findMissingPattern {bipd} | R Documentation |
Find missing data pattern in a given data
Description
Find missing data pattern in a given data i.e. whether variables are systematically missing or sporadically missing. Also calculates missing count and percentage for exploratory purposes.
Usage
findMissingPattern(
dataset = NULL,
covariates = NULL,
typeofvar = NULL,
studyname = NULL,
treatmentname = NULL,
outcomename = NULL
)
Arguments
dataset |
data which contains variables of interests |
covariates |
vector of variable names that the user is interested in finding a missing data pattern |
typeofvar |
type of covariate variables; should be a vector of these values: "continuous", "binary", or "count". Order should follow that of covariates parameter. |
studyname |
study name in the data specified |
treatmentname |
treatment name in the data specified |
outcomename |
outcome name in the data specified |
Value
missingcount |
missing number of patients for each study and covariate |
missingpercent |
missing percentage of patients for each study and covariate |
sys_missing |
a vector indicating whether each covariate is systematically missing |
spor_missing |
a vector indicating whether each covariate is sporadically missing |
sys_covariates |
a vector of systematically missing covariates |
spor_covariates |
a vector of sporadically missing covariates |
without_sys_covariates |
a vector of covariates that are not systematically missing |
covariates |
vector of variable names that the user is interested in finding a missing data pattern |
studyname |
study name in the data specified |
treatmentname |
treatment name in the data specified |
outcomename |
outcome name in the data specified |
Examples
simulated_dataset <- generate_sysmiss_ipdma_example(Nstudies = 10, Ncov = 5, sys_missing_prob = 0.3,
magnitude = 0.2, heterogeneity = 0.1)
missP <- findMissingPattern(simulated_dataset, covariates = c("x1", "x2", "x3", "x4", "x5"),
typeofvar = c("continuous", "binary", "binary", "continuous", "continuous"), studyname = "study",
treatmentname = "treat", outcomename = "y")
missP