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.
# S3 method for lmrob tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
| x | A |
|---|---|
| conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
| conf.level | The confidence level to use for the confidence interval
if |
| ... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
For tidiers for robust models from the MASS package see
tidy.rlm().
Other robustbase tidiers:
augment.glmrob(),
augment.lmrob(),
glance.lmrob(),
tidy.glmrob()
library(robustbase) # From the robustbase::lmrob examples: data(coleman) set.seed(0) m <- robustbase::lmrob(Y ~ ., data = coleman) tidy(m) #> # A tibble: 6 × 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 30.5 6.71 4.54 4.59e- 4 #> 2 salaryP -1.67 0.431 -3.86 1.72e- 3 #> 3 fatherWc 0.0843 0.0147 5.74 5.10e- 5 #> 4 sstatus 0.668 0.0339 19.7 1.30e-11 #> 5 teacherSc 1.17 0.110 10.6 4.35e- 8 #> 6 motherLev -4.14 0.921 -4.49 5.07e- 4 augment(m) #> # A tibble: 20 × 8 #> Y salaryP fatherWc sstatus teacherSc motherLev .fitted .resid #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 37.0 3.83 28.9 7.2 26.6 6.19 36.8 0.191 #> 2 26.5 2.89 20.1 -11.7 24.4 5.17 26.7 -0.159 #> 3 36.5 2.86 69.0 12.3 25.7 7.04 40.7 -4.16 #> 4 40.7 2.92 65.4 14.3 25.7 7.1 41.3 -0.625 #> 5 37.1 3.06 29.6 6.31 25.4 6.15 36.3 0.768 #> 6 33.9 2.07 44.8 6.16 21.6 6.41 33.7 0.249 #> 7 41.8 2.52 77.4 12.7 24.9 6.86 42.0 -0.203 #> 8 33.4 2.45 24.7 -0.17 25.0 5.78 33.7 -0.282 #> 9 41.0 3.13 65.0 9.85 26.6 6.51 41.5 -0.466 #> 10 37.2 2.44 9.99 -0.05 28.0 5.57 36.9 0.286 #> 11 23.3 2.09 12.2 -12.9 23.5 5.62 23.7 -0.368 #> 12 35.2 2.52 22.6 0.92 23.6 5.34 34.3 0.912 #> 13 34.9 2.22 14.3 4.77 24.5 5.8 35.8 -0.924 #> 14 33.1 2.67 31.8 -0.96 25.8 6.19 32.6 0.486 #> 15 22.7 2.71 11.6 -16.0 25.2 5.62 22.4 0.266 #> 16 39.7 3.14 68.5 10.6 25.0 6.94 38.6 1.07 #> 17 31.8 3.54 42.6 2.66 25.0 6.33 33.0 -1.19 #> 18 31.7 2.52 16.7 -11.0 24.8 6.01 24.5 7.23 #> 19 43.1 2.68 86.3 15.0 25.5 7.51 42.1 1.03 #> 20 41.0 2.37 76.7 12.8 24.5 6.96 41.4 -0.367 glance(m) #> # A tibble: 1 × 3 #> r.squared sigma df.residual #> <dbl> <dbl> <int> #> 1 0.981 1.13 14 # From the robustbase::glmrob examples: data(carrots) Rfit <- glmrob(cbind(success, total - success) ~ logdose + block, family = binomial, data = carrots, method = "Mqle", control = glmrobMqle.control(tcc = 1.2) ) tidy(Rfit) #> # A tibble: 4 × 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 2.39 0.692 3.45 0.000561 #> 2 logdose -2.05 0.368 -5.56 0.0000000268 #> 3 blockB2 0.235 0.212 1.11 0.268 #> 4 blockB3 -0.450 0.241 -1.87 0.0620 augment(Rfit) #> # A tibble: 24 × 4 #> `cbind(success, total - success)`[,"success"] [,""] logdose block .fitted #> <int> <int> <dbl> <fct> <dbl> #> 1 10 25 1.52 B1 -0.726 #> 2 16 26 1.64 B1 -0.972 #> 3 8 42 1.76 B1 -1.22 #> 4 6 36 1.88 B1 -1.46 #> 5 9 26 2 B1 -1.71 #> 6 9 33 2.12 B1 -1.96 #> 7 1 31 2.24 B1 -2.20 #> 8 2 26 2.36 B1 -2.45 #> 9 17 21 1.52 B2 -0.491 #> 10 10 30 1.64 B2 -0.737 #> # … with 14 more rows