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Run analysis on TOCS raw values to return t-scores

Source: R/tocs.R
score_tocs2.Rd

score_tocs2() returns gendered and non-gendered t-scores for the Toronto Obsessive-Compulsive Scale (TOCS) assessment

[Experimental]

Usage

score_tocs2(
  df = NULL,
  file = FALSE,
  output_folder = NULL,
  max_missing = 0,
  ignore_check = FALSE
)

Arguments

df

If you already have the TOCS-2 data in your R environment, pass the dataframe to this parameter

file

If you prefer scoring a spreadsheet...

  1. Change to TRUE to pop-up a finder to allow you select a file. Alternatively, leave df and file empty to pop-up a finder.

  2. Or specify a pathway

output_folder

Optional, output file pathway. Defauts to NULL. Specify a pathway to output a csv file.

max_missing

By default, 0 items are allowed to be missing on the TOCS. Any questionnaire with 1 or more missing, will not be scored. If you'd like to adjust this number, change the max_missing value. This will use a prorated score to generate t-scores. Please be aware that missingness can induce issues when analyzing.

ignore_check

Data are validated to look for missing or improperly formatted values before scoring. Errors are thrown when data aren't valid; however, this can cause issues in real data sets where data vary for good reasons. To skip the validation process, set ignore_check to TRUE. NAs will be returned where data are invalid

Value

table with t-scores attached to raw swan values

Examples

# Read in the file of scores
# This is an example file
csv <- system.file("extdata", "sample_tocs.csv", package = "sfsScorer")

# Score via the file parameter
scores_csv <- score_tocs2(file = csv)
#> ✔ The model scored 5 observations.

# Score via the df paramter
df <- rio::import(csv)
scores_csv <- score_tocs2(df = df)
#> ✔ The model scored 5 observations.

# The data are automatically validated.
# To ignore the validation errors and introduce `NA`, set `ignore_check = TRUE`
df_mod <- df |>
  dplyr::mutate(p_respondent = 2)
scores_csv <- score_tocs2(df = df_mod, ignore_check = TRUE)
#> ! 5 non-valid p_respondent values were changed to NA. This could impact scores. 
#> The only valid p_respondent values are 1 and 0. To correct, review the following rows before re-running - 1, 2, 3, 4, and 5
#> ✔ The model scored 0 observations.