smi: Multiple imputations

In mitools: Tools for Multiple Imputation of Missing Data

Description

An imputationList object containing five imputations of data from the Victorian Adolescent Health Cohort Study.

Usage

data(smi)

Format

The underlying data are in a data frame with 1170 observations on the following 12 variables.

id

a numeric vector

wave

a numeric vector

mmetro

a numeric vector

parsmk

a numeric vector

drkfre

a factor with levels Non drinker not in last wk <3 days last wk >=3 days last wk

alcdos

a factor with levels Non drinker not in last wk av <5units/drink_day av =>5units/drink_day

alcdhi

a numeric vector

smk

a factor with levels non/ex-smoker <6 days 6/7 days

cistot

a numeric vector

mdrkfre

a numeric vector

sex

a numeric vector

drinkreg

a logical vector

Source

Carlin, JB, Li, N, Greenwood, P, Coffey, C. (2003) "Tools for analysing multiple imputed datasets" The Stata Journal 3; 3: 1-20.

Examples

data(smi)
with(smi, table(sex, drkfre))
model1<-with(smi, glm(drinkreg~wave*sex, family=binomial()))
MIcombine(model1)
summary(MIcombine(model1))

Example output

[[1]]
   drkfre
sex Non drinker not in last wk <3 days last wk >=3 days last wk
  0         282            201             105               12
  1         207            194             134               35

[[2]]
   drkfre
sex Non drinker not in last wk <3 days last wk >=3 days last wk
  0         282            195             109               14
  1         200            200             132               38

[[3]]
   drkfre
sex Non drinker not in last wk <3 days last wk >=3 days last wk
  0         278            202             109               11
  1         209            194             131               36

[[4]]
   drkfre
sex Non drinker not in last wk <3 days last wk >=3 days last wk
  0         284            188             114               14
  1         203            206             128               33

[[5]]
   drkfre
sex Non drinker not in last wk <3 days last wk >=3 days last wk
  0         288            191             109               12
  1         206            192             136               36

attr(,"call")
with(smi, table(sex, drkfre))
Multiple imputation results:
      with(smi, glm(drinkreg ~ wave * sex, family = binomial()))
      MIcombine.default(model1)
                results         se
(Intercept) -2.25974358 0.26830731
wave         0.24055250 0.06587423
sex          0.64905222 0.34919264
wave:sex    -0.03725422 0.08609199
Multiple imputation results:
      with(smi, glm(drinkreg ~ wave * sex, family = binomial()))
      MIcombine.default(model1)
                results         se      (lower     upper) missInfo
(Intercept) -2.25974358 0.26830731 -2.78584855 -1.7336386      4 %
wave         0.24055250 0.06587423  0.11092461  0.3701804     12 %
sex          0.64905222 0.34919264 -0.03537187  1.3334763      1 %
wave:sex    -0.03725422 0.08609199 -0.20623121  0.1317228      7 %

mitools documentation built on May 2, 2019, 8:52 a.m.