mi.pooled: Modeling Functions for Multiply Imputed Dataset

In mi: Missing Data Imputation and Model Checking

Description

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

Usage

mi.pooled(coef, se)
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

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 [e-mail],

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, y, z)
  dat.xy <- mi:::.create.missing(dat.xy, pct.mis=10)  
  
  # imputation 
  IMP <- mi(dat.xy, n.iter=6, add.noise=FALSE)

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

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

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

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

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

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

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

mi documentation built on May 2, 2019, 4:43 p.m.