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 cross 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, exponentiate = FALSE, ...) # S3 method for summary.lm.beta tidy(x, ...)

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 |

exponentiate | Logical indicating whether or not to exponentiate the
the coefficient estimates. This is typical for logistic and multinomial
regressions, but a bad idea if there is no log or logit link. Defaults
to |

... | Additional arguments. Not used. Needed to match generic
signature only. |

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.svyglm`

,
`tidy.glm`

, `tidy.lm`

,
`tidy.mlm`

, `tidy.svyglm`

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.

library(lm.beta) mod <- stats::lm(speed ~ ., data = cars) # standardize mod_standardized <- lm.beta::lm.beta(mod) tidy(mod_standardized)#> # A tibble: 2 x 6 #> term estimate std_estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 8.28 0 0.874 9.47 1.44e-12 #> 2 dist 0.166 0.807 0.0175 9.46 1.49e-12## Taken from lm/ lm.beta help ## ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight 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) lm.D9 <- stats::lm(weight ~ group) # standardize lm.D9.beta <- lm.beta::lm.beta(lm.D9) tidy(lm.D9.beta)#> # A tibble: 2 x 6 #> term estimate std_estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 5.03 0 0.220 22.9 9.55e-15 #> 2 groupTrt -0.371 -0.270 0.311 -1.19 2.49e- 1