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 nls
glance(x, ...)

Arguments

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.

See also

tidy, stats::nls()

Other nls tidiers: augment.nls, tidy.nls

Value

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

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 for the model.

finTol

the achieved convergence tolerance

isConv

whether the fit successfully converged

logLik

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

sigma

Estimated standard error of the residuals.

Examples

n <- nls(mpg ~ k * e ^ wt, data = mtcars, start = list(k = 1, e = 2)) tidy(n)
#> # A tibble: 2 x 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
#> # A tibble: 32 x 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
#> # A tibble: 1 x 8 #> sigma isConv finTol logLik AIC BIC deviance df.residual #> <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 2.67 TRUE 0.00000204 -75.8 158. 162. 214. 30
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 x 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 RX… 21 6 160 110 3.9 3.88 17.0 0 1 4 4 #> 3 Datsun 7… 22.8 4 108 93 3.85 3.32 18.6 1 1 4 1 #> 4 Hornet 4… 21.4 6 258 110 3.08 4.22 19.4 1 0 3 1 #> 5 Hornet S… 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>