Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modelling function. This includes the name of the modelling function or any arguments passed to the modelling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

# S3 method for lm
glance(x, ...)

# S3 method for summary.lm
glance(x, ...)

Arguments

x

An lm object created by stats::lm().

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also

Value

A tibble::tibble() with exactly one row and columns:

adj.r.squared

Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.

AIC

Akaike's Information Criterion for the model.

BIC

Bayesian Information Criterion for the model.

deviance

Deviance of the model.

df

Degrees of freedom used by the model.

df.residual

Residual degrees of freedom for the model.

logLik

The log-likelihood of the model. [stats::logLik()] may be a useful reference.

p.value

Needs custom info.

r.squared

R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.

sigma

Estimated standard error of the residuals.

statistic

Needs custom info.

Examples

library(ggplot2) library(dplyr) mod <- lm(mpg ~ wt + qsec, data = mtcars) tidy(mod)
#> # A tibble: 3 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 19.7 5.25 3.76 7.65e- 4 #> 2 wt -5.05 0.484 -10.4 2.52e-11 #> 3 qsec 0.929 0.265 3.51 1.50e- 3
glance(mod)
#> # A tibble: 1 x 11 #> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC #> * <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> #> 1 0.826 0.814 2.60 69.0 9.39e-12 3 -74.4 157. 163. #> # ... with 2 more variables: deviance <dbl>, df.residual <int>
# coefficient plot d <- tidy(mod) %>% mutate( low = estimate - std.error, high = estimate + std.error ) ggplot(d, aes(estimate, term, xmin = low, xmax = high, height = 0)) + geom_point() + geom_vline(xintercept = 0) + geom_errorbarh()
augment(mod)
#> # A tibble: 32 x 11 #> .rownames mpg wt qsec .fitted .se.fit .resid .hat .sigma .cooksd #> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 2.62 16.5 21.8 0.683 -0.815 0.0693 2.64 2.63e-3 #> 2 Mazda RX… 21 2.88 17.0 21.0 0.547 -0.0482 0.0444 2.64 5.59e-6 #> 3 Datsun 7… 22.8 2.32 18.6 25.3 0.640 -2.53 0.0607 2.60 2.17e-2 #> 4 Hornet 4… 21.4 3.22 19.4 21.6 0.623 -0.181 0.0576 2.64 1.05e-4 #> 5 Hornet S… 18.7 3.44 17.0 18.2 0.512 0.504 0.0389 2.64 5.29e-4 #> 6 Valiant 18.1 3.46 20.2 21.1 0.803 -2.97 0.0957 2.58 5.10e-2 #> 7 Duster 3… 14.3 3.57 15.8 16.4 0.701 -2.14 0.0729 2.61 1.93e-2 #> 8 Merc 240D 24.4 3.19 20 22.2 0.730 2.17 0.0791 2.61 2.18e-2 #> 9 Merc 230 22.8 3.15 22.9 25.1 1.41 -2.32 0.295 2.59 1.59e-1 #> 10 Merc 280 19.2 3.44 18.3 19.4 0.491 -0.185 0.0358 2.64 6.55e-5 #> # ... with 22 more rows, and 1 more variable: .std.resid <dbl>
augment(mod, mtcars)
#> # A tibble: 32 x 19 #> .rownames mpg cyl disp hp drat wt qsec vs am gear carb #> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 Mazda RX… 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 Datsun 7… 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 Hornet 4… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 Hornet S… 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 Duster 3… 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # ... with 22 more rows, and 7 more variables: .fitted <dbl>, .se.fit <dbl>, #> # .resid <dbl>, .hat <dbl>, .sigma <dbl>, .cooksd <dbl>, .std.resid <dbl>
# predict on new data newdata <- mtcars %>% head(6) %>% mutate(wt = wt + 1) augment(mod, newdata = newdata)
#> # A tibble: 6 x 13 #> mpg cyl disp hp drat wt qsec vs am gear carb .fitted #> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 6 160 110 3.9 3.62 16.5 0 1 4 4 16.8 #> 2 21 6 160 110 3.9 3.88 17.0 0 1 4 4 16.0 #> 3 22.8 4 108 93 3.85 3.32 18.6 1 1 4 1 20.3 #> 4 21.4 6 258 110 3.08 4.22 19.4 1 0 3 1 16.5 #> 5 18.7 8 360 175 3.15 4.44 17.0 0 0 3 2 13.1 #> 6 18.1 6 225 105 2.76 4.46 20.2 1 0 3 1 16.0 #> # ... with 1 more variable: .se.fit <dbl>
au <- augment(mod, data = mtcars) ggplot(au, aes(.hat, .std.resid)) + geom_vline(size = 2, colour = "white", xintercept = 0) + geom_hline(size = 2, colour = "white", yintercept = 0) + geom_point() + geom_smooth(se = FALSE)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
plot(mod, which = 6)
ggplot(au, aes(.hat, .cooksd)) + geom_vline(xintercept = 0, colour = NA) + geom_abline(slope = seq(0, 3, by = 0.5), colour = "white") + geom_smooth(se = FALSE) + geom_point()
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
# column-wise models a <- matrix(rnorm(20), nrow = 10) b <- a + rnorm(length(a)) result <- lm(b ~ a) tidy(result)
#> # A tibble: 6 x 6 #> response term estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Y1 (Intercept) 0.591 0.359 1.64 0.144 #> 2 Y1 a1 0.971 0.284 3.42 0.0111 #> 3 Y1 a2 -0.0905 0.414 -0.219 0.833 #> 4 Y2 (Intercept) 0.0105 0.350 0.0299 0.977 #> 5 Y2 a1 0.00789 0.277 0.0285 0.978 #> 6 Y2 a2 1.90 0.403 4.72 0.00216