rqlm (version 2.3-1)

mi_glm: Multiple imputation analysis for the generalized linear model

Description

Multiple imputation analysis for the generalized linear model is performed for the imputed datasets generated by mice function in mice package. For computing covariance matrix estimate, the ordinary Rubin's rule is adapted to the model variance estimates.

Usage

mi_glm(ice, formula, family=gaussian, offset=NULL, eform=FALSE, cl=0.95, digits=4)

Value

Results of the multiple imputation analysis for the generalized linear model. For computing covariance matrix estimate, the ordinary Rubin's rule is adapted to the model variance estimates.

• coef: Coefficient estimates; transformed to the exponential scale if eform=TRUE.

• SE: Standard error estimates for coef.

• CL: Lower limits of confidence intervals.

• CU: Upper limits of confidence intervals.

• df: Degree of freedom for the t-approximation.

• P-value: P-values for the coefficient tests.

Arguments

ice

An output object of mice function in mice package.

formula

An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.

family

A description of the error distribution and link function to be used in the model.

offset

A vector of offset. This can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases.

eform

A logical value that specify whether the outcome should be transformed by exponential function (default: FALSE)

cl

Confidence level for calculating confidence intervals (default: 0.95)

digits

Number of decimal places in the output (default: 4).

References

Little, R. J., and Rubin, D. B. (2019). Statistical Analysis with Missing Data, 3rd edition. New York: Wiley.

Examples

library("mice")

data(exdata03)

exdata03$x2 <- factor(exdata03$x2)
exdata03$x3 <- factor(exdata03$x3)
exdata03$x4 <- factor(exdata03$x4)

ice5 <- mice(exdata03,m=5)
# For illustration. m should be >=100.

mi_glm(ice5, y ~ x1 + x2 + x3 + x4, family=binomial, eform=TRUE)
# Logistic regression analysis
# Coefficient estimates are translated to odds ratio scales

mi_glm(ice5, x1 ~ x2 + x3 + x4, family=gaussian)
# Ordinary least-squares regression analysis with the model variance estimator