cleanData: Clean data
In tranlm/lrecImpact: Analyzing the impact of a Low Risk Express Care program

Description Usage Arguments Details Value

cleanData will clean and organize a data frame such that it is ready to be discretized into person-time. Its primary purpose is to make sure there are no inconsistencies with the data and to impute missing values.

1 2	cleanData(data, time = NULL, locf = TRUE, impute = TRUE, baseline = NULL, ignore = NULL)

`data`	A `data.frame` object, with the ID variable in the first column.
`time`	Name or index of time variable (e.g. calendar date) for longitudinal data in long form. If `NULL`, then data is assumed to include only baseline covariates and will only impute
`locf`	If `TRUE`, all missing values will be imputed in a last observation carried forward fashion.
`impute`	If `TRUE`, all remaining missing values will be imputed using either the median (if continuous) or mode (if character or factor). This option cannot be `TRUE` if locf=FALSE.
`baseline`	A vector of baseline variable names or indicies.
`ignore`	Vector of covariates ignoring in the imputations

This function can take data with both time-varying and baseline covariates. If locf=TRUE, then missing values will be imputed in a 'last observation carried forward' approach. Furthermore, if impute=TRUE, then remaining observations that have no value to carry forward will be imputed with the median (if continuous) or mode (if character or factor). If the data includes only baseline covariates, then only the

clean returns an object of class "data.frame" with the data ordered by ID, time, and ordered column wise alphabetically.

tranlm/lrecImpact documentation built on May 31, 2019, 7:45 p.m.