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 lm.beta
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
An lm.beta
object created by lm.beta::lm.beta.
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE
.
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 pass conf.lvel = 0.9
, all computation will
proceed using conf.level = 0.95
. Additionally, if you pass
newdata = my_tibble
to an augment()
method that does not
accept a newdata
argument, it will use the default value for
the data
argument.
If the linear model is an mlm
object (multiple linear model),
there is an additional column response
.
If you have missing values in your model data, you may need to refit
the model with na.action = na.exclude
.
Other lm tidiers:
augment.glm()
,
augment.lm()
,
glance.glm()
,
glance.lm()
,
glance.summary.lm()
,
glance.svyglm()
,
tidy.glm()
,
tidy.lm()
,
tidy.mlm()
,
tidy.summary.lm()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("lm.beta", quietly = TRUE)) {
# load libraries for models and data
library(lm.beta)
# fit models
mod <- stats::lm(speed ~ ., data = cars)
std <- lm.beta(mod)
# summarize model fit with tidiers
tidy(std, conf.int = TRUE)
# generate data
ctl <- c(4.17, 5.58, 5.18, 6.11, 4.50, 4.61, 5.17, 4.53, 5.33, 5.14)
trt <- c(4.81, 4.17, 4.41, 3.59, 5.87, 3.83, 6.03, 4.89, 4.32, 4.69)
group <- gl(2, 10, 20, labels = c("Ctl", "Trt"))
weight <- c(ctl, trt)
# fit models
mod2 <- lm(weight ~ group)
std2 <- lm.beta(mod2)
# summarize model fit with tidiers
tidy(std2, conf.int = TRUE)
}
#> # A tibble: 2 × 8
#> term estimate std_estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Interc… 5.03 NA 0.220 22.9 9.55e-15 NA NA
#> 2 groupTrt -0.371 -0.270 0.311 -1.19 2.49e- 1 -0.925 0.384