $ Inside50s           : int  73 49 49 56 61 45 47 50 49 48 ...
 $ Clearances          : int  39 33 38 52 37 43 43 48 37 52 ...
 $ Clangers            : int  47 38 44 62 49 46 32 24 31 41 ...
 $ FreesFor            : int  15 14 15 18 17 15 19 14 18 20 ...
 $ ContendedPossessions: int  152 141 149 192 138 164 148 151 160 155 ...
 $ ContestedMarks      : int  10 16 11 3 12 12 17 14 15 11 ...
 $ MarksInside50       : int  16 13 10 8 12 9 14 13 6 12 ...
 $ OnePercenters       : int  42 54 30 58 24 56 32 53 50 57 ...
 $ Bounces             : int  1 6 4 4 1 7 11 14 0 4 ...
 $ GoalAssists         : int  15 6 9 10 9 12 13 14 5 14 ...

Here's the glm I'm trying to fit:

ad.glm.all <- glm(WinLoss ~ factor(Team) + Season  + Round + Score  + Margin + Opposition + Venue + Disposals + Kicks + Marks + Handballs + Goals + Behinds + Hitouts + Tackles + Rebound50s + Inside50s+ Clearances+ Clangers+ FreesFor + ContendedPossessions + ContestedMarks + MarksInside50 + OnePercenters + Bounces+GoalAssists, 
                  data = ad.train, family = binomial(logit))

I know it's a lot of variables (plan is to reduce via forward variable selection). But even know it's a lot of variables they're either int or Factor; which as I understand things should just work with a glm. However, every time I try to fit this model I get:

Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels

Which sort of looks to me as if R isn't treating my Factor variables as Factor variables for some reason?

Even something as simple as:

ad.glm.test <- glm(WinLoss ~ factor(Team), data = ad.train, family = binomial(logit))

isn't working! (same error message)

Where as this:

ad.glm.test <- glm(WinLoss ~ Clearances, data = ad.train, family = binomial(logit))

Will work!

Anyone know what's going on here? Why can't I fit these Factor variables to my glm??

Thanks in advance!

-Troy

glm lm regression r-faq r
3 Answers

Perhaps as a very quick step one is to verify that you do indeed have at least 2 factors. The quick way I found was:

df %>% dplyr::mutate_all(as.factor) %>% str
user_28836030

From my experience ten minutes ago this situation can happen where there are more than one category but with a lot of NAs. Taking the Kaggle Houseprice Dataset as example, if you loaded data and run a simple regression,

train.df = read.csv('train.csv')
lm1 = lm(SalePrice ~ ., data = train.df)

you will get same error. I also tried testing the number of levels of each factor, but none of them says it has less than 2 levels.

cols = colnames(train.df)
for (col in cols){
  if(is.factor(train.df[[col]])){
    cat(col, ' has ', length(levels(train.df[[col]])), '\n')
  }
}

So after a long time I used summary(train.df) to see details of each col, and removed some, and it finally worked:

train.df = subset(train.df, select=-c(Id, PoolQC,Fence, MiscFeature, Alley, Utilities))
lm1 = lm(SalePrice ~ ., data = train.df)

and removing any one of them the regression fails to run again with same error (which I have tested myself).

Another way to debug this error with a lot of NAs is, replace each NA with the most common attributes of the column. Note the following method cannot debug where NA is the mode of the column, which I suggest drop these columns or substutite these columns manually, individually rather than applying a function working on the whole dataset like this:

fill.na.with.mode = function(df){
    cols = colnames(df)
    for (col in cols){
        if(class(df[[col]])=='factor'){
            x = summary(df[[col]])
            mode = names(x[which.max(x)])
            df[[col]][is.na(df[[col]])]=mode
        }
        else{
            df[[col]][is.na(df[[col]])]=0
        }
    }
    return (df)
}

And above attributes generally have 1400+ NAs and 10 useful values, so you might want to remove these garbage attributes, even they have 3 or 4 levels. I guess a function counting how many NAs in each column will help.

user_90375900
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