glmSeries: Run a series of generalized linear regression analyses

In tagteam/Publish: Format Output of Various Routines in a Suitable Way for Reports and Publication

Run a series of generalized linear regression analyses

Description

Run a series of generalized linear regression analyses for a list of predictor variables and summarize the results in a table. The regression models can be adjusted for a fixed set of covariates.

Usage

glmSeries(formula, data, vars, ...)

Arguments

formula	The fixed part of the regression formula. For univariate analyses this is simply y~1 where y is the outcome variable. When the aim is to control the effect of vars in each element of the series by a fixed set of variables it is y~x1+x2 where again y is the outcome and x1 and x2 are confounders.
data	A data.frame in which we evaluate the formula.
vars	A list of variable names, the changing part of the regression formula.
...	passed to glm

Value

Matrix with regression coefficients, one for each element of vars.

Author(s)

Thomas Alexander Gerds

Examples

data(Diabetes)
Diabetes$hyper1 <- factor(1*(Diabetes$bp.1s>140))
## collect odds ratios from three univariate logistic regression analyses
uni.odds <- glmSeries(hyper1~1,vars=c("chol","hdl","location"),data=Diabetes,family=binomial)
uni.odds
## control the logistic regression analyses for age and gender
## but collect only information on the variables in `vars'.
controlled.odds <- glmSeries(hyper1~age+gender,
                             vars=c("chol","hdl","location"),
                             data=Diabetes, family=binomial)
controlled.odds

tagteam/Publish documentation built on April 13, 2025, 6:22 a.m.