exceptions: Apply Exceptions to a Dataframe

In mattsigal/mmisc: Matt's Miscellaneous Functions for MHS

Description

exceptions is a dual purpose function that returns a full dataframe that has either applied exceptions based upon an overall normative sample or exceptions based upon age and gender specific norms (based upon the norm argument). Since exceptions are applied based upon raw score values, the function requires both dataframes. Exceptions are also passed to the function as a dataframe. Age and Gender are included as arguments simply to designate the names of the respective variables.

Usage

exceptions(dat, normedDat, exceptions.list = NULL, norm = "overall",
  AGE = NULL, GENDER = NULL, maxVal = 132)

Arguments

dat	An R dataframe object pertaining to the raw data.
normedDat	An R dataframe object pertaining to the normed dataset.
exceptions.list	An R dataframe object pertaining to the list of exceptions to be applied.
norm	A character scalar, either "overall" or "agegender", depending if scoring should be done based upon overall values or age/gender specific norms.
maxVal	A scalar indicating the value at which to "cap" scales. All values greater than maxVal are set to maxVal.
Age	A character vector of length 1, used to indicate the name of the Age variable in dat.
Gender	A character vector of length 1, used to indicate the name of the Gender variable in dat.

Details

This function requires an external R dataframe to be passed to exceptions.list that has the following structure: columns called SCALE (with scale names that match those in 'dat'), AGE, GENDER, RAW, and SS. Raw values pertain to problematic values in terms of raw score, SS contains the values that should replace them in the standard score rubric. For the rows pertaining to overall norms, the AGE and GENDER values should be the character string "overall". See the example code for more details.

Value

An R dataframe object with N rows and K columns, where K is the number of scales included in dat.

See Also

scoring

Examples

## Not run: 
set.seed(77)
exceptions.list <- data.frame(SCALE = rep(LETTERS[1:5], 5),
                              AGE = c(rep("overall", 5),
                                    rep(c("Young", "Old"), each = 5),
                                    rep(c("Young", "Old"), each = 5)),
                              GENDER = c(rep("overall", 5), rep(c("Male", "Female"), each = 10)),
                              RAW = rnorm(25, mean = 100, sd = 10),
                              SS = rnorm(25, mean = 10, sd = 3),
                            stringsAsFactors = FALSE)
set.seed(78)
n <- 500
dat <- data.frame(AGE = sample(c("Young","Old"), size = n, replace = TRUE),
                  GENDER = sample(c("Male","Female"), size = n, replace = TRUE),
                  A = rnorm(n, mean = 100, sd = 10),
                  B = rnorm(n, mean = 100, sd = 10),
                  C = rnorm(n, mean = 100, sd = 10),
                  D = rnorm(n, mean = 100, sd = 10),
                  E = rnorm(n, mean = 100, sd = 10))

overall <- exceptions(dat[,3:7], overall, exceptions.list[1:5,], norm = "overall")
agegen <- exceptions(dat[,3:7], agegen, exceptions.list[6:25,], norm = "agegender",
                  Age = dat[,1], Gender = dat[,2])

## End(Not run)

mattsigal/mmisc documentation built on May 21, 2019, 1:26 p.m.