Glance at a(n) nlrq 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 class 'nlrq'
glance(x, ...)

Arguments

x

A nlrq object returned from quantreg::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 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.

See also

glance(), quantreg::nlrq()

Other quantreg tidiers: augment.nlrq(), augment.rq(), augment.rqs(), glance.rq(), tidy.nlrq(), tidy.rq(), tidy.rqs()

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