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 'drc'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)Arguments
- x
A
drcobject produced by a call todrc::drm().- 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.- ...
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 tibble has one row for each curve and term in the regression.
The curveid column indicates the curve.
See also
Other drc tidiers:
augment.drc(),
glance.drc()
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.
- curve
Index identifying the curve.
Examples
# load libraries for models and data
library(drc)
# fit model
mod <- drm(dead / total ~ conc, type,
weights = total, data = selenium, fct = LL.2(), type = "binomial"
)
# summarize model fit with tidiers
tidy(mod)
#> # A tibble: 8 × 6
#> term curve estimate std.error statistic p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 b 1 -1.50 0.155 -9.67 2.01e-22
#> 2 b 2 -0.843 0.139 -6.06 1.35e- 9
#> 3 b 3 -2.16 0.138 -15.7 1.65e-55
#> 4 b 4 -1.45 0.169 -8.62 3.41e-18
#> 5 e 1 252. 13.8 18.2 1.16e-74
#> 6 e 2 378. 39.4 9.61 3.53e-22
#> 7 e 3 120. 5.91 20.3 1.14e-91
#> 8 e 4 88.8 8.62 10.3 3.28e-25
tidy(mod, conf.int = TRUE)
#> # A tibble: 8 × 8
#> term curve estimate std.error statistic p.value conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 b 1 -1.50 0.155 -9.67 2.01e-22 -1.81 -1.20
#> 2 b 2 -0.843 0.139 -6.06 1.35e- 9 -1.12 -0.571
#> 3 b 3 -2.16 0.138 -15.7 1.65e-55 -2.43 -1.89
#> 4 b 4 -1.45 0.169 -8.62 3.41e-18 -1.78 -1.12
#> 5 e 1 252. 13.8 18.2 1.16e-74 225. 279.
#> 6 e 2 378. 39.4 9.61 3.53e-22 301. 456.
#> 7 e 3 120. 5.91 20.3 1.14e-91 108. 131.
#> 8 e 4 88.8 8.62 10.3 3.28e-25 71.9 106.
glance(mod)
#> # A tibble: 1 × 4
#> AIC BIC logLik df.residual
#> <dbl> <dbl> <logLik> <int>
#> 1 768. 778. -376.2099 17
augment(mod, selenium)
#> # A tibble: 25 × 7
#> type conc total dead .fitted .resid .cooksd
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 151 3 0 0.0199 0
#> 2 1 100 146 40 0.199 0.0748 0.0000909
#> 3 1 200 116 31 0.414 -0.146 0.000104
#> 4 1 300 159 85 0.565 -0.0302 0.00000516
#> 5 1 400 150 102 0.667 0.0133 0.00000220
#> 6 1 500 140 112 0.737 0.0633 0.0000720
#> 7 2 0 141 2 0 0.0142 0
#> 8 2 100 153 30 0.246 -0.0495 0.000168
#> 9 2 200 142 59 0.369 0.0468 0.0000347
#> 10 2 300 139 82 0.451 0.139 0.0000430
#> # ℹ 15 more rows