Generate missing data

genMiss(dtName, missDefs, idvars, repeated = FALSE, periodvar = "period")

Arguments

dtName

Name of complete data set

missDefs

Definitions of missingness

idvars

Index variables

repeated

Indicator for longitudinal data

periodvar

Name of variable that contains period

Value

Missing data matrix indexed by idvars (and period if relevant)

See also

Examples

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
#> varname formula logit.link baseline monotonic #> 1: x1 0.15 FALSE FALSE FALSE #> 2: x2 .05 + m * 0.25 FALSE FALSE FALSE #> 3: x3 .05 + u * 0.25 FALSE FALSE FALSE #> 4: u 1 FALSE FALSE FALSE
# Generate missing data matrix missMat <- genMiss(dtAct, defM, idvars = "id") missMat
#> id x1 x2 x3 u m #> 1: 1 0 0 0 1 0 #> 2: 2 0 0 0 1 0 #> 3: 3 1 0 0 1 0 #> 4: 4 0 0 0 1 0 #> 5: 5 0 0 0 1 0 #> --- #> 996: 996 0 0 1 1 0 #> 997: 997 0 0 0 1 0 #> 998: 998 0 0 0 1 0 #> 999: 999 0 0 0 1 0 #> 1000: 1000 0 0 1 1 0
# Generate observed data from actual data and missing data matrix dtObs <- genObs(dtAct, missMat, idvars = "id") dtObs
#> id m u x1 x2 x3 #> 1: 1 0 NA -2.250859 0.9351444 0.4240502 #> 2: 2 0 NA 1.254368 1.1885831 0.8909210 #> 3: 3 1 NA NA 19.7829796 20.1291439 #> 4: 4 0 NA -1.678498 -1.2700800 -1.0723500 #> 5: 5 0 NA -1.981356 1.1946704 1.1647107 #> --- #> 996: 996 1 NA 39.605641 40.0627717 NA #> 997: 997 0 NA -1.350135 1.1546430 -0.2701091 #> 998: 998 0 NA 17.800273 21.5847249 18.0452866 #> 999: 999 1 NA 19.993959 19.7405706 19.6181556 #> 1000: 1000 1 NA 41.717430 37.5900058 NA