ValidateOutcomeDataset: Validates the schema of datasets containing outcome...
In NlsyLinks: Utilities and kinship information for research with the NLSY

Description Usage Arguments Details Value Author(s) Examples

The NlsyLinks handles a lot of the plumbing code needed to transform extracted NLSY datasets into a format that statistical routines can interpret. In some cases, a dataset of measured variables is needed, with one row per subject. This function validates the measured/outcome dataset, to ensure it posses an interpretable schema. For a specific list of the requirements, see Details below.

1	ValidateOutcomeDataset(dsOutcome, outcomeNames)

`dsOutcome`	A `data.frame` with the measured variables
`outcomeNames`	The column names of the measure variables that eventually will be used by a statistical procedure.

The dsOutcome parameter must:

Have a non-missing value.
Contain at least one row.
Contain a column called 'SubjectTag' (case sensitive).
Have the SubjectTag column containing only positive numbers.
Have the SubjectTag column where all values are unique (ie, two rows/subjects cannot have the same value).

The outcomeNames parameter must:

Have a non-missing value
Contain only column names that are present in the dsOutcome data frame.

Returns TRUE if the validation passes. Returns an error (and associated descriptive message) if it false.

Will Beasley

library(NlsyLinks) #Load the package into the current R session.
ds <- ExtraOutcomes79
outcomeNames <- c("MathStandardized", "WeightZGenderAge")
ValidateOutcomeDataset(dsOutcome=ds, outcomeNames=outcomeNames) #Returns TRUE.
outcomeNamesBad <- c("MathMisspelled", "WeightZGenderAge")
#ValidateOutcomeDataset(dsOutcome=ds, outcomeNames=outcomeNamesBad) #Throws error.