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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 lm
  scale = FALSE,
  confint = getOption("summ-confint", FALSE),
  ci.width = getOption("summ-ci.width", 0.95),
  robust = getOption("summ-robust", FALSE),
  cluster = NULL,
  vifs = getOption("summ-vifs", FALSE),
  digits = getOption("jtools-digits", 2),
  pvals = getOption("summ-pvals", TRUE), = 1,
  center = FALSE,
  transform.response = FALSE,
  scale.only = FALSE,
  data = NULL,
  part.corr = FALSE, = getOption("", TRUE), = getOption("", TRUE),
  model.coefs = getOption("summ-model.coefs", TRUE),
  which.cols = NULL,
  vcov = NULL,



A lm 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.


If not FALSE, reports heteroskedasticity-robust standard errors instead of conventional SEs. These are also known as Huber-White standard errors. There are several options provided by sandwich::vcovHC(): "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", "HC5".

Default is FALSE.

This requires the sandwich package to compute the standard errors.


For clustered standard errors, provide the column name of the cluster variable in the input data frame (as a string). Alternately, provide a vector of clusters. Note that you must set robust to either "HC1", "HC2", or "HC3" in order to have clustered standard errors ("HC4" and "HC5" are not supported.


If TRUE, adds a column to output with variance inflation factors (VIF). Default is 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 you provide the data used to fit the model here, that data frame is used to re-fit the model (if scale is TRUE) instead of the stats::model.frame() of the model. This is particularly useful if you have variable transformations or polynomial terms specified in the formula.


Print partial (labeled "partial.r") and semipartial (labeled "part.r") correlations with the table? Default is FALSE. See details about these quantities when robust standard errors are used.

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.


You may provide your own variance-covariance matrix for the regression coefficients if you want to calculate standard errors in some way not accommodated by the robust and cluster options.


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 R-squared value plus adjusted R-squared

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

There are several options available for robust. The heavy lifting is done by sandwich::vcovHC(), where those are better described. Put simply, you may choose from "HC0" to "HC5". Based on the recommendation of the developers of sandwich, the default is set to "HC3". Stata's default is "HC1", so that choice may be better if the goal is to replicate Stata's output. Any option that is understood by vcovHC() will be accepted. Cluster-robust standard errors are computed if cluster is set to the name of the input data's cluster variable or is a vector of clusters.

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.

If using part.corr = TRUE, then you will get these two common effect size metrics on the far right two columns of the output table. However, it should be noted that these do not go hand in hand with robust standard error estimators. The standard error of the coefficient doesn't change the point estimate, just the uncertainty. However, this function uses t-statistics in its calculation of the partial and semipartial correlation. This provides what amounts to a heteroskedasticity-adjusted set of estimates, but I am unaware of any statistical publication that validates this type of use. Please use these as a heuristic when used alongside robust standard errors; do not report the "robust" partial and semipartial correlations in publications.


King, G., & Roberts, M. E. (2015). How robust standard errors expose methodological problems they do not fix, and what to do about it. Political Analysis, 23(2), 159–179. doi:10.1093/pan/mpu015

Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The Importance of the Normality Assumption in Large Public Health Data Sets. Annual Review of Public Health, 23, 151–169. doi:10.1146/annurev.publhealth.23.100901.140546

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.merMod(), summ.rq(), summ.svyglm()


Jacob Long


# Create lm object
fit <- lm(Income ~ Frost + Illiteracy + Murder,
          data =

# Print the output with standardized coefficients and 3 digits
summ(fit, scale = TRUE, digits = 3)
#> Observations: 50
#> Dependent Variable: Income
#> Type: OLS linear regression 
#> F(3,46) = 4.049, p = 0.012
#> R² = 0.209
#> Adj. R² = 0.157 
#> Standard errors: OLS
#> -------------------------------------------------------
#>                         Est.      S.E.   t val.       p
#> ----------------- ---------- --------- -------- -------
#> (Intercept)         4435.800    79.773   55.605   0.000
#> Frost                -65.188   109.686   -0.594   0.555
#> Illiteracy          -372.251   129.914   -2.865   0.006
#> Murder                85.179   114.217    0.746   0.460
#> -------------------------------------------------------
#> Continuous predictors are mean-centered and scaled by 1 s.d. The outcome variable remains in its original units.