Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
Usage
# S3 method for nlrq
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)Arguments
- x
A
nlrqobject returned fromquantreg::nlrq().- conf.int
Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to
FALSE.- conf.level
The confidence level to use for the confidence interval if
conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.- ...
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.nlrq(),
glance.rq(),
tidy.rqs(),
tidy.rq()
Value
A tibble::tibble() with columns:
- conf.high
Upper bound on the confidence interval for the estimate.
- conf.low
Lower bound on the confidence interval for the estimate.
- estimate
The estimated value of the regression term.
- p.value
The two-sided p-value associated with the observed statistic.
- statistic
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
- std.error
The standard error of the regression term.
- term
The name of the regression term.
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