Print a Model Summary With DMAR p-value Formatting

Pretty-print a model summary (the output of summary.lm, summary.glm, or summary on an lme4 or lmerTest fit) with p-values formatted at a fixed number of decimal places (default 4) and with a “< 10^(-digits_p)” floor for values too small to express. The default print.summary.lm / print.summary.merMod routes p-values through stats::format.pval, which applies its own digit rule and switches to scientific notation for tiny values. print_summary() sidesteps that by converting the p-value columns to character strings up front and printing as a data frame.

Usage

print_summary(fit, digits_p = 4L)

Arguments

fit

A fitted model object with a summary method that returns coefficients via coef(summary(fit)), including a Pr(...) column. Tested with lm, glm, lme4::lmer, and lmerTest::lmer.

digits_p

Integer number of decimal places for the p-value column(s). Default 4L.

Value

The model summary, invisibly and unchanged.

Details

For a linear model, the function prints the coefficient table, the residual standard error and degrees of freedom, the multiple and adjusted \(R^2\), and the omnibus F test and its p-value. For a mixed-effects model fit through lme4 / lmerTest, the function prints the random-effect variances (from lme4::VarCorr) and the fixed-effect coefficient table.

The returned object is the model summary, invisibly and unchanged: the underlying numeric p-values retain full precision and can still be indexed (for example as coef(summary(fit))[, "Pr(>|t|)"]).

See also

format_p, print_anova.

Author

Ken Kelley

Examples

fit_lm <- lm(weight ~ Time + Diet, data = ChickWeight)
print_summary(fit_lm)
#> Coefficients:
#>              Estimate Std. Error   t value Pr(>|t|)
#> (Intercept) 10.924391  3.3606567  3.250672   0.0012
#> Time         8.750492  0.2218052 39.451248 < 0.0001
#> Diet2       16.166074  4.0858416  3.956608 < 0.0001
#> Diet3       36.499407  4.0858416  8.933143 < 0.0001
#> Diet4       30.233456  4.1074850  7.360576 < 0.0001
#> 
#> Residual standard error: 35.99 on 573 degrees of freedom
#> Multiple R-squared: 0.7453,  Adjusted R-squared: 0.7435
#> F-statistic: 419.2 on 4 and 573 DF, p-value: < 0.0001

fit_lmer <- lme4::lmer(weight ~ Time + (1 | Chick), data = ChickWeight)
print_summary(fit_lmer)
#> Random effects:
#>  Groups   Name        Std.Dev.
#>  Chick    (Intercept) 26.793  
#>  Residual             28.274  
#> 
#> Fixed effects:
#>              Estimate Std. Error   t value
#> (Intercept) 27.845104  4.3876736  6.346211
#> Time         8.726062  0.1755185 49.715925

# Underlying numeric p-values are untouched:
sm <- summary(fit_lm)
sm$coefficients[, "Pr(>|t|)"]   # full-precision doubles
#>   (Intercept)          Time         Diet2         Diet3         Diet4 
#>  1.218886e-03 1.803038e-165  8.556049e-05  5.628378e-18  6.391748e-13