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 nlrq
glance(x, ...)Arguments
- x
A
nlrqobject returned fromquantreg::nlrq().- ...
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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
See also
Other quantreg tidiers:
augment.nlrq(),
augment.rqs(),
augment.rq(),
glance.rq(),
tidy.nlrq(),
tidy.rqs(),
tidy.rq()
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.
- df.residual
Residual degrees of freedom.
- logLik
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
- tau
Quantile.
Examples
# load modeling library
library(quantreg)
# build artificial data with multiplicative error
set.seed(1)
dat <- NULL
dat$x <- rep(1:25, 20)
dat$y <- SSlogis(dat$x, 10, 12, 2) * rnorm(500, 1, 0.1)
# fit the median using nlrq
mod <- nlrq(y ~ SSlogis(x, Asym, mid, scal),
data = dat, tau = 0.5, trace = TRUE
)
#> 109.059 : 9.968027 11.947208 1.962113
#> final value 108.942725
#> converged
#> lambda = 1
#> 108.9427 : 9.958648 11.943273 1.967144
#> final value 108.490939
#> stopped after 2 iterations
#> lambda = 0.9750984
#> 108.4909 : 9.949430 11.987472 1.998607
#> final value 108.471416
#> converged
#> lambda = 0.9999299
#> 108.4714 : 9.94163 11.99077 1.99344
#> final value 108.471243
#> converged
#> lambda = 1
#> 108.4712 : 9.941008 11.990550 1.992921
#> final value 108.470935
#> converged
#> lambda = 0.8621249
#> 108.4709 : 9.942734 11.992773 1.993209
#> final value 108.470923
#> converged
#> lambda = 0.9999613
#> 108.4709 : 9.942629 11.992728 1.993136
#> final value 108.470919
#> converged
#> lambda = 1
#> 108.4709 : 9.942644 11.992737 1.993144
#> final value 108.470919
#> converged
#> lambda = 1
#> 108.4709 : 9.942644 11.992737 1.993144
#> final value 108.470919
#> converged
#> lambda = 1
#> 108.4709 : 9.942644 11.992737 1.993144
# summarize model fit with tidiers
tidy(mod)
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Asym 9.94 0.0841 118. 0
#> 2 mid 12.0 0.0673 178. 0
#> 3 scal 1.99 0.0248 80.3 0
glance(mod)
#> # A tibble: 1 × 5
#> tau logLik AIC BIC df.residual
#> <dbl> <logLik> <dbl> <dbl> <int>
#> 1 0.5 -429.0842 864. 877. 497
augment(mod)
#> # A tibble: 500 × 4
#> x y .fitted .resid
#> <int> <dbl> <dbl> <dbl>
#> 1 1 0.0382 0.0399 -0.00171
#> 2 2 0.0682 0.0657 0.00250
#> 3 3 0.101 0.108 -0.00728
#> 4 4 0.209 0.177 0.0315
#> 5 5 0.303 0.289 0.0137
#> 6 6 0.435 0.469 -0.0332
#> 7 7 0.796 0.751 0.0448
#> 8 8 1.28 1.18 0.0982
#> 9 9 1.93 1.81 0.118
#> 10 10 2.61 2.67 -0.0671
#> # ℹ 490 more rows