tabglm: Generate Summary Tables of Fitted Generalized Linear Models...
In tab: Functions for Creating Summary Tables for Statistical Reports

Description Usage Arguments Details Value Note Author(s) References See Also Examples

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

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,
       greek.beta = FALSE, binary.compress = TRUE, bold.colnames = TRUE,
       bold.varnames = FALSE, bold.varlevels = FALSE,
       predictor.colname = "Variable", print.html = FALSE,
       html.filename = "table1.html")

`glmfit`	An object returned from `glm` function call.
`latex`	If `TRUE`, object returned is formatted for printing in LaTeX using `xtable` [1]; if `FALSE`, 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, generic labels are used.
`ci.beta`	If `TRUE`, the table returned will include a column for Wald 95% confidence interval for the estimated 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` function), and p-values are based on t or z statistics, depending on the GLM family; if `"profile.z"`, CI's are based on profile likelihood, and p-values are based on z statistics.
`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 = 0.1` and `p.decimals = 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) will be printed to three decimal places.
`p.lowerbound`	Controls cut-point at which p-values are no longer printed as their value, but rather <lowerbound. For example, by default `p.lowerbound = 0.001`. Under this setting, p-values less than 0.001 are printed as `<0.001`.
`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 `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.
`n`	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.
`greek.beta`	If `TRUE`, column headings refer to regression parameters as Greek letter beta rather than Beta. Only used when `latex = TRUE`.
`binary.compress`	If `TRUE`, only one row of the table is dedicated to parameter estimates for each binary factor predictor. If `FALSE`, the table displays separate rows for the variable name and the two levels for each binary factor predictor, much like the presentation for factor variables with more than two levels.
`bold.colnames`	If `TRUE`, column headings are printed in bold font. Only applies if `latex = TRUE`.
`bold.varnames`	If `TRUE`, variable names in the first column of the table are printed in bold font. Only applies if `latex = TRUE`.
`bold.varlevels`	If `TRUE`, levels of each factor variable are printed in bold font. Only applies if `latex = TRUE` and there is at least one factor variable included as a predictor.
`predictor.colname`	Character string with desired column heading for the column of predictors.
`print.html`	If `TRUE`, function prints a .html file to the current working directory.
`html.filename`	Character string indicating the name of the .html file that gets printed if `print.html = TRUE`.

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 tabgee. 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 and polynomial terms are compatible with tabglm. If interaction terms are included, the table will be formatted a little differently. Basically including an interaction is equivalent to setting basic.form = 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.

A character matrix that summarizes the fitted generalized linear model. If latex = TRUE, the character matrix will be formatted for inserting into a Markdown/Sweave/knitr report using the xtable package [1].

This function replaces the previous functions tablin and tablog, which were for linear and logistic regression, respectively. The only capability those functions had that tabglm does not currently have is the ability to calculate standardized regression coefficients. This feature may be added to tabglm in the future.

While tabglm should work with any object generated by a call to glm, not all possibilities have been tested. Therefore in general I recommend always doing a quick check that the table created by tabglm matches the information in the glm object itself.

If you wish to paste your tables into Word, you can use either of these approaches:

1. Use the write.cb function in the Kmisc package [2]. If your table is stored in a character matrix named table1, use write.cb(table1) to copy the table to your clipboard. Paste the result into Word, then highlight the text and go to Insert - Table - Convert Text to Table... OK.

2. Set print.html = TRUE. This will result in a .html file writing to your current working directory. When you open this file, you will see a nice looking table that you can copy and paste into Word. You can control the name of this file with html.filename.

If you wish to use LaTeX, R Markdown, knitr, Sweave, etc., set latex = TRUE and then use xtable [1]. You may have to set sanitize.text.function = identity when calling print.xtable.

If you have suggestions for additional options or features, or if you would like some help using any function in tab, please e-mail me at vandomed@gmail.com. Thanks!

Dane R. Van Domelen

1. Dahl DB (2013). xtable: Export tables to LaTeX or HTML. R package version 1.7-1, https://cran.r-project.org/package=xtable.

2. Kevin Ushey (2013). Kmisc: Kevin Miscellaneous. R package version 0.5.0. https://CRAN.R-project.org/package=Kmisc.

Acknowledgment: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0940903.

glm
tabfreq
tabmeans
tabmedians
tabmulti
tabcox
tabgee
tabfreq.svy
tabmeans.svy
tabmedians.svy
tabmulti.svy
tabglm.svy

# 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"))