Useful for item difficulty estimation according to Mislevy's recommendation. Also allowing for escaping rows with all missingess (typically not administered).
Usage
omitted_recoder_df(
df,
accept_vector = FALSE,
skipped = 0L,
not_administered = NA_integer_,
all_missing = NA_integer_
)Arguments
- df
Data frame, or vector. Must be a dataframe, not a matrix, in this function. Only include item variables.
- accept_vector
Handles vectors if accept_vector=TRUE. Set to false to avoid accidents when using function per block and there is just one item in the block.
- skipped
What to replace skipped values with
- not_administered
What to replace not administered values with.
- all_missing
What to replace values in rows with all missing with.
Examples
# Original data
input <- stats::setNames(as.data.frame(matrix(c(
1,0,1,0,1, # All present
NA,0,1,0,1, # First missing
NA,NA,1,0,1, # First two missing
1,0,NA,0,1, # One in middle missing
1,NA,NA,NA,1, # All in the middle missing
1,0,1,0,NA, # Last one missing
1,0,1,NA,NA, # Last two missing
1,0,NA,NA,NA, # Last three missing
NA,NA,NA,NA,NA # All missing
), nrow = 9, byrow = TRUE)), nm=stringi::stri_c(ignore_null=TRUE, "X", 1:5))
# What should be the output for item estimation according to Mislevy
# Skipped=> 0, not_administered=>NA, all_missing=>NA
y_i <- stats::setNames(as.data.frame(matrix(c(
1,0,1,0,1, # All present
0,0,1,0,1, # First missing
0,0,1,0,1, # First two missing
1,0,0,0,1, # One in middle missing
1,0,0,0,1, # All in the middle missing
1,0,1,0,0, # Last one missing
1,0,1,0,NA, # Last two missing
1,0,0,NA,NA, # Last three missing
NA,NA,NA,NA,NA # All missing
), nrow = 9, byrow = TRUE)), nm=stringi::stri_c(ignore_null=TRUE, "X", 1:5))
# What should be the output for person estimation according to Mislevy
# Skipped=> 0, not_administered=>NA, all_missing=>NA
y_p <- stats::setNames(as.data.frame(matrix(c(
1,0,1,0,1, # All present
0,0,1,0,1, # First missing
0,0,1,0,1, # First two missing
1,0,0,0,1, # One in middle missing
1,0,0,0,1, # All in the middle missing
1,0,1,0,0, # Last one missing
1,0,1,0,0, # Last two missing
1,0,0,0,0, # Last three missing
0,0,0,0,0 # All missing
), nrow = 9, byrow = TRUE)), nm=stringi::stri_c(ignore_null=TRUE, "X", 1:5))
# Recoding for counting skipped, not_administered, all_missing, etc
# Skipped=> 99, not_administered=>999, all_missing=>9999
y_info <- stats::setNames(as.data.frame(matrix(c(
1,0,1,0,1, # All present
99,0,1,0,1, # First missing
99,99,1,0,1, # First two missing
1,0,99,0,1, # One in middle missing
1,99,99,99,1, # All in the middle missing
1,0,1,0,99, # Last one missing
1,0,1,99,999, # Last two missing
1,0,99,999,999, # Last three missing
9999,9999,9999,9999,9999 # All missing
), nrow = 9, byrow = TRUE)), nm=stringi::stri_c(ignore_null=TRUE, "X", 1:5))
y_i2 <- omitted_recoder_df(input) #Mislevy item estimation
y_p2 <- omitted_recoder_df(input, skipped = 0L, #Mislevy person estimation
not_administered = 0L, all_missing = 0L)
y_info2 <- omitted_recoder_df(input, skipped = 99,
not_administered = 999, all_missing = 9999)
identical(y_i, y_i2)
#> [1] TRUE
identical(y_p, y_p2)
#> [1] TRUE
identical(y_info, y_info2)
#> [1] TRUE
if (FALSE) { # \dontrun{
omitted_recoder_df(input[,4]) # Should fail
} # }
identical(omitted_recoder_df(input[,4], accept_vector=TRUE),
c(0,0,0,0,0,0,0,NA,NA))
#> [1] TRUE
identical(omitted_recoder_df(input[,4, drop=FALSE]),
input[,4, drop=FALSE]) # Output should equal input
#> Warning: Unable to recode single-column data.frame without knowing context.
#> [1] TRUE