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 nls
glance(x, ...)
An nls
object returned from stats::nls()
.
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.
Other nls tidiers:
augment.nls()
,
tidy.nls()
A tibble::tibble()
with exactly one row and columns:
Akaike's Information Criterion for the model.
Bayesian Information Criterion for the model.
Deviance of the model.
Residual degrees of freedom.
The achieved convergence tolerance.
Whether the fit successfully converged.
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
Number of observations used.
Estimated standard error of the residuals.
# fit model
n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2))
# summarize model fit with tidiers + visualization
tidy(n)
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 k 49.7 3.79 13.1 5.96e-14
#> 2 e 0.746 0.0199 37.5 8.86e-27
augment(n)
#> # A tibble: 32 × 4
#> mpg wt .fitted .resid
#> <dbl> <dbl> <dbl> <dbl>
#> 1 21 2.62 23.0 -2.01
#> 2 21 2.88 21.4 -0.352
#> 3 22.8 2.32 25.1 -2.33
#> 4 21.4 3.22 19.3 2.08
#> 5 18.7 3.44 18.1 0.611
#> 6 18.1 3.46 18.0 0.117
#> 7 14.3 3.57 17.4 -3.11
#> 8 24.4 3.19 19.5 4.93
#> 9 22.8 3.15 19.7 3.10
#> 10 19.2 3.44 18.1 1.11
#> # … with 22 more rows
glance(n)
#> # A tibble: 1 × 9
#> sigma isConv finTol logLik AIC BIC deviance df.residual nobs
#> <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 2.67 TRUE 0.00000204 -75.8 158. 162. 214. 30 32
library(ggplot2)
ggplot(augment(n), aes(wt, mpg)) +
geom_point() +
geom_line(aes(y = .fitted))
newdata <- head(mtcars)
newdata$wt <- newdata$wt + 1
augment(n, newdata = newdata)
#> # A tibble: 6 × 13
#> .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 1 more variable: .fitted <dbl>