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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 merMod
  scale = FALSE,
  confint = getOption("summ-confint", FALSE),
  ci.width = getOption("summ-ci.width", 0.95),
  conf.method = getOption("summ-conf.method", c("Wald", "profile", "boot")),
  digits = getOption("jtools-digits", default = 2),
  r.squared = TRUE,
  pvals = getOption("summ-pvals", NULL), = 1,
  center = FALSE,
  transform.response = FALSE,
  scale.only = FALSE,
  data = NULL,
  exp = FALSE,
  t.df = NULL, = getOption("", TRUE), = getOption("", TRUE),
  model.coefs = getOption("summ-model.coefs", TRUE),
  re.variance = getOption("summ-re.variance", c("sd", "var")),
  which.cols = NULL,
  re.table = getOption("summ-re.table", TRUE),
  groups.table = getOption("summ-groups.table", TRUE),



A merMod 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.


Argument passed to lme4::confint.merMod(). Default is "Wald", but "profile" or "boot" are better when accuracy is a priority. Be aware that both of the alternate methods are sometimes very time-consuming.


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.


Calculate an r-squared model fit statistic? Default is TRUE, but if it has errors or takes a long time to calculate you may want to consider setting to FALSE.


Show p values? If FALSE, these are not printed. Default is TRUE, except for merMod objects (see details).

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.


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.


For lmerMod models only. User may set the degrees of freedom used in conducting t-tests. See details for options.

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.


Should random effects variances be expressed in standard deviations or variances? Default, to be consistent with previous versions of jtools, is "sd". Use "var" to get the variance instead.


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


Show table summarizing variance of random effects? Default is TRUE.


Show table summarizing the grouping variables? Default is TRUE.


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 secondary table with the grouping variables and random coefficients.


The tertiary table with the grouping variables, numbers of groups, and ICCs.


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/BIC.

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

merMod models are a bit different than the others. The lme4 package developers have, for instance, made a decision not to report or compute p values for lmer() models. There are good reasons for this, most notably that the t-values produced are not "accurate" in the sense of the Type I error rate. For certain large, balanced samples with many groups, this is no big deal. What's a "big" or "small" sample? How much balance is necessary? What type of random effects structure is okay? Good luck getting a statistician to give you any clear guidelines on this. Some simulation studies have been done on fewer than 100 observations, so for sure if your sample is around 100 or fewer you should not interpret the t-values. A large number of groups is also crucial for avoiding bias using t-values. If groups are nested or crossed in a linear model, it is best to just get the pbkrtest package.

By default, this function follows lme4's lead and does not report the p values for lmer() models. If the user has pbkrtest installed, however, p values are reported using the Kenward-Roger d.f. approximation unless pvals = FALSE or t.df is set to something other than NULL. In publications, you should cite the Kenward & Roger (1997) piece as well as either this package or pbkrtest package to explain how the p values were calculated.

See pvalues from the lme4 for more details. If you're looking for a simple test with no extra packages installed, it is better to use the confidence intervals and check to see if they exclude zero than use the t-test. For users of glmer(), see some of the advice there as well. While lme4 and by association summ() does as well, they are still imperfect.

You have some options to customize the output in this regard with the t.df argument. If NULL, the default, the degrees of freedom used depends on whether the user has lmerTest or pbkrtest installed. If lmerTest is installed, the degrees of freedom for each coefficient are calculated using the Satterthwaite method and the p values calculated accordingly. If only pbkrtest is installed or t.df is "k-r", the Kenward-Roger approximation of the standard errors and degrees of freedom for each coefficient is used. Note that Kenward-Roger standard errors can take longer to calculate and may cause R to crash with models fit to large (roughly greater than 5000 rows) datasets.

If neither is installed and the user sets pvals = TRUE, then the residual degrees of freedom is used. If t.df = "residual", then the residual d.f. is used without a message. If the user prefers to use some other method to determine the d.f., then any number provided as the argument will be used.

About pseudo-R^2

There is no one way to calculate R^2 for mixed models or nonlinear models. Many caution against interpreting or even using such approximations outside of OLS regression. With that said, this package reports one version for your benefit, though you should of course understand that it is not an unambiguous measure of model fit.

This package calculates R^2 for mixed models using an adapted version of rsquared() from the piecewiseSEM package. This is an implementation of the Nakagawa & Schielzeth (2013) procedure with refinements by Johnson (2014). If you choose to report the pseudo-R^2 in a publication, you should cite Nakagawa & Schielzeth to explain how the calculation was done.


Johnson, P. C. D. (2014). Extension of Nakagawa & Schielzeth's $R^2_GLMM$ to random slopes models. Methods in Ecology and Evolution, 5, 944–946. doi:10.1111/2041-210X.12225

Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983. doi:10.2307/2533558

Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82. doi:10.18637/jss.v082.i13

Luke, S. G. (2017). Evaluating significance in linear mixed-effects models in R. Behavior Research Methods, 49, 1494–1502. doi:10.3758/s13428-016-0809-y

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining $R^2$ from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4, 133–142. doi:10.1111/j.2041-210x.2012.00261.x

See also

scale_mod() can simply perform the standardization if preferred.

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

pbkrtest::get_ddf_Lb() gets the Kenward-Roger degrees of freedom if you have pbkrtest installed.

A tweaked version of piecewiseSEM::rsquared() is used to generate the pseudo-R-squared estimates for linear models.

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


Jacob Long


if (requireNamespace("lme4")) {
  library(lme4, quietly = TRUE)
  mv <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)

  summ(mv) # Note lack of p values if you don't have lmerTest/pbkrtest

  # Without lmerTest/pbkrtest, you'll get message about Type 1 errors
  summ(mv, pvals = TRUE)

  # To suppress message, manually specify t.df argument
  summ(mv, t.df = "residual")

  # Confidence intervals may be better alternative to p values
  summ(mv, confint = TRUE)
  # Use conf.method to get profile intervals (may be slow to run)
  # summ(mv, confint = TRUE, conf.method = "profile")

#> Observations: 180
#> Dependent Variable: Reaction
#> Type: Mixed effects linear regression 
#> AIC = 1755.63, BIC = 1774.79
#> Pseudo-R² (fixed effects) = 0.28
#> Pseudo-R² (total) = 0.80 
#> --------------------------------------------------------------------
#>                       Est.     2.5%    97.5%   t val.    d.f.      p
#> ----------------- -------- -------- -------- -------- ------- ------
#> (Intercept)         251.41   238.03   264.78    36.84   17.00   0.00
#> Days                 10.47     7.44    13.50     6.77   17.00   0.00
#> --------------------------------------------------------------------
#> p values calculated using Satterthwaite d.f.
#> ------------------------------------
#>   Group      Parameter    Std. Dev. 
#> ---------- ------------- -----------
#>  Subject    (Intercept)     24.74   
#>  Subject       Days         5.92    
#>  Residual                   25.59   
#> ------------------------------------
#> Grouping variables:
#> ---------------------------
#>   Group    # groups   ICC  
#> --------- ---------- ------
#>  Subject      18      0.48 
#> ---------------------------