# genObs: Create an observed data set that includes missing data In kgoldfeld/simstudy: Simulation of Study Data

## Description

Create an observed data set that includes missing data

## Usage

 `1` ```genObs(dtName, dtMiss, idvars) ```

## Arguments

 `dtName` Name of complete data set `dtMiss` Name of missing data matrix `idvars` Index variables that cannot be missing

## Value

A data table that represents observed data, including missing data

`defMiss`, `genMiss`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```def1 <- defData(varname = "m", dist = "binary", formula = .5) def1 <- defData(def1, "u", dist = "binary", formula = .5) def1 <- defData(def1, "x1", dist="normal", formula = "20*m + 20*u", variance = 2) def1 <- defData(def1, "x2", dist="normal", formula = "20*m + 20*u", variance = 2) def1 <- defData(def1, "x3", dist="normal", formula = "20*m + 20*u", variance = 2) dtAct <- genData(1000, def1) defM <- defMiss(varname = "x1", formula = .15, logit.link = FALSE) defM <- defMiss(defM, varname = "x2", formula = ".05 + m * 0.25", logit.link = FALSE) defM <- defMiss(defM, varname = "x3", formula = ".05 + u * 0.25", logit.link = FALSE) defM <- defMiss(defM, varname = "u", formula = 1, logit.link = FALSE) # not observed defM # Generate missing data matrix missMat <- genMiss(dtAct, defM, idvars = "id") missMat # Generate observed data from actual data and missing data matrix dtObs <- genObs(dtAct, missMat, idvars = "id") dtObs ```