mi.continuous: Elementary function: linear regression to impute a continuous...
In mi: Missing Data Imputation and Model Checking

Description Usage Arguments Details Value Author(s) References See Also Examples

Imputes univariate missing data using linear regression.

1 2	mi.continuous(formula, data = NULL, start = NULL, maxit = 100, draw.from.beta = TRUE, missing.index = NULL, ...)

`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.

see bayesglm

`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.

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

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.

mi.info, mi.method, mi

  # 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)