tabglm: Generate Summary Tables of Fitted Generalized Linear Models for Statistical Reports

Description

This function takes an object returned from the glm function and generates a clean summary table for a statistical report.

Usage

tabglm(glmfit, latex = FALSE, xlabels = NULL, ci.beta = TRUE, inference = "wald", 
       decimals = 2, p.decimals = c(2, 3), p.cuts = 0.01, p.lowerbound = 0.001, 
       p.leading0 = TRUE, p.avoid1 = FALSE, basic.form = FALSE, intercept = TRUE, 
       n = FALSE, events = FALSE)

Arguments

glmfit

An object returned from glm function call.

latex

If TRUE, object returned will be formatted for printing in LaTeX using xtable [1]; if FALSE, it will be formatted for copy-and-pasting from RStudio into a word processor.

xlabels

Optional character vector to label the x variables and their levels. If unspecified, the function uses the variable names and values themselves.

ci.beta

If TRUE, the table returned will include a column for 95% confidence interval for the regression coefficients.

inference

If "wald", CI's and p-values are based on t or z statistics, depending on the GLM family (i.e. Gaussian, Poisson, binomial, etc.); if "wald.z", CI's and p-values are based on z statistics; if "profile", CI's are based on profile likelihood (confint funct

decimals

Number of decimal places for numeric values in the table (except p-values).

p.decimals

Number of decimal places for p-values. If a vector is provided rather than a single value, number of decimal places will depend on what range the p-value lies in. See p.cuts.

p.cuts

Cut-point(s) to control number of decimal places used for p-values. For example, by default p.cuts is 0.1 and p.decimals is c(2, 3). This means that p-values in the range [0.1, 1] will be printed to two decimal places, while p-values in the range [0, 0.1)

p.lowerbound

Controls cut-point at which p-values are no longer printed as their value, but rather

p.leading0

If TRUE, p-values are printed with 0 before decimal place; if FALSE, the leading 0 is omitted.

p.avoid1

If TRUE, p-values rounded to 1 are not printed as 1, but as >0.99 (or similarly depending on values for p.decimals and p.cuts).

basic.form

If TRUE, there is no attempt to neatly format factor variables and their levels, and the table returned is very similar to what you see when you run summary(glmfit).

intercept

If FALSE, the table returned will not include a row for the intercept.

If TRUE, the table returned will include a column for sample size.

events

If TRUE, the table returned will include a column for number of events observed. Only meaningful when the outcome variable is binary.

Value

A character matrix that summarizes the fitted generalized linear model. If you click on the matrix name under "Data" in the RStudio Workspace tab, you will see a clean table that you can copy and paste into a statistical report or manuscript. If latex is set to TRUE, the character matrix will be formatted for inserting into an Sweave or Knitr report using the xtable package [1].

Details

The function should work well with categorical predictors (factors), provided they are not ordered. For ordered factors, just convert to unordered before creating the glm object to pass to tabglm. Note that you can define the levels of an unordered factor to control, which dictates which level is used as the reference group in regression models. For example, suppose a factor variable x takes values "low", "medium", and "high". If you write x = factor(x = x, levels = c("low", "medium", "high")), then you can run levels(x) to see that the levels are now arranged "low", "medium", "high". It is still a regular factor, but now if you use x as a predictor in a call to glm, "low" will be the reference group. Interaction terms are compatible with tabglm, but the table will be formatted a little differently if interaction terms are present. Basically including an interaction is equivalent to setting basic.form to TRUE. All variable names and levels will be exactly as they appear when you run summary(glmfit), where glmfit is the object returned from a call to glm.

References

1. Dahl DB (2013). xtable: Export tables to LaTeX or HTML. R package version 1.7-1, http://CRAN.R-project.org/package=xtable. Acknowledgment: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0940903.

Examples

Run this code

# Load in sample dataset d and drop rows with missing values
data(d)
d <- d[complete.cases(d), ]

# Create labels for race levels
races <- c("White", "Black", "Mexican American", "Other")

# Test whether age, sex, race, and treatment group are associated with BMI
glmfit1 <- glm(bmi ~ age + sex + race + group, data = d)
lintable <- tabglm(glmfit = glmfit1, 
                   xlabels = c("Intercept", "Age", "Male", "Race", races, "Treatment"))

# Test whether age, sex, race, and treatment group are associated with 1-year mortality
glmfit2 <- glm(death_1yr ~ age + sex + race + group, data = d, family = binomial)
logtable <- tabglm(glmfit = glmfit2, ci.beta = FALSE,
                   xlabels = c("Intercept", "Age", "Male", "Race", races, "Treatment"))

# Click on lintable and logtable in the Workspace tab of RStudio to see the tables that 
# could be copied and pasted into a report or manuscript. Alternatively, setting the
# latex input to TRUE produces tables that can be inserted into LaTeX using the xtable 
# package.

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