mi.fixed: Elementary function: imputation of constant variable.

In mi: Missing Data Imputation and Model Checking

Description

Imputes univariate constant missing data.

Usage

 mi.fixed( formula, data = NULL, missing.index = NULL, ... )
 mi.copy(Y, X, missing.index = NULL, ...)

Arguments

formula	an object of class '"formula"' (or one that can be coerced to that class): a symbolic description of the model to be fitted. See bayesglm 'formula' for details.
data	A data frame containing the incomplete data and the matrix of the complete predictors.
missing.index	The index of missing units of the outcome variable
Y	A variable that is collinear with X

.

X	A variable that is colliear with Y

.

...	Currently not used

Value

model	A summary of the fitted model.
expected	The expected values estimated by the model.
random	Vector of length n.mis of random predicted values predicted by using the normal distribution.

Author(s)

Masanao Yajima [e-mail], M.Grazia Pittau [e-mail], Andrew Gelman [e-mail]

References

Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2006.

See Also

mi.info, mi.method, mi

Examples

# fake data
n <- 100
x1 <- rbinom(n, 1, .45)
x2 <- 2*x1
x1[c(1, 3, 5, 20, 26)] <- NA

# impute data
mi.copy(x1, x2)

mi documentation built on May 2, 2019, 4:43 p.m.