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Expand summary table of patterns and frequencies

Source: R/expand.table.R
expand.table.Rd

The expand.table function expands a summary table of unique response patterns to a full sized data-set. By default the response frequencies are assumed to be on rightmost column of the input data, though this can be modified.

Usage

expand.table(tabdata, freq = colnames(tabdata)[ncol(tabdata)], sample = FALSE)

Arguments

tabdata

An object of class data.frame or matrix with the unique response patterns and the number of frequencies in the rightmost column (though see freq for details on how to omit this column)

freq

either a character vector specifying the column in tabdata to be used as the frequency count indicator for each response pattern (defaults to the right-most column) or a integer vector of length nrow(tabdata) specifying the frequency counts. When using the latter approach the tabdata input should not include any information regarding the counts, and instead should only include the unique response patterns themselves

sample

logical; randomly switch the rows in the expanded table? This does not change the expanded data, only the row locations

Value

Returns a numeric matrix with all the response patterns.

References

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples


data(LSAT7)
head(LSAT7) # frequency in right-most column
#>   Item.1 Item.2 Item.3 Item.4 Item.5 freq
#> 1      0      0      0      0      0   12
#> 2      0      0      0      0      1   19
#> 3      0      0      0      1      0    1
#> 4      0      0      0      1      1    7
#> 5      0      0      1      0      0    3
#> 6      0      0      1      0      1   19
LSAT7full <- expand.table(LSAT7)
head(LSAT7full)
#>   Item.1 Item.2 Item.3 Item.4 Item.5
#> 1      0      0      0      0      0
#> 2      0      0      0      0      0
#> 3      0      0      0      0      0
#> 4      0      0      0      0      0
#> 5      0      0      0      0      0
#> 6      0      0      0      0      0
dim(LSAT7full)
#> [1] 1000    5

# randomly switch rows in the expanded response table
LSAT7samp <- expand.table(LSAT7, sample = TRUE)
head(LSAT7samp)
#>   Item.1 Item.2 Item.3 Item.4 Item.5
#> 1      1      0      0      0      0
#> 2      1      0      1      1      1
#> 3      1      1      0      0      1
#> 4      0      0      1      1      0
#> 5      1      1      0      1      1
#> 6      1      1      1      1      1
colMeans(LSAT7full)
#> Item.1 Item.2 Item.3 Item.4 Item.5 
#>  0.828  0.658  0.772  0.606  0.843 
colMeans(LSAT7samp) #equal
#> Item.1 Item.2 Item.3 Item.4 Item.5 
#>  0.828  0.658  0.772  0.606  0.843 

#--------

if (FALSE) { # \dontrun{
# Generate data from separate response pattern matrix and freq vector
# The following uses Table 2.1 from de Ayala (2009)
f <- c(691,2280,242,235,158,184,1685,1053,134,462,92,65,571,79,87,41,1682,702,
       370,63,626,412,166,52,28,15,2095,1219,500,187,40,3385)

pat <- matrix(c(
   0, 0, 0, 0, 0,
   1, 0, 0, 0, 0,
   0, 1, 0, 0, 0,
   0, 0, 1, 0, 0,
   0, 0, 0, 1, 0,
   0, 0, 0, 0, 1,
   1, 1, 0, 0, 0,
   1, 0, 1, 0, 0,
   0, 1, 1, 0, 0,
   1, 0, 0, 1, 0,
   0, 1, 0, 1, 0,
   0, 0, 1, 1, 0,
   1, 0, 0, 0, 1,
   0, 1, 0, 0, 1,
   0, 0, 1, 0, 1,
   0, 0, 0, 1, 1,
   1, 1, 1, 0, 0,
   1, 1, 0, 1, 0,
   1, 0, 1, 1, 0,
   0, 1, 1, 1, 0,
   1, 1, 0, 0, 1,
   1, 0, 1, 0, 1,
   1, 0, 0, 1, 1,
   0, 1, 1, 0, 1,
   0, 1, 0, 1, 1,
   0, 0, 1, 1, 1,
   1, 1, 1, 1, 0,
   1, 1, 1, 0, 1,
   1, 1, 0, 1, 1,
   1, 0, 1, 1, 1,
   0, 1, 1, 1, 1,
   1, 1, 1, 1, 1), ncol=5, byrow=TRUE)

colnames(pat) <- paste0('Item.', 1:5)
head(pat)

table2.1 <- expand.table(pat, freq = f)
dim(table2.1)

} # }