title: logistic regression with caret and glmnet in R
body: <p>I'm trying to fit a logistic regression model to my data, using glmnet (for lasso) and caret (for k-fold cross-validation).  I've tried two different syntaxes, but they both throw an error:</p>
  
  <pre><code>fitControl &lt;- trainControl(method = "repeatedcv",
                         number = 10,
                         repeats = 3,
                         verboseIter = TRUE)
  
  # with response as a integer (0/1)
  fit_logistic &lt;- train(response ~.,
                     data = df_without,
                     method = "glmnet",
                     trControl = fitControl,
                     family = "binomial")
  
  Error in cut.default(y, breaks, include.lowest = TRUE) : 
   invalid number of intervals
  
  df_without$response &lt;- as.factor(df_without$response)
  # with response as a factor
  fit_logistic &lt;- train(as.matrix(df_without[1:47]), df_without$response,
                method = "glmnet",
                trControl = fitControl,
                family = "binomial")
  
  Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs,  : 
    NA/NaN/Inf in foreign function call (arg 5)
  In addition: Warning message:
  In lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs,  :
    NAs introduced by coercion
  </code></pre>
  
  <p>Do I need to convert my dataframe to a matrix or not?</p>
  
  <p>Does my response variable need to be a factor or just 0/1 integers?</p>
  
  <p>The .Rdata file with the df_without data frame is <a href="https://www.dropbox.com/s/1zdv264rrvzat3c/df_without.Rdata?dl=0" rel="nofollow">here</a>.</p>
  
  <blockquote>
    <p>sessionInfo()</p>
  </blockquote>
  
  <pre><code>R version 3.2.0 (2015-04-16)
  Platform: x86_64-apple-darwin13.4.0 (64-bit)
  Running under: OS X 10.10.1 (Yosemite)
  
  locale:
  [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
  
  attached base packages:
  [1] parallel  splines   stats     graphics  grDevices utils         datasets  methods   base     
  
  other attached packages:
   [1] e1071_1.6-4     plyr_1.8.2      gbm_2.1.1       survival_2.38-1     glmnet_2.0-2    foreach_1.4.2  
   [7] Matrix_1.2-0    caret_6.0-47    ggplot2_1.0.1   lattice_0.20-31     lubridate_1.3.3 RJDBC_0.2-5    
  [13] rJava_0.9-6     DBI_0.3.1      
  
  loaded via a namespace (and not attached):
   [1] Rcpp_0.11.6         compiler_3.2.0      nloptr_1.0.4            class_7.3-12        iterators_1.0.7    
   [6] tools_3.2.0         digest_0.6.8        lme4_1.1-7              memoise_0.2.1       nlme_3.1-120       
  [11] gtable_0.1.2        mgcv_1.8-6          brglm_0.5-9             SparseM_1.6         proto_0.3-10       
  [16] BradleyTerry2_1.0-6 stringr_1.0.0       gtools_3.5.0            grid_3.2.0          nnet_7.3-9         
  [21] minqa_1.2.4         reshape2_1.4.1      car_2.0-25              magrittr_1.5        scales_0.2.4       
  [26] codetools_0.2-11    MASS_7.3-40         pbkrtest_0.4-2          colorspace_1.2-6    quantreg_5.11      
  [31] stringi_0.4-1       munsell_0.4.2  
  </code></pre>