mi_dstat: Cohen's d Effect Size for Missingness Indicators
In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'
Description
Usage
Arguments
Value
Examples
Description
Computes Cohen's d effect size indicating whether missingness
on a variable is related to other variables (covariates).
Usage
1
mi_dstat(dat)
Arguments
dat
Data frame
Value
A matrix. Missingness indicators refer to rows and covariates
to columns.
Examples
1
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#############################################################################
# EXAMPLE 1: d effect size for missingness indicators data.ma01
#############################################################################
data(data.ma01)
dat <- data.ma01
# compute d effect sizes
md <- miceadds::mi_dstat(dat)
round( md, 3 )
miceadds documentation built on July 7, 2018, 1 a.m.
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Description Usage Arguments Value Examples
Description
Computes Cohen's d effect size indicating whether missingness on a variable is related to other variables (covariates).
Usage
1 | mi_dstat(dat)
|
Arguments
dat |
Data frame |
Value
A matrix. Missingness indicators refer to rows and covariates to columns.
Examples
1 2 3 4 5 6 7 8 9 10 | #############################################################################
# EXAMPLE 1: d effect size for missingness indicators data.ma01
#############################################################################
data(data.ma01)
dat <- data.ma01
# compute d effect sizes
md <- miceadds::mi_dstat(dat)
round( md, 3 )
|
miceadds documentation built on July 7, 2018, 1 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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