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 class 'glmrob'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)Arguments
- x
A
glmrobobject returned fromrobustbase::glmrob().- 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:
Details
For tidiers for robust models from the MASS package see
tidy.rlm().
See also
Other robustbase tidiers:
augment.glmrob(),
augment.lmrob(),
glance.lmrob(),
tidy.lmrob()
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
if (requireNamespace("robustbase", quietly = TRUE)) {
# load libraries for models and data
library(robustbase)
data(coleman)
set.seed(0)
m <- lmrob(Y ~ ., data = coleman)
tidy(m)
augment(m)
glance(m)
data(carrots)
Rfit <- glmrob(cbind(success, total - success) ~ logdose + block,
family = binomial, data = carrots, method = "Mqle",
control = glmrobMqle.control(tcc = 1.2)
)
tidy(Rfit)
augment(Rfit)
}
#> # A tibble: 24 × 5
#> cbind(success, total - success…¹ [,""] logdose block .fitted .resid[,1]
#> <int> <int> <dbl> <fct> <dbl> <dbl>
#> 1 10 25 1.52 B1 -0.726 10.7
#> 2 16 26 1.64 B1 -0.972 17.0
#> 3 8 42 1.76 B1 -1.22 9.22
#> 4 6 36 1.88 B1 -1.46 7.46
#> 5 9 26 2 B1 -1.71 10.7
#> 6 9 33 2.12 B1 -1.96 11.0
#> 7 1 31 2.24 B1 -2.20 3.20
#> 8 2 26 2.36 B1 -2.45 4.45
#> 9 17 21 1.52 B2 -0.491 17.5
#> 10 10 30 1.64 B2 -0.737 10.7
#> # ℹ 14 more rows
#> # ℹ abbreviated name: ¹`cbind(success, total - success)`[,"success"]
#> # ℹ 1 more variable: .resid[2] <dbl>