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 modeling function. This includes the name of the modeling function or any arguments passed to the modeling 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.

Glance returns the same number of columns regardless of whether the model matrix is rank-deficient or not. If so, entries in columns that no longer have a well-defined value are filled in with an NA of the appropriate type.

# 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.

Details

The glance.summary.lm() method is a potentially useful alternative to glance.lm(). For instance, if users have already converted large lm objects into their leaner summary.lm equivalents to conserve memory. Note, however, that this method does not return all of the columns of the non-summary method (e.g. AIC and BIC will be missing.)

See also

glance(), glance.summary.lm()

Other lm tidiers: augment.glm(), augment.lm(), glance.glm(), glance.lm(), glance.svyglm(), tidy.glm(), tidy.lm.beta(), tidy.lm(), tidy.mlm(), tidy.summary.lm()

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.

df.residual

Residual degrees of freedom.

nobs

Number of observations used.

p.value

P-value corresponding to the test statistic.

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

Test statistic.

df

The degrees for freedom from the numerator of the overall F-statistic. This is new in broom 0.7.0. Previously, this reported the rank of the design matrix, which is one more than the numerator degrees of freedom of the overall F-statistic.

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 12 #> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 0.826 0.814 2.60 69.0 9.39e-12 2 -74.4 157. 163. #> # … with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>
# coefficient plot d <- tidy(mod, conf.int = TRUE) ggplot(d, aes(estimate, term, xmin = conf.low, xmax = conf.high, height = 0)) + geom_point() + geom_vline(xintercept = 0, lty = 4) + geom_errorbarh()
# Aside: There are tidy() and glance() methods for lm.summary objects too. # This can be useful when you want to conserve memory by converting large lm # objects into their leaner summary.lm equivalents. s <- summary(mod) tidy(s, conf.int = TRUE)
#> # A tibble: 3 x 7 #> term estimate std.error statistic p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 19.7 5.25 3.76 7.65e- 4 9.00 30.5 #> 2 wt -5.05 0.484 -10.4 2.52e-11 -6.04 -4.06 #> 3 qsec 0.929 0.265 3.51 1.50e- 3 0.387 1.47
glance(s)
#> # A tibble: 1 x 8 #> r.squared adj.r.squared sigma statistic p.value df df.residual nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> #> 1 0.826 0.814 2.60 69.0 9.39e-12 2 29 32
augment(mod)
#> # A tibble: 32 x 10 #> .rownames mpg wt qsec .fitted .resid .hat .sigma .cooksd .std.resid #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 2.62 16.5 21.8 -0.815 0.0693 2.64 2.63e-3 -0.325 #> 2 Mazda RX4… 21 2.88 17.0 21.0 -0.0482 0.0444 2.64 5.59e-6 -0.0190 #> 3 Datsun 710 22.8 2.32 18.6 25.3 -2.53 0.0607 2.60 2.17e-2 -1.00 #> 4 Hornet 4 … 21.4 3.22 19.4 21.6 -0.181 0.0576 2.64 1.05e-4 -0.0716 #> 5 Hornet Sp… 18.7 3.44 17.0 18.2 0.504 0.0389 2.64 5.29e-4 0.198 #> 6 Valiant 18.1 3.46 20.2 21.1 -2.97 0.0957 2.58 5.10e-2 -1.20 #> 7 Duster 360 14.3 3.57 15.8 16.4 -2.14 0.0729 2.61 1.93e-2 -0.857 #> 8 Merc 240D 24.4 3.19 20 22.2 2.17 0.0791 2.61 2.18e-2 0.872 #> 9 Merc 230 22.8 3.15 22.9 25.1 -2.32 0.295 2.59 1.59e-1 -1.07 #> 10 Merc 280 19.2 3.44 18.3 19.4 -0.185 0.0358 2.64 6.55e-5 -0.0728 #> # … with 22 more rows
augment(mod, mtcars, interval = "confidence")
#> # A tibble: 32 x 20 #> .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 RX4 … 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 Hornet 4 D… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 Hornet Spo… 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 360 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 8 more variables: .fitted <dbl>, .lower <dbl>, #> # .upper <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 14 #> .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 3.62 16.5 0 1 4 4 #> 2 Mazda RX4 W… 21 6 160 110 3.9 3.88 17.0 0 1 4 4 #> 3 Datsun 710 22.8 4 108 93 3.85 3.32 18.6 1 1 4 1 #> 4 Hornet 4 Dr… 21.4 6 258 110 3.08 4.22 19.4 1 0 3 1 #> 5 Hornet Spor… 18.7 8 360 175 3.15 4.44 17.0 0 0 3 2 #> 6 Valiant 18.1 6 225 105 2.76 4.46 20.2 1 0 3 1 #> # … with 2 more variables: .fitted <dbl>, .resid <dbl>
# ggplot2 example where we also construct 95% prediction interval mod2 <- lm(mpg ~ wt, data = mtcars) ## simpler bivariate model since we're plotting in 2D au <- augment(mod2, newdata = newdata, interval = "prediction") ggplot(au, aes(wt, mpg)) + geom_point() + geom_line(aes(y = .fitted)) + geom_ribbon(aes(ymin = .lower, ymax = .upper), col = NA, alpha = 0.3)
# predict on new data without outcome variable. Output does not include .resid newdata <- newdata %>% select(-mpg)
#> Error in select(., -mpg): unused argument (-mpg)
augment(mod, newdata = newdata)
#> # A tibble: 6 x 14 #> .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 3.62 16.5 0 1 4 4 #> 2 Mazda RX4 W… 21 6 160 110 3.9 3.88 17.0 0 1 4 4 #> 3 Datsun 710 22.8 4 108 93 3.85 3.32 18.6 1 1 4 1 #> 4 Hornet 4 Dr… 21.4 6 258 110 3.08 4.22 19.4 1 0 3 1 #> 5 Hornet Spor… 18.7 8 360 175 3.15 4.44 17.0 0 0 3 2 #> 6 Valiant 18.1 6 225 105 2.76 4.46 20.2 1 0 3 1 #> # … with 2 more variables: .fitted <dbl>, .resid <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) 0.116 0.263 0.440 0.673 #> 2 Y1 a1 1.06 0.272 3.90 0.00593 #> 3 Y1 a2 -0.205 0.284 -0.720 0.495 #> 4 Y2 (Intercept) -0.0740 0.221 -0.335 0.747 #> 5 Y2 a1 -0.0375 0.229 -0.164 0.874 #> 6 Y2 a2 0.620 0.239 2.60 0.0355