[R] Linear Discriminant Analysis in R

[cobbler_squad]

Joris, 

You are a life saver. Based on two sample files above, I think lda should go
something like this:

vowel_features <- read.table(file = "mappings_for_vowels.txt")
mask_features <- data.frame(as.matrix(read.table(file =
"3dmaskdump_ICA_37_Combined.txt")))
G <- vowel_features[15]

cvc_lda <- lda(G~ vowel_features[15], data=mask_features,
na.action="na.omit", CV=TRUE)

ERROR: Error in model.frame.default(formula = G ~ vowel_features[15], data =
mask_features,  : 
  invalid type (list) for variable 'G'

I am clearly doing something wrong declaring G (how should I declare
grouping in R when I need to use one column from vowel_feature file)? Sorry
for stupid questions and thank you for being so helpful!

---------
again, sample files that I am working with:

mappings_for_vowels.txt:

    V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
V21 V22 V23 V24 V25 V26
1    E  0  0  0  0  0  0  0  0   0   0   0   0   1   1   0   0   0   1   0  
0   0   0   0   0   0
2    o  0  0  0  0  0  0  0  0   0   0   0   0   1   0   0   1   0   1   0  
1   0   1   0   0   0
3    I  0  0  0  0  0  0  0  0   0   0   0   0   1   1   0   0   1   0   0  
0   0   0   0   0   0
4    ^  0  0  0  0  0  0  0  0   0   0   0   0   1   0   1   0   0   1   0  
0   0   0   0   0   0
5    @  0  0  0  0  0  0  0  0   0   0   0   0   1   0   0   1   0   0   1  
0   0   0   0   0   0

and the mask_features file is:

              V42          V43          V44          V45          V46         
V47          V48          V49
  [1,]  2.890891625  2.881188521  2.887784444 -2.882606612 -2.888877341 
2.879834384  2.886483229  2.883815864
  [2,]  2.763404707  2.756198683  2.761863881 -2.756827983 -2.762268531 
2.754305072  2.760017050  2.758399799
  [3,]  0.556614506  0.556377530  0.556247414 -0.556300910 -0.556098321 
0.557495060  0.557383073  0.556867424
  [4,]  0.367065248  0.366962036  0.366870087 -0.366794442 -0.366644148 
0.366613343  0.366537320  0.366953464
  [5,]  0.423692393  0.421835623  0.421741829 -0.421897460 -0.421659824 
0.421567705  0.421465738  0.422407838

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