Print factor analysis from factanal() with item labels

Question

EDIT

So it looks like it's something in my call to library(reshape) that's breaking the labeling of factors. This was not included in the minimal example, but will be added now. It's not needed to create the example, but it's needed to recreate the issue. I need the library to get my data in shape to even do factanal() . Any ideas what part of reshape is breaking it and how to fix it?

---

Original question

I have been running factor analyses on my data and have been having an intermittent issue with the way results are printed.

If I create a data set like the following:

library(reshape)
mock <- data.frame(
  sample_name1 = sample(1:100),
  sample_name2 = sample(1:100),
  sample_name3 = sample(1:100),
  s_amplename_4 = sample(1:100),
  samplename5 = sample(1:100),
  sa_mplen_a_me_6 = sample(1:100),
  samplename7 = sample(1:100),
  samplename8 = sample(1:100)
)

and run a factor analysis with

factanal(mock, factors = 2)

I get the output to print out very prettily with item names as labels for the rows, eg:

# Snip snip
Loadings:
                Factor1 Factor2
sample_name1    -0.126  -0.105 
sample_name2    -0.414         
sample_name3     0.665         
s_amplename_4           -0.314 
samplename5              0.850 
sa_mplen_a_me_6 -0.117         
samplename7      0.442         
samplename8     -0.139 

This kind of output is exactly what I am looking for. However, when I run the same type of analysis on my own data (and I apologize for the length here):

miniset <- structure(list(`clarity1` = c(2, 2, 2, 3, 4.5, 1.5, 1.5, 3.5, 
                                           2, 6, 2.5, 4, 1, 1.5, 6, 2, 5.5, 2, 2, 3, 1.5, 5, 3.5, 2, 1.5, 
                                           2.5, 3, 3, 2, 1), 
                          `clarity2` = c(1.5, 2, 2, 2, 3.5, 5, 3, 5, 
                                           2, 4, 2, 2.5, 1, 1.5, 2, 4, 5, 2, 2, 3.5, 6, 1, 2, 1.5, 1, 2, 
                                           2, 3, 6.5, 1), 
                          `clarity3` = c(3, 3.5, 2, 3.5, 5.5, 4, 6, 5.5, 
                                           2, 3, 3, 3.5, 1, 2.5, 2, 5, 5, 5, 2, 6.5, 5.5, 5, 5.5, 6, 3, 
                                           2, 2, 5, 4.5, 5.5), 
                          `detail1` = c(3, 4, 2, 6, 5, 6.5, 5.5, 
                                          4, 3, 6, 2.5, 4, 1, 4, 2, 4.5, 7, 6.5, 2, 6.5, 6, 2, 6, 5, 2.5, 
                                          5.5, 4, 5.5, 6, 1.5), 
                          `detail2` = c(3.5, 4, 4, 6.5, 4.5, 6, 
                                          4, 4.5, 2, 6, 2.5, 5, 2, 4, 3, 6, 7, 7, 2, 6.5, 6, 3, 6, 6, 2.5, 
                                          6, 3, 5, 6.5, 2.5), 
                          `detail3` = c(2.5, 4, 2, 6, 5, 6, 6, 4, 
                                          2, 6, 2, 5, 2, 3, 3, 5, 6.5, 6, 2, 6.5, 7, 7, 5.5, 5, 3.5, 2, 
                                          3, 5, 6, 2), 
                          `complete1` = c(2, 2.5, 2, 3, 3.5, 5.5, 2.5, 2.5, 
                                            2, 3, 3, 3.5, 2, 4, 3, 3, 7, 4, 2, 3, 6, 3, 5.5, 2, 3, 2, 2, 
                                            3, 6, 3), 
                          `complete2` = c(3, 4.5, 2, 3, 4.5, 6, 6, 4.5, 3, 
                                            3, 3.5, 4, 2, 5, 3, 4, 7, 4, 2, 6, 7, 5, 5, 6, 3, 3, 5, 5, 6, 
                                            2), 
                          `complete3` = c(3, 4.5, 2, 2.5, 4.5, 6.5, 5, 5, 2, 6.5, 
                                            3.5, 3.5, 1, 3, 3, 2.5, 7, 4, 2, 6, 1.5, 7, 5.5, 6.5, 3.5, 5.5, 
                                            3, 3, 2.5, 1), 
                          `truthful1` = c(2.5, 2, 2, 3, 3.5, 2, 2, 2.5, 
                                            2, 3, 3, 2.5, 2, 3, 2, 2, 3.5, 3, 2, 3.5, 1.5, 1, 3.5, 2.5, 3, 
                                            2, 2, 3, 1.5, 1.5), 
                          `truthful2` = c(2.5, 1.5, 2, 2, 3, 1.5, 
                                            2, 1, 1, 5.5, 3, 3.5, 1, 4.5, 2, 2, 5, 2, 2, 1.5, 4.5, 1, 3.5, 
                                            2, 3.5, 2.5, 2, 2, 4.5, 1), 
                          `truthful3` = c(2, 1.5, 2, 3.5, 
                                            2.5, 2, 2, 2.5, 2, 2, 3.5, 2.5, 1, 1.5, 3, 2, 5, 3, 3, 2, 3.5, 
                                            1, 2, 1, 3.5, 2, 2, 2.5, 4.5, 1), 
                          `relevant1` = c(1.5, 1.5, 
                                            2, 5, 2.5, 1.5, 2, 3.5, 2, 4.5, 2.5, 3.5, 1, 3.5, 3, 1.5, 5.5, 
                                            3.5, 2, 2, 6, 3, 3.5, 3, 1.5, 2, 3, 3, 6, 1), 
                          `relevant2` = c(1.5, 
                                            3, 2, 2, 3.5, 1.5, 2.5, 5.5, 1, 2, 3.5, 2, 1, 1.5, 2, 4, 5.5, 
                                            2, 3, 5.5, 5.5, 1, 4, 5, 1.5, 2, 3, 2.5, 3, 1), 
                          `relevant3` = c(1.5, 
                                            2, 2, 3, 2, 1, 2, 2, 1, 2, 1.5, 2.5, 1, 1.5, 2, 1.5, 5.5, 5, 
                                            2, 1, 7, 1, 1, 2, 1, 2, 3, 3, 2.5, 1)), 
                     .Names = c("clarity1", 
                                "clarity2", "clarity3", "detail1", "detail2", "detail3", 
                                "complete1", "complete2", "complete3", "truthful1", "truthful2", 
                                "truthful3", "relevant1", "relevant2", "relevant3"), 
                     row.names = c(NA, 30L), class = c("cast_df", "data.frame"))

factanal(miniset, factors = 3)

the result is much less pretty, eg:

Loadings:
      Factor1 Factor2 Factor3
 [1,]          0.222   0.664 
 [2,]  0.559   0.524         
 [3,]  0.824                 
 [4,]  0.740   0.361   0.282 
 [5,]  0.698   0.374   0.251 
 [6,]  0.783   0.278   0.265 
 [7,]  0.498   0.598   0.140 
 [8,]  0.796   0.227   0.204 
 [9,]  0.490  -0.240   0.835 
[10,]  0.147   0.156   0.348 
[11,]          0.697   0.324 
[12,]          0.756         
[13,]  0.319   0.811   0.204 
[14,]  0.567   0.252   0.108 
[15,]  0.320   0.690 

So rather than having the nice item names as labels for the loadings, I now get indices. While that's fine for me, I'll be working with a professor tomorrow who is less familiar with R and will probably get frustrated by the lack of labels. So what happens to the labels in the second case? And how can I get them back?

Answer 1

The issue is that miniset is a cast_df and factanal calls as.matrix(x) . The as.matrix.cast_df method uses rrownames and rcolnames (all reshape functions) to extract "special dimension names".

For miniset these are NULL (hence the rownames are lost). Without knowing how you constructed miniset I can't help further here. (You must have used reshape to construct miniset at some point as you have created a cast_df object.

Good news is that

factanal(as.data.frame(miniset))

Works as you wish