Glance at a(n) lm object

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.

Usage

# S3 method for 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. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

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.residual: Residual degrees of freedom.
logLik: The log-likelihood of the model. [stats::logLik()] may be a useful reference.
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 × 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 × 12
#>   r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC
#>       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>  <dbl> <dbl>
#> 1     0.826         0.814  2.60      69.0 9.39e-12     2  -74.4  157.
#> # ℹ 4 more variables: BIC <dbl>, 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 × 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 × 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 × 10
#>    .rownames         mpg    wt  qsec .fitted  .resid   .hat .sigma .cooksd
#>    <chr>           <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
#>  2 Mazda RX4 Wag    21    2.88  17.0    21.0 -0.0482 0.0444   2.64 5.59e-6
#>  3 Datsun 710       22.8  2.32  18.6    25.3 -2.53   0.0607   2.60 2.17e-2
#>  4 Hornet 4 Drive   21.4  3.22  19.4    21.6 -0.181  0.0576   2.64 1.05e-4
#>  5 Hornet Sportab…  18.7  3.44  17.0    18.2  0.504  0.0389   2.64 5.29e-4
#>  6 Valiant          18.1  3.46  20.2    21.1 -2.97   0.0957   2.58 5.10e-2
#>  7 Duster 360       14.3  3.57  15.8    16.4 -2.14   0.0729   2.61 1.93e-2
#>  8 Merc 240D        24.4  3.19  20      22.2  2.17   0.0791   2.61 2.18e-2
#>  9 Merc 230         22.8  3.15  22.9    25.1 -2.32   0.295    2.59 1.59e-1
#> 10 Merc 280         19.2  3.44  18.3    19.4 -0.185  0.0358   2.64 6.55e-5
#> # ℹ 22 more rows
#> # ℹ 1 more variable: .std.resid <dbl>
augment(mod, mtcars, interval = "confidence")
#> # A tibble: 32 × 20
#>    .rownames     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear
#>    <chr>       <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
#>  2 Mazda RX4 …  21       6  160    110  3.9   2.88  17.0     0     1     4
#>  3 Datsun 710   22.8     4  108     93  3.85  2.32  18.6     1     1     4
#>  4 Hornet 4 D…  21.4     6  258    110  3.08  3.22  19.4     1     0     3
#>  5 Hornet Spo…  18.7     8  360    175  3.15  3.44  17.0     0     0     3
#>  6 Valiant      18.1     6  225    105  2.76  3.46  20.2     1     0     3
#>  7 Duster 360   14.3     8  360    245  3.21  3.57  15.8     0     0     3
#>  8 Merc 240D    24.4     4  147.    62  3.69  3.19  20       1     0     4
#>  9 Merc 230     22.8     4  141.    95  3.92  3.15  22.9     1     0     4
#> 10 Merc 280     19.2     6  168.   123  3.92  3.44  18.3     1     0     4
#> # ℹ 22 more rows
#> # ℹ 9 more variables: carb <dbl>, .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 × 14
#>   .rownames      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear
#>   <chr>        <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
#> 2 Mazda RX4 W…  21       6   160   110  3.9   3.88  17.0     0     1     4
#> 3 Datsun 710    22.8     4   108    93  3.85  3.32  18.6     1     1     4
#> 4 Hornet 4 Dr…  21.4     6   258   110  3.08  4.22  19.4     1     0     3
#> 5 Hornet Spor…  18.7     8   360   175  3.15  4.44  17.0     0     0     3
#> 6 Valiant       18.1     6   225   105  2.76  4.46  20.2     1     0     3
#> # ℹ 3 more variables: carb <dbl>, .fitted <dbl>, .resid <dbl>

# ggplot2 example where we also construct 95% prediction interval

# simpler bivariate model since we're plotting in 2D
mod2 <- lm(mpg ~ wt, data = mtcars)

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)

augment(mod, newdata = newdata)
#> # A tibble: 6 × 12
#>   .rownames      cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4        6   160   110  3.9   3.62  16.5     0     1     4     4
#> 2 Mazda RX4 W…     6   160   110  3.9   3.88  17.0     0     1     4     4
#> 3 Datsun 710       4   108    93  3.85  3.32  18.6     1     1     4     1
#> 4 Hornet 4 Dr…     6   258   110  3.08  4.22  19.4     1     0     3     1
#> 5 Hornet Spor…     8   360   175  3.15  4.44  17.0     0     0     3     2
#> 6 Valiant          6   225   105  2.76  4.46  20.2     1     0     3     1
#> # ℹ 1 more variable: .fitted <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 × 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

Usage

Arguments

See also

Value

Examples