Aliases: tidy.nlrq nlrq_tidiers
Keywords:
### ** Examples #' # feel free to ignore the following line—it allows {broom} to supply # examples without requiring the model-supplying package to be installed. if (requireNamespace("quantreg", quietly = TRUE)) { # 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) # summarize model fit with tidiers tidy(mod) glance(mod) augment(mod) }
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 converged 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
# 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 # … with 490 more rows