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, ...)
x | An |
---|---|
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 |
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.
if (requireNamespace("lm.beta", quietly = TRUE)) { library(lm.beta) mod <- stats::lm(speed ~ ., data = cars) std <- lm.beta(mod) tidy(std, conf.int = TRUE) 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) mod2 <- lm(weight ~ group) std2 <- lm.beta(mod2) 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 0 0.220 22.9 9.55e-15 -0.463 0.463 #> 2 groupTrt -0.371 -0.270 0.311 -1.19 2.49e- 1 -0.925 0.384