mi.continuous {mi}R Documentation

Elementary function: linear regression to impute a continuous variable.

Description

Imputes univariate missing data using linear regression.

Usage

mi.continuous(formula, data = NULL, start = NULL, n.iter = 100, 
    draw.from.beta = FALSE, ...)

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.
start Starting value for bayesglm.
n.iter Maximum number of iteration for bayesglm. The default is 100.
draw.from.beta Draws from posterior distribution of the betas to add randomness.
... Currently not used.

Details

see bayesglm

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 yajima@stat.columbia.edu, Yu-Sung Su yajima@stat.columbia.edu, M.Grazia Pittau grazia@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu

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

  # true data
  x<-rnorm(100,0,1) # N(0,1)
  y<-rnorm(100,(1+2*x),1.2) # y ~ 1 + 2*x + N(0,1.2)
  # create artificial missingness on y
  y[seq(1,100,10)]<-NA
  dat.xy <- data.frame(x,y)
  # imputation
  mi.continuous(y~x, data = dat.xy)

[Package mi version 0.04-6 Index]