mi (version 0.10-2)

mi.pooled: Modeling Functions for Multiply Imputed Dataset

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, \dots)
## S3 method for class 'mi.pooled':
coef(object)
## S3 method for class 'mi.pooled':
se.coef(object)
## S3 method for class '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<

Value

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

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)