Akaike's information criterion.
Schwarz's Bayesian information criterion.
The correlation of the fixed effects (R's cov2cor(vcov(fit)); array of rows, 1 on the diagonal).
Degrees of freedom of the log-likelihood (the fit's df).
nObs − df (R's df.resid).
Estimates (R's Estimate).
The family (R's Family: line).
The link.
The log-likelihood (the fit's).
R's messages (a rank-deficient fixed-effects matrix, a singular fit).
−2 logLik (R's -2*log(L); the fit's deviance is R's deviance(), another quantity).
The fit's nAGQ (R's title: Laplace approximation for 1, adaptive Gauss-Hermite quadrature otherwise).
Names of the fixed effects (R's row names).
The number of levels of each grouping factor (R's ngrps()), in the order of the fit's ranef.
Number of observations (R's Number of obs).
Two-sided p-values from the normal distribution, 2 pnorm(−|statistic|) (R's Pr(>|z|)), for every family: lme4
uses no t distribution for the families with a dispersion either.
Minimum, quartiles, median and maximum of the Pearson residuals divided by sigma (R's Scaled residuals).
R's sigma() (1 for binomial and poisson).
Estimates over standard errors (R's third column).
The third column's name: "z value" for binomial and poisson, "t value" for the families with a dispersion.
Standard errors (R's Std. Error, from the fit's vcov).
The variance components (R's VarCorr(), not scaled by sigma; no residual row).
R's warnings (the fallback of vcov to R_X, if the fit gave it).
Result of glmerSummary: lme4's
summary()of a glmer fit.