Logistic Regression using for loops in R

Question

I am trying to run a binary logistic regression using For loops in R. My code for the same is as follows:

mydata5<-read.table(file.choose(),header=T,sep=",")
colnames(mydata5)
 Class <- 1:16   
Countries  <- 1:5
Months  <- 1:7
DayDiff  <- 1:28
mydata5$CT <- factor(mydata5$CT)
mydata5$CC <- factor(mydata5$CC)
mydata5$C <- factor(mydata5$C)
mydata5$DD <- factor(mydata5$DD)
mydata5$UM <- factor(mydata5$UM)
for(i in seq(along=Class))
   {
     mydata5$C=mydata5$C[i];

for(i2 in seq(along=Countries))
{
  mydata5$CC=mydata5$CC[i2];

for(i3 in seq(along=Months))
{
  mydata5$UM=mydata5$UM[i3];

for(i4 in seq(along=DayDiff))
{
  mydata5$DD=mydata5$DD[i4];

  lrfit5 <- glm(CT ~ C+CC+UM+DD, family = binomial(link = "logit"),data=mydata5)
  summary(lrfit5)
  library(lattice) 
  in_frame<-data.frame(C="mydata5$C[i]",CC="mydata5$CC[i2]",UM="mydata5$UM[i3]",DD="mydata5$DD[i4]")
  predict(lrfit5,in_frame, type="response",se.fit=FALSE)
}
}
}
}

However, I'm getting the following error: Error in contrasts<-(*tmp*, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels

Why is the error occurring? Also,the dataset "mydata5" has around 50000 rows.Please help.

Thanks in Advance.

We don't have your data set so we can't run this. You don't tell us which line the error happened (guess: the `glm`). Why don't you print i,i2,i3 and i4 in the loop to find out if it happens on the first time or on a specific time? Is it getting as far as the obvious problem with the construction of `in_frame` where you've put things in quotes? Please improve this question. — Spacedman, Sep 05 '14 at 07:07
Reading the error messages and guessing again it looks like the error you would get if you tried to fit a model with data that had categorical variables with only one category - like trying to fit an effect for "sex" with only "male" data points. But that's a guess because we don't have your datafile, or a sample, to try it on. — Spacedman, Sep 05 '14 at 07:09
What is the intention of all these loops?!? It looks like you are horribly disfiguring your data with all these bizarre reassignments. As @Spacedman said, you should include a [reproducible example](http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) otherwise we can't test possible solutions or even see where the error is really happening. I'm guessing that in the end, `mydata` has all identical covariate values after all those loops thanks to vector recycling. — MrFlick, Sep 05 '14 at 07:11
@Spacedman: yes, the error happened in the glm line. The categorical variables have the desired number of levels. I checked that using the function lapply(mydata5[c("C", "CC", "UM", "DD")], unique). Basically, my intention behind using the 4 For loops was that i want to run various permutations and combinations from within the 4 categorical variables and determining their individual probabilities. — Srinivasan Ramanujam, Sep 05 '14 at 08:15

score 1 · Answer 1 · answered Sep 05 '14 at 08:39

You have tried to do a regression with a factor having only one level. Since you haven't given us your data we can't reproduce your analysis but I can simply reproduce your error message:

> d = data.frame(x=runif(10),y=factor("M",levels=c("M","F")))
> d
            x y
1  0.07104688 M
2  0.11948466 M
3  0.20807068 M
4  0.24049508 M
5  0.44251492 M
6  0.69775646 M
7  0.44479983 M
8  0.64814971 M
9  0.75151207 M
10 0.38810621 M
> glm(x~y,data=d)
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
  contrasts can be applied only to factors with 2 or more levels

By setting one of the factor values to "F" I don't get the error message:

> d$y[5]="F"
> glm(x~y,data=d)

Call:  glm(formula = x ~ y, data = d)

Coefficients:
(Intercept)           yF  
    0.39660      0.04591  

Degrees of Freedom: 9 Total (i.e. Null);  8 Residual
Null Deviance:      0.5269 
Residual Deviance: 0.525    AIC: 4.91

So somewhere in your loops (which we cannot run because we don't have your data) you are doing this.

Logistic Regression using for loops in R

1 Answers1