data.allison: Datasets from Allison's _Missing Data_ Book

Description Usage Format Source References Examples

Description

Datasets from Allison's missing data book (Allison 2002).

Usage

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Format

  • Data data.allison.gssexp:

    'data.frame': 2991 obs. of 14 variables:
    $ AGE : num 33 59 NA 59 21 22 40 25 41 45 ...
    $ EDUC : num 12 12 12 8 13 15 9 12 12 12 ...
    $ FEMALE : num 1 0 1 0 1 1 1 0 1 1 ...
    $ SPANKING: num 1 1 2 2 NA 1 3 1 1 NA ...
    $ INCOM : num 11.2 NA 16.2 18.8 13.8 ...
    $ NOCHILD : num 0 0 0 0 1 1 0 0 0 0 ...
    $ NODOUBT : num NA NA NA 1 NA NA 1 NA NA 1 ...
    $ NEVMAR : num 0 0 0 0 1 1 0 1 0 0 ...
    $ DIVSEP : num 1 0 0 0 0 0 0 0 0 1 ...
    $ WIDOW : num 0 0 0 0 0 0 1 0 1 0 ...
    $ BLACK : num 1 1 1 0 1 1 0 1 1 1 ...
    $ EAST : num 1 1 1 1 1 1 1 1 1 1 ...
    $ MIDWEST : num 0 0 0 0 0 0 0 0 0 0 ...
    $ SOUTH : num 0 0 0 0 0 0 0 0 0 0 ...

  • Data data.allison.hip:

    'data.frame': 880 obs. of 7 variables:
    $ SID : num 1 1 1 1 2 2 2 2 9 9 ...
    $ WAVE: num 1 2 3 4 1 2 3 4 1 2 ...
    $ ADL : num 3 2 3 3 3 1 2 1 3 3 ...
    $ PAIN: num 0 5 0 0 0 1 5 NA 0 NA ...
    $ SRH : num 2 4 2 2 4 1 1 2 2 3 ...
    $ WALK: num 1 0 0 0 0 0 0 0 1 NA ...
    $ CESD: num 9 28 31 11.6 NA ...

  • Data data.allison.usnews:

    'data.frame': 1302 obs. of 7 variables:
    $ CSAT : num 972 961 NA 881 NA ...
    $ ACT : num 20 22 NA 20 17 20 21 NA 24 26 ...
    $ STUFAC : num 11.9 10 9.5 13.7 14.3 32.8 18.9 18.7 16.7 14 ...
    $ GRADRAT: num 15 NA 39 NA 40 55 51 15 69 72 ...
    $ RMBRD : num 4.12 3.59 4.76 5.12 2.55 ...
    $ PRIVATE: num 1 0 0 0 0 1 0 0 0 1 ...
    $ LENROLL: num 4.01 6.83 4.49 7.06 6.89 ...

Source

The datasets are available from http://www.ats.ucla.edu/stat/examples/md/.

References

Allison, P. D. (2002). Missing data. Newbury Park, CA: Sage.

Examples

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## Not run: 
#############################################################################
# EXAMPLE 1: Hip dataset | Imputation using a wide format
#############################################################################

# at first, the hip dataset is 'melted' for imputation

data(data.allison.hip)
  ##   head(data.allison.hip)
  ##     SID WAVE ADL PAIN SRH WALK   CESD
  ##   1   1    1   3    0   2    1  9.000
  ##   2   1    2   2    5   4    0 28.000
  ##   3   1    3   3    0   2    0 31.000
  ##   4   1    4   3    0   2    0 11.579
  ##   5   2    1   3    0   4    0     NA
  ##   6   2    2   1    1   1    0  2.222

library(reshape)
hip.wide <- reshape::reshape(data.allison.hip, idvar = "SID", timevar = "WAVE", 
                direction = "wide")
  ##   > head(hip.wide , 2)
  ##     SID ADL.1 PAIN.1 SRH.1 WALK.1 CESD.1 ADL.2 PAIN.2 SRH.2 WALK.2 CESD.2 ADL.3
  ##   1   1     3      0     2      1      9     2      5     4      0 28.000     3
  ##   5   2     3      0     4      0     NA     1      1     1      0  2.222     2
  ##     PAIN.3 SRH.3 WALK.3 CESD.3 ADL.4 PAIN.4 SRH.4 WALK.4 CESD.4
  ##   1      0     2      0     31     3      0     2      0 11.579
  ##   5      5     1      0     12     1     NA     2      0     NA

# imputation of the hip wide dataset
imp <- mice::mice( as.matrix( hip.wide[,-1] ) , m=5 , maxit=3 )
summary(imp)

## End(Not run)


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