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 summary.lm tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
x | A |
---|---|
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 |
The tidy.summary.lm()
method is a potentially useful alternative
to tidy.lm()
. For instance, if users have already converted large lm
objects into their leaner summary.lm
equivalents to conserve memory.
Other lm tidiers:
augment.glm()
,
augment.lm()
,
glance.glm()
,
glance.lm()
,
glance.summary.lm()
,
glance.svyglm()
,
tidy.glm()
,
tidy.lm.beta()
,
tidy.lm()
,
tidy.mlm()
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(mpg ~ wt + qsec, data = mtcars) modsumm <- summary(mod) tidy(mod, conf.int = TRUE) #> # A tibble: 3 × 7 #> term estimate std.error statistic p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 19.7 5.25 3.76 7.65e- 4 9.00 30.5 #> 2 wt -5.05 0.484 -10.4 2.52e-11 -6.04 -4.06 #> 3 qsec 0.929 0.265 3.51 1.50e- 3 0.387 1.47 tidy(modsumm, conf.int = TRUE) # same #> # A tibble: 3 × 7 #> term estimate std.error statistic p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 19.7 5.25 3.76 7.65e- 4 9.00 30.5 #> 2 wt -5.05 0.484 -10.4 2.52e-11 -6.04 -4.06 #> 3 qsec 0.929 0.265 3.51 1.50e- 3 0.387 1.47 glance(mod) #> # A tibble: 1 × 12 #> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 0.826 0.814 2.60 69.0 9.39e-12 2 -74.4 157. 163. #> # … with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int> glance(modsumm) # mostly the same, except for a few missing columns #> # A tibble: 1 × 8 #> r.squared adj.r.squared sigma statistic p.value df df.residual nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> #> 1 0.826 0.814 2.60 69.0 9.39e-12 2 29 32