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 clm tidy( x, conf.int = FALSE, conf.level = 0.95, conf.type = c("profile", "Wald"), exponentiate = FALSE, ... )
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 |
conf.type | Whether to use |
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. Cautionary note: Misspelled arguments will be
absorbed in |
In broom 0.7.0
the coefficient_type
column was renamed to
coef.type
, and the contents were changed as well.
Note that intercept
type coefficients correspond to alpha
parameters, location
type coefficients correspond to beta
parameters, and scale
type coefficients correspond to zeta
parameters.
tidy, ordinal::clm()
, ordinal::confint.clm()
Other ordinal tidiers:
augment.clm()
,
augment.polr()
,
glance.clmm()
,
glance.clm()
,
glance.polr()
,
glance.svyolr()
,
tidy.clmm()
,
tidy.polr()
,
tidy.svyolr()
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.
#> # A tibble: 7 x 6 #> term estimate std.error statistic p.value coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 -1.41 0.545 -2.59 9.66e- 3 intercept #> 2 2|3 1.14 0.510 2.24 2.48e- 2 intercept #> 3 3|4 3.38 0.638 5.29 1.21e- 7 intercept #> 4 4|5 4.94 0.751 6.58 4.66e-11 intercept #> 5 tempwarm 2.32 0.701 3.31 9.28e- 4 location #> 6 contactyes 1.35 0.660 2.04 4.13e- 2 location #> 7 tempwarm:contactyes 0.360 0.924 0.389 6.97e- 1 location#> # A tibble: 7 x 8 #> term estimate std.error statistic p.value conf.low conf.high coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 -1.41 0.545 -2.59 9.66e- 3 NA NA intercept #> 2 2|3 1.14 0.510 2.24 2.48e- 2 NA NA intercept #> 3 3|4 3.38 0.638 5.29 1.21e- 7 NA NA intercept #> 4 4|5 4.94 0.751 6.58 4.66e-11 NA NA intercept #> 5 tempwarm 2.32 0.701 3.31 9.28e- 4 1.20 3.52 location #> 6 contactyes 1.35 0.660 2.04 4.13e- 2 0.284 2.47 location #> 7 tempwarm:c… 0.360 0.924 0.389 6.97e- 1 -1.17 1.89 location#> # A tibble: 7 x 8 #> term estimate std.error statistic p.value conf.low conf.high coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 0.244 0.545 -2.59 9.66e- 3 0.0837 0.710 intercept #> 2 2|3 3.14 0.510 2.24 2.48e- 2 1.16 8.52 intercept #> 3 3|4 29.3 0.638 5.29 1.21e- 7 8.38 102. intercept #> 4 4|5 140. 0.751 6.58 4.66e-11 32.1 610. intercept #> 5 tempwarm 10.2 0.701 3.31 9.28e- 4 2.58 40.2 location #> 6 contactyes 3.85 0.660 2.04 4.13e- 2 1.05 14.0 location #> 7 tempwarm:c… 1.43 0.924 0.389 6.97e- 1 0.234 8.76 location#> # A tibble: 1 x 6 #> edf AIC BIC logLik df.residual nobs #> <int> <dbl> <dbl> <logLik> <dbl> <dbl> #> 1 7 187. 203. -86.4162 65 72#> # A tibble: 72 x 4 #> rating temp contact .fitted #> <ord> <fct> <fct> <dbl> #> 1 2 cold no 0.562 #> 2 3 cold no 0.209 #> 3 3 cold yes 0.435 #> 4 4 cold yes 0.0894 #> 5 4 warm no 0.190 #> 6 4 warm no 0.190 #> 7 5 warm yes 0.286 #> 8 5 warm yes 0.286 #> 9 1 cold no 0.196 #> 10 2 cold no 0.562 #> # … with 62 more rows#> # A tibble: 72 x 4 #> rating temp contact .fitted #> <ord> <fct> <fct> <fct> #> 1 2 cold no 2 #> 2 3 cold no 2 #> 3 3 cold yes 3 #> 4 4 cold yes 3 #> 5 4 warm no 3 #> 6 4 warm no 3 #> 7 5 warm yes 4 #> 8 5 warm yes 4 #> 9 1 cold no 2 #> 10 2 cold no 2 #> # … with 62 more rows#> # A tibble: 9 x 6 #> term estimate std.error statistic p.value coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2.(Intercept) -1.32 0.562 -2.35 0.0186 intercept #> 2 2|3.(Intercept) 1.25 0.475 2.63 0.00866 intercept #> 3 3|4.(Intercept) 3.55 0.656 5.41 0.0000000625 intercept #> 4 4|5.(Intercept) 4.66 0.860 5.42 0.0000000608 intercept #> 5 1|2.contactyes -1.62 1.16 -1.39 0.164 intercept #> 6 2|3.contactyes -1.51 0.591 -2.56 0.0105 intercept #> 7 3|4.contactyes -1.67 0.649 -2.58 0.00985 intercept #> 8 4|5.contactyes -1.05 0.897 -1.17 0.241 intercept #> 9 tempwarm 2.52 0.535 4.71 0.00000250 location#> # A tibble: 1 x 6 #> edf AIC BIC logLik df.residual nobs #> <int> <dbl> <dbl> <logLik> <dbl> <dbl> #> 1 9 190. 211. -86.20855 63 72