mice (version 1.14)

glm.mids: Generelized Linear Regression on Multiply Imputed Data

Description

Performs repeated glm on a multiply imputed data set

Usage

glm.mids(formula=formula(data), family=gaussian, data=sys.parent(), weights, 
    subset, na.action, start=eta, control=glm.control(...), method="glm.fit",
    model=FALSE,x=FALSE, y=TRUE, contrasts=NULL, ...)

Arguments

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().

family

see glm

weights

subset

see glm

na.action

see glm

start

see glm

control

see glm

method

see glm

model

see glm

x

see glm

y

see glm

contrasts

see glm

...

not used.

Value

• An objects of class mira, which stands for 'multiply imputed repeated analysis'. This object contains m glm.objects, plus some descriptive information.

Details

see glm

References

Van Buuren, S. & Oudshoorn, C.G.M. (2000). Multivariate Imputation by Chained Equations: MICE V1.0 User's manual. Report PG/VGZ/00.038, TNO Prevention and Health, Leiden.

See Also

glm, mids, mira

Examples

data(nhanes)
imp <- mice(nhanes)     # do default multiple imputation on a numeric matrix
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 
    # 
    # $analyses[[3]]:
    # Call:
    # glm(formula = formula, data = data.i)
    # 
    # Coefficients:
    #  (Intercept)          bmi         chl 
    #   -0.1503616 -0.003002851 0.002130091
    # 
    # Degrees of Freedom: 25 Total; 22 Residual
    # Residual Deviance: 3.799126 
    # 
    # $analyses[[4]]:
    # Call:
    # glm(formula = formula, data = data.i)
    # 
    # Coefficients:
    #  (Intercept)        bmi         chl 
    #  0.009442083 -0.0237619 0.004631881
    # 
    # Degrees of Freedom: 25 Total; 22 Residual
    # Residual Deviance: 3.874522 
    # 
    # $analyses[[5]]:
    # Call:
    # glm(formula = formula, data = data.i)
    # 
    # Coefficients:
    #  (Intercept)         bmi         chl 
    #   0.09932161 -0.02168292 0.003857599
    # 
    # Degrees of Freedom: 25 Total; 22 Residual
    # Residual Deviance: 4.025066 
    # 
    # 
    # > 
    #