mi.logcontinuous {mi} | R Documentation |
Imputes univariate missing data using linear regression on the logarithm values.
mi.logcontinuous( formula, data = NULL, start = NULL, n.iter = 100, draw.from.beta = FALSE, ... ) ## S3 method for class 'mi.logcontinuous': residuals(object, y, ...) ## S3 method for class 'mi.logcontinuous': plot( x, y, main=deparse( substitute( y ) ), gray.scale = FALSE, ...)
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. |
object |
mi.logcontinuous object. |
x |
mi.logcontinuous object. |
y |
Observed values. |
main |
main title of the plot. |
gray.scale |
When set to TRUE, makes the plot into gray scale with predefined color and line type. |
see lm
.
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 |
The incomplete data vector has to be positive.
Masanao Yajima yajima@stat.columbia.edu, M.Grazia Pittau grazia@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu
Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
# true data x<-rnorm(100,0,1) # N(0,1) y<-rnorm(100,(4+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.logcontinuous(y~x, data = dat.xy)