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.
Usage
# S3 method for class 'svyolr'
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)Arguments
- x
A
svyolrobject returned fromsurvey::svyolr().- conf.int
Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to
FALSE.- conf.level
The confidence level to use for the confidence interval if
conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.- 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
FALSE.- ...
Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in
..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
Details
The tidy.svyolr() tidier is a light wrapper around
tidy.polr(). However, the implementation for p-value calculation
in tidy.polr() is both computationally intensive and specific to that
model, so the p.values argument to tidy.svyolr() is currently ignored,
and will raise a warning when passed.
See also
Other ordinal tidiers:
augment.clm(),
augment.polr(),
glance.clm(),
glance.clmm(),
glance.polr(),
glance.svyolr(),
tidy.clm(),
tidy.clmm(),
tidy.polr()
Value
A tibble::tibble() with columns:
- conf.high
Upper bound on the confidence interval for the estimate.
- conf.low
Lower bound on the confidence interval for the estimate.
- estimate
The estimated value of the regression term.
- p.value
The two-sided p-value associated with the observed statistic.
- statistic
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
- std.error
The standard error of the regression term.
- term
The name of the regression term.
Examples
library(broom)
library(survey)
data(api)
dclus1 <- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc)
dclus1 <- update(dclus1, mealcat = cut(meals, c(0, 25, 50, 75, 100)))
m <- svyolr(mealcat ~ avg.ed + mobility + stype, design = dclus1)
m
#> Call:
#> svyolr(mealcat ~ avg.ed + mobility + stype, design = dclus1)
#>
#> Coefficients:
#> avg.ed mobility stypeH stypeM
#> -2.6999217 0.0325042 -1.7574715 -0.6191463
#>
#> Intercepts:
#> (0,25]|(25,50] (25,50]|(50,75] (50,75]|(75,100]
#> -8.857919 -6.586464 -4.924938
tidy(m, conf.int = TRUE)
#> # A tibble: 7 × 7
#> term estimate std.error statistic conf.low conf.high coef.type
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 avg.ed -2.70 1.13 -2.38 -4.92e+0 -0.477 coeffici…
#> 2 mobility 0.0325 0.0207 1.57 -7.98e-3 0.0730 coeffici…
#> 3 stypeH -1.76 0.700 -2.51 -3.13e+0 -0.386 coeffici…
#> 4 stypeM -0.619 0.310 -2.00 -1.23e+0 -0.0123 coeffici…
#> 5 (0,25]|(25,50] -8.86 3.69 -2.40 -1.61e+1 -1.63 scale
#> 6 (25,50]|(50,7… -6.59 3.11 -2.12 -1.27e+1 -0.493 scale
#> 7 (50,75]|(75,1… -4.92 2.86 -1.72 -1.05e+1 0.687 scale