| 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. |
This function is included for backward compatibility with V1.0. The function is
superseeded by with.mids.
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
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
#