| glm.mids {mice} | R Documentation |
Applies glm() to a multiply imputed data set
glm.mids(formula, data, ...)
formula |
a formula expression as for other regression models, of the form
response ~ predictors. See the documentation
of lm and formula for details. |
data |
An object of type mids, which stands for 'multiply imputed data set', typically
created by function mice(). |
... |
Additional parameters passed to glm. |
see glm
An objects of class mira, which stands for 'multiply imputed repeated analysis'.
This object contains data$m distinct glm.objects, plus some descriptive information.
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000
Van Buuren, S., Groothuis-Oudshoorn, C.G.M. (2000) Multivariate Imputation by Chained Equations: MICE V1.0 User's manual. Leiden: TNO Quality of Life. http://www.stefvanbuuren.nl/publications/MICE V1.0 Manual TNO00038 2000.pdf
data(nhanes)
imp <- mice(nhanes)
glm.mids((hyp==2)~bmi+chl, data=imp)
# fit
# $call:
# glm.mids(formula = (hyp == 2) ~ bmi + chl, data = imp)
#
# $call1:
# mice(data = nhanes)
#
# $nmis:
# age bmi hyp chl
# 0 9 8 10
#
# $analyses:
# $analyses[[1]]:
# Call:
# glm(formula = formula, data = data.i)
#
# Coefficients:
# (Intercept) bmi chl
# -0.4746337 -0.01565534 0.005417846
#
# Degrees of Freedom: 25 Total; 22 Residual
# Residual Deviance: 2.323886
#
# $analyses[[2]]:
# Call:
# glm(formula = formula, data = data.i)
#
# Coefficients:
# (Intercept) bmi chl
# -0.1184695 -0.02885779 0.006090282
#
# Degrees of Freedom: 25 Total; 22 Residual
# Residual Deviance: 3.647927
#