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


# S3 method for svyglm
  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), = 1,
  center = FALSE,
  transform.response = FALSE,
  scale.only = FALSE,
  exp = FALSE,
  vifs = getOption("summ-vifs", FALSE), = getOption("", TRUE), = getOption("", TRUE),
  model.coefs = getOption("summ-model.coefs", TRUE),
  which.cols = NULL,



A svyglm object.


If TRUE, reports standardized regression coefficients by scaling and mean-centering input data (the latter can be changed via the scale.only argument). 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? If FALSE, these are not printed. Default is TRUE.

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. Note that setting this to false does not affect whether scale mean-centers variables. Use scale.only for that.


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


If you want to scale but not center, set this to TRUE. Note that for legacy reasons, setting scale = TRUE and center = FALSE will not achieve the same effect. 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.


Toggles printing of model coefficents.


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


Among other things, arguments are passed to scale_mod() or center_mod() when center or scale is TRUE.


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_mod() and center_mod(), 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_mod() can simply perform the standardization if preferred.

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

Other summ: summ.glm(), summ.lm(), summ.merMod(), summ.rq()


Jacob Long


if (requireNamespace("survey")) {
  dstrat <- svydesign(id = ~1, strata =~ stype, weights =~ pw,
                      data = apistrat, fpc =~ fpc)
  regmodel <- svyglm(api00 ~ ell * meals, design = dstrat)

#> Observations: 200
#> Dependent Variable: api00
#> Type: Survey-weighted linear regression 
#>  = 0.66
#> Adj. R² = 0.66 
#> 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