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 'lm.beta'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)Arguments
- x
An
lm.betaobject created by lm.beta::lm.beta.- 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
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.
See also
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()
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 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)
#> # A tibble: 2 × 8
#> term estimate std_estimate std.error statistic p.value conf.low
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 8.28 NA 0.874 9.47 1.44e-12 NA
#> 2 dist 0.166 0.807 0.0175 9.46 1.49e-12 0.772
#> # ℹ 1 more variable: conf.high <dbl>
# 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
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 5.03 NA 0.220 22.9 9.55e-15 NA
#> 2 groupTrt -0.371 -0.270 0.311 -1.19 2.49e- 1 -0.925
#> # ℹ 1 more variable: conf.high <dbl>