2

I am trying to fit a VLMC to a dataset where the longest sequence is 296 states. I do it as shown below:

# Load libraries
library(PST)
library(RCurl)
library(TraMineR)

# Load and transform data
x <- getURL("https://gist.githubusercontent.com/aronlindberg/08228977353bf6dc2edb3ec121f54a29/raw/241ef39125ecb55a85b43d7f4cd3d58f617b2ecf/challenge_level.csv")
data <- read.csv(text = x)

data.seq <- seqdef(data[,2:ncol(data)], missing = NA, right = NA, nr = "*")
S1 <- pstree(data.seq, ymin = 0.01, lik = TRUE, with.missing = TRUE, nmin = 2)

This, however, yields the following error:

Error in res[i, , drop = FALSE] : subscript out of bounds

How can I fit the model to data with sequences this long? Are there any good justifications for limiting the length within the model?

histelheim
  • 4,938
  • 6
  • 33
  • 63

1 Answers1

3

The problem comes from your data. By not setting L in the pstree function, you mean that you want to fit a model of maximum order. The fitting process produces an error at L=8, since you have nmin=2 but at this order only one context has nmin=2

> cprob(data.seq, L=8, nmin=2)
 [>] 21 sequences, min/max length: 19/296
 [>] computing prob., L=8, 2043 distinct context(s)
 [>] removing 1894 context(s) where n<2
 [>] total time: 0.156 secs
                        EX  FA I1  I2 I3 N1 N2 N3 NR QU TR [n]
I2-I3-FA-I3-EX-I3-EX-I2  0 0.5  0 0.5  0  0  0  0  0  0  0   2

Fitting a model using L=8 works fine

S1 <- pstree(data.seq, ymin = 0.01, lik = TRUE, nmin = 2, L=8) 

 [>] 21 sequence(s) - min/max length: 19/296
 [>] max. depth L=8, nmin=2, ymin=0.01
     [L]  [nodes]
       0        1
       1       11
       2       99
       3      368
       4      340
       5      126
       6       34
       7        4
       8        1
 [>] computing sequence(s) likelihood ... (0.804 secs)
 [>] total time: 2.968 secs

Again, you don't need to use any 'missing', 'right' or 'nr' option in seqdef(), nor 'with.missing' in pstree()

Best, Alexis

Alexis Gabadinho
  • 66
  • 1
  • You are right that I don't need the `missing` for the `pstree()` function, however, if I don't use the `missing` option then problems are caused later when I try to use `tune()` and `cmine()`. E.g. the `tune()` function gives: `[>] model 1: AICc=NA (C=4.74) Error in if (pst.IC == min(IC, na.rm = TRUE)) { : missing value where TRUE/FALSE needed In addition: Warning message: In min(IC, na.rm = TRUE) : no non-missing arguments to min; returning Inf` Are there any arguments to turn off missing values in these functions? – histelheim Feb 24 '17 at 14:41
  • Also, how did you identify that an error occurred at `L=8`? – histelheim Feb 24 '17 at 14:49