Elementary function: linear regression to impute a continuous variable.

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Description

Imputes univariate missing data using linear regression.

Usage

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mi.continuous(formula, data = NULL, start = NULL, maxit = 100,
    draw.from.beta = TRUE, 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.

start

Starting value for bayesglm.

maxit

Maximum number of iteration for bayesglm. The default is 100.

draw.from.beta

Draws from posterior distribution of the betas to add randomness.

missing.index

The index of missing units of the outcome variable

...

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 suyusung@tsinghua.edu.cn, M.Grazia Pittau grazia@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu

References

Yu-Sung Su, Andrew Gelman, Jennifer Hill, Masanao Yajima. (2011). “Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box”. Journal of Statistical Software 45(2).

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

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  # 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)

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