summ prints output for a regression model in a fashion similar to summary, but formatted differently with more options.

# S3 method for svyglm
summ(model, scale = FALSE,
  confint = getOption("summ-confint", FALSE),
  ci.width = getOption("summ-ci.width", 0.95),
  digits = getOption("jtools-digits", default = 2),
  pvals = getOption("summ-pvals", TRUE),
  stars = getOption("summ-stars", FALSE), = 1, center = FALSE,
  transform.response = FALSE, exp = FALSE,
  vifs = getOption("summ-vifs", FALSE), = getOption("", TRUE), = getOption("", TRUE), which.cols = NULL,



A svyglm object.


If TRUE, reports standardized regression coefficients. Default is FALSE.


Show confidence intervals instead of standard errors? Default is FALSE.


A number between 0 and 1 that signifies the width of the desired confidence interval. Default is .95, which corresponds to a 95% confidence interval. Ignored if confint = FALSE.


An integer specifying the number of digits past the decimal to report in the output. Default is 2. You can change the default number of digits for all jtools functions with options("jtools-digits" = digits) where digits is the desired number.


Show p values and significance stars? If FALSE, these are not printed. Default is TRUE.


Show significance stars with p values? Default is FALSE.

If scale = TRUE, how many standard deviations should predictors be divided by? Default is 1, though some suggest 2.


If you want coefficients for mean-centered variables but don't want to standardize, set this to TRUE.


Should scaling/centering apply to response variable? Default is FALSE.


If TRUE, reports exponentiated coefficients with confidence intervals for exponential models like logit and Poisson models. This quantity is known as an odds ratio for binary outcomes and incidence rate ratio for count models.


If TRUE, adds a column to output with variance inflation factors (VIF). Default is FALSE.

Toggles printing of basic information on sample size, name of DV, and number of predictors.

Toggles printing of model fit statistics.


Developmental feature. By providing columns by name, you can add/remove/reorder requested columns in the output. Not fully supported, for now.


This just captures extra arguments that may only work for other types of models.


If saved, users can access most of the items that are returned in the output (and without rounding).


The outputted table of variables and coefficients


The model for which statistics are displayed. This would be most useful in cases in which scale = TRUE.

Much other information can be accessed as attributes.


By default, this function will print the following items to the console:

  • The sample size

  • The name of the outcome variable

  • The (Pseudo-)R-squared value and AIC.

  • A table with regression coefficients, standard errors, t values, and p values.

The scale and center options are performed via refitting the model with scale_lm and center_lm, respectively. Each of those in turn uses gscale for the mean-centering and scaling. These functions can handle svyglm objects correctly by calling svymean and svyvar to compute means and standard deviations. Weights are not altered. The fact that the model is refit means the runtime will be similar to the original time it took to fit the model.

See also

scale_lm can simply perform the standardization if preferred.

gscale does the heavy lifting for mean-centering and scaling behind the scenes.


if (requireNamespace("survey")) { library(survey) data(api) dstrat <- svydesign(id = ~1, strata =~ stype, weights =~ pw, data = apistrat, fpc =~ fpc) regmodel <- svyglm(api00 ~ ell * meals, design = dstrat) summ(regmodel) }
#> MODEL INFO: #> Observations: 200 #> Dependent Variable: api00 #> Type: Survey-weighted linear regression #> #> MODEL FIT: #> = 0.66 #> Adj. R² = 0.65 #> #> Standard errors: Robust #> #> | | Est.| S.E.| t val.| p| #> |:-----------|------:|-----:|------:|----:| #> |(Intercept) | 836.62| 10.99| 76.10| 0.00| #> |ell | -1.69| 1.01| -1.67| 0.10| #> |meals | -3.32| 0.28| -11.99| 0.00| #> |ell:meals | 0.02| 0.01| 1.42| 0.16| #> #> Estimated dispersion parameter = 5118.85