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 mlm 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 |
In contrast to lm
object (simple linear model), tidy output for
mlm
(multiple linear model) objects contain 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.beta()
,
tidy.lm()
,
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.
mod <- lm(cbind(mpg, disp) ~ wt, mtcars) tidy(mod, conf.int = TRUE) #> # A tibble: 4 × 8 #> response term estimate std.error statistic p.value conf.low conf.high #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mpg (Intercept) 37.3 1.88 19.9 8.24e-19 33.5 41.1 #> 2 mpg wt -5.34 0.559 -9.56 1.29e-10 -6.49 -4.20 #> 3 disp (Intercept) -131. 35.7 -3.67 9.33e- 4 -204. -58.2 #> 4 disp wt 112. 10.6 10.6 1.22e-11 90.8 134.