Generalized linear model for mids object

Applies glm() to a multiply imputed data set

glm.mids(formula, family = gaussian, data, ...)

Arguments

formula: a formula expression as for other regression models, of the form response ~ predictors. See the documentation of lm and formula for details.
family: The family of the glm model
data: An object of type mids, which stands for 'multiply imputed data set', typically created by function mice().
...: Additional parameters passed to glm.

Value

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

Details

This function is included for backward compatibility with V1.0. The function is superseded by with.mids.

References

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.

Author

Stef van Buuren, Karin Groothuis-Oudshoorn, 2000

Examples


imp <- mice(nhanes)
#> 
#>  iter imp variable
#>   1   1  bmi  hyp  chl
#>   1   2  bmi  hyp  chl
#>   1   3  bmi  hyp  chl
#>   1   4  bmi  hyp  chl
#>   1   5  bmi  hyp  chl
#>   2   1  bmi  hyp  chl
#>   2   2  bmi  hyp  chl
#>   2   3  bmi  hyp  chl
#>   2   4  bmi  hyp  chl
#>   2   5  bmi  hyp  chl
#>   3   1  bmi  hyp  chl
#>   3   2  bmi  hyp  chl
#>   3   3  bmi  hyp  chl
#>   3   4  bmi  hyp  chl
#>   3   5  bmi  hyp  chl
#>   4   1  bmi  hyp  chl
#>   4   2  bmi  hyp  chl
#>   4   3  bmi  hyp  chl
#>   4   4  bmi  hyp  chl
#>   4   5  bmi  hyp  chl
#>   5   1  bmi  hyp  chl
#>   5   2  bmi  hyp  chl
#>   5   3  bmi  hyp  chl
#>   5   4  bmi  hyp  chl
#>   5   5  bmi  hyp  chl

# logistic regression on the imputed data
fit <- glm.mids((hyp == 2) ~ bmi + chl, data = imp, family = binomial)
#> Warning: Use with(imp, glm(yourmodel)).
fit
#> call :
#> glm.mids(formula = (hyp == 2) ~ bmi + chl, family = binomial, 
#>     data = imp)
#> 
#> call1 :
#> mice(data = nhanes)
#> 
#> nmis :
#> age bmi hyp chl 
#>   0   9   8  10 
#> 
#> analyses :
#> [[1]]
#> 
#> Call:  glm(formula = formula, family = family, data = complete(data, 
#>     i))
#> 
#> Coefficients:
#> (Intercept)          bmi          chl  
#>    -4.98053      0.02486      0.01474  
#> 
#> Degrees of Freedom: 24 Total (i.e. Null);  22 Residual
#> Null Deviance:	    25.02 
#> Residual Deviance: 23.12 	AIC: 29.12
#> 
#> [[2]]
#> 
#> Call:  glm(formula = formula, family = family, data = complete(data, 
#>     i))
#> 
#> Coefficients:
#> (Intercept)          bmi          chl  
#>    -7.51505      0.02664      0.02666  
#> 
#> Degrees of Freedom: 24 Total (i.e. Null);  22 Residual
#> Null Deviance:	    25.02 
#> Residual Deviance: 22.03 	AIC: 28.03
#> 
#> [[3]]
#> 
#> Call:  glm(formula = formula, family = family, data = complete(data, 
#>     i))
#> 
#> Coefficients:
#> (Intercept)          bmi          chl  
#>    -8.29196      0.10502      0.01992  
#> 
#> Degrees of Freedom: 24 Total (i.e. Null);  22 Residual
#> Null Deviance:	    25.02 
#> Residual Deviance: 21.99 	AIC: 27.99
#> 
#> [[4]]
#> 
#> Call:  glm(formula = formula, family = family, data = complete(data, 
#>     i))
#> 
#> Coefficients:
#> (Intercept)          bmi          chl  
#>    -7.09719      0.01271      0.02846  
#> 
#> Degrees of Freedom: 24 Total (i.e. Null);  22 Residual
#> Null Deviance:	    29.65 
#> Residual Deviance: 25.16 	AIC: 31.16
#> 
#> [[5]]
#> 
#> Call:  glm(formula = formula, family = family, data = complete(data, 
#>     i))
#> 
#> Coefficients:
#> (Intercept)          bmi          chl  
#>    -2.55325     -0.10346      0.02218  
#> 
#> Degrees of Freedom: 24 Total (i.e. Null);  22 Residual
#> Null Deviance:	    29.65 
#> Residual Deviance: 26.23 	AIC: 32.23
#> 
#>

Generalized linear model for `mids` object