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 rq
glance(x, ...)Arguments
- x
An
rqobject returned fromquantreg::rq().- ...
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:
Details
Only models with a single tau value may be passed.
For multiple values, please use a purrr::map() workflow instead, e.g.
taus %>%
map(function(tau_val) rq(y ~ x, tau = tau_val)) %>%
map_dfr(glance)See also
Other quantreg tidiers:
augment.nlrq(),
augment.rqs(),
augment.rq(),
glance.nlrq(),
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 and data
library(quantreg)
data(stackloss)
# median (l1) regression fit for the stackloss data.
mod1 <- rq(stack.loss ~ stack.x, .5)
# weighted sample median
mod2 <- rq(rnorm(50) ~ 1, weights = runif(50))
# summarize model fit with tidiers
tidy(mod1)
#> # A tibble: 4 × 5
#> term estimate conf.low conf.high tau
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -39.7 -53.8 -24.5 0.5
#> 2 stack.xAir.Flow 0.832 0.509 1.17 0.5
#> 3 stack.xWater.Temp 0.574 0.272 3.04 0.5
#> 4 stack.xAcid.Conc. -0.0609 -0.278 0.0153 0.5
glance(mod1)
#> # A tibble: 1 × 5
#> tau logLik AIC BIC df.residual
#> <dbl> <logLik> <dbl> <dbl> <int>
#> 1 0.5 -50.15272 108. 112. 17
augment(mod1)
#> # A tibble: 21 × 5
#> stack.loss stack.x[,"Air.Flow"] [,"Water.Temp"] .resid .fitted .tau
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 42 80 27 5.06e+ 0 36.9 0.5
#> 2 37 80 27 -1.42e-14 37 0.5
#> 3 37 75 25 5.43e+ 0 31.6 0.5
#> 4 28 62 24 7.63e+ 0 20.4 0.5
#> 5 18 62 22 -1.22e+ 0 19.2 0.5
#> 6 18 62 23 -1.79e+ 0 19.8 0.5
#> 7 19 62 24 -1.00e+ 0 20 0.5
#> 8 20 62 24 -7.11e-15 20 0.5
#> 9 15 58 23 -1.46e+ 0 16.5 0.5
#> 10 14 58 18 -2.03e- 2 14.0 0.5
#> # ℹ 11 more rows
#> # ℹ 1 more variable: stack.x[3] <dbl>
tidy(mod2)
#> # A tibble: 1 × 5
#> term estimate conf.low conf.high tau
#> <chr> <dbl> <lgl> <lgl> <dbl>
#> 1 (Intercept) 0.124 NA NA 0.5
glance(mod2)
#> # A tibble: 1 × 5
#> tau logLik AIC BIC df.residual
#> <dbl> <logLik> <dbl> <dbl> <int>
#> 1 0.5 -78.76986 160. 161. 49
augment(mod2)
#> # A tibble: 50 × 5
#> `rnorm(50)` `(weights)` .resid .fitted .tau
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.393 0.696 0.269 0.124 0.5
#> 2 0.458 0.266 0.334 0.124 0.5
#> 3 -1.22 0.660 -1.34 0.124 0.5
#> 4 -1.12 0.212 -1.25 0.124 0.5
#> 5 0.993 0.00527 0.869 0.124 0.5
#> 6 -1.83 0.103 -1.96 0.124 0.5
#> 7 0.124 0.287 0 0.124 0.5
#> 8 0.591 0.444 0.467 0.124 0.5
#> 9 0.805 0.693 0.681 0.124 0.5
#> 10 0.00754 0.0209 -0.116 0.124 0.5
#> # ℹ 40 more rows
# varying tau to generate an rqs object
mod3 <- rq(stack.loss ~ stack.x, tau = c(.25, .5))
tidy(mod3)
#> # A tibble: 8 × 5
#> term estimate conf.low conf.high tau
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -3.6 e+ 1 -59.0 -7.84 0.25
#> 2 stack.xAir.Flow 5.00e- 1 0.229 0.970 0.25
#> 3 stack.xWater.Temp 1.00e+ 0 0.286 2.26 0.25
#> 4 stack.xAcid.Conc. -4.58e-16 -0.643 0.0861 0.25
#> 5 (Intercept) -3.97e+ 1 -53.8 -24.5 0.5
#> 6 stack.xAir.Flow 8.32e- 1 0.509 1.17 0.5
#> 7 stack.xWater.Temp 5.74e- 1 0.272 3.04 0.5
#> 8 stack.xAcid.Conc. -6.09e- 2 -0.278 0.0153 0.5
augment(mod3)
#> # A tibble: 42 × 5
#> stack.loss stack.x[,"Air.Flow"] [,"Water.Temp"] .tau .resid .fitted
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 42 80 27 0.25 1.10e+ 1 31.0
#> 2 42 80 27 0.5 5.06e+ 0 36.9
#> 3 37 80 27 0.25 6.00e+ 0 31.0
#> 4 37 80 27 0.5 -1.42e-14 37
#> 5 37 75 25 0.25 1.05e+ 1 26.5
#> 6 37 75 25 0.5 5.43e+ 0 31.6
#> 7 28 62 24 0.25 9.00e+ 0 19
#> 8 28 62 24 0.5 7.63e+ 0 20.4
#> 9 18 62 22 0.25 1.00e+ 0 17.0
#> 10 18 62 22 0.5 -1.22e+ 0 19.2
#> # ℹ 32 more rows
#> # ℹ 1 more variable: stack.x[3] <dbl>
# glance cannot handle rqs objects like `mod3`--use a purrr
# `map`-based workflow instead