mi.pooled {mi}R Documentation

Modeling Functions for Multiply Imputed Dataset

Description

Modeling Function that pulls together the estimates from multiply imputed dataset.

Usage

mi.pooled(coef, se, m)
lm.mi(formula, mi.object, ...)
glm.mi(formula, mi.object, family = gaussian, ...)
bayesglm.mi(formula, mi.object, family = gaussian, ...)
polr.mi(formula, mi.object, ...)
bayespolr.mi(formula, mi.object, ...)
lmer.mi(formula, mi.object, rescale=FALSE, ...)
glmer.mi(formula, mi.object, family = gaussian, rescale=FALSE, ...)
## S3 method for class 'mi.pooled':
print(x, ...)
## S4 method for signature 'mi.pooled':
coef(object)
## S4 method for signature 'mi.pooled':
se.coef(object)
## S4 method for signature 'mi.pooled':
display(object, digits=2)

Arguments

coef list of coefficients
se list of standard errors
m number of chains for the imputation
formula See lm, glm, polr, lmerfor detail.
mi.object mi object
family See glm, polr, lmerfor detail.
rescale default is FALSE, see rescale for detail.
x mi.pooled object.
object mi.pooled object.
digits number of significant digits to display, default=2.
... Any option to pass on to lm, glm, bayesglm, bayespolr, polr, and lmer functions

Value

call the matched call.
mi.pooled pulled estimates from the multiple dataset.
mi.fit estimates from each dataset.

Author(s)

Yu-Sung Su ys463@columbia.edu,

References

Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.

See Also

lm, glm, bayesglm, bayespolr, polr, and lmer

Examples

  # true data
  n <- 100
  x <- rbinom(n,1,.45) 
  z <- ordered(rep(seq(1, 5),n)[sample(1:n, n)])
  y <- rnorm(n)
  group <- rep(1:10, 10)

  # create artificial missingness
  dat.xy <- data.frame(x, z, y)
  dat.xy <- mi:::.create.missing(dat.xy, pct.mis=10)  

  # imputation 
  dat.mi <- mi(dat.xy, n.iter=6, preprocess=FALSE, add.noise=FALSE)

  # fit models
  M1 <- lm.mi(y ~ x + z, dat.mi)
  display(M1)
  coef(M1)
  se.coef(M1)

  M2 <- glm.mi(x ~ y , dat.mi, family = binomial(link="logit"))
  display(M2)
  coef(M2)
  se.coef(M2)

  M3 <- bayesglm.mi(x ~ y , dat.mi, family = binomial(link="logit"))
  display(M3)
  coef(M3)
  se.coef(M3)

  M4 <- polr.mi(ordered(z) ~  y, dat.mi)
  display(M4)
  coef(M4)
  se.coef(M4)

  M5 <- bayespolr.mi(ordered(z) ~  y, dat.mi)
  display(M5)
  coef(M5)
  se.coef(M5)

  M6 <- lmer.mi(y ~ x  + (1|group), dat.mi)
  display(M6)
  coef(M6)
  se.coef(M6)

  M7 <- glmer.mi(x ~ y  + (1|group), dat.mi, family = binomial(link="logit"))
  display(M7)
  coef(M7)
  se.coef(M7)  

[Package mi version 0.08-03 Index]