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 drc
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

A drc object produced by a call to drc::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 pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Details

The tibble has one row for each curve and term in the regression. The curveid column indicates the curve.

See also

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.

curveid

Id of the curve

Examples

library(drc) mod <- drm(dead/total~conc, type, weights = total, data = selenium, fct = LL.2(), type = "binomial") tidy(mod)
#> # A tibble: 8 x 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 x 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 NA NA #> 2 b 2 -0.843 0.139 -6.06 1.35e- 9 NA NA #> 3 b 3 -2.16 0.138 -15.7 1.65e-55 NA NA #> 4 b 4 -1.45 0.169 -8.62 3.41e-18 NA NA #> 5 e 1 252. 13.8 18.2 1.16e-74 NA NA #> 6 e 2 378. 39.4 9.61 3.53e-22 NA NA #> 7 e 3 120. 5.91 20.3 1.14e-91 NA NA #> 8 e 4 88.8 8.62 10.3 3.28e-25 NA NA
glance(mod)
#> # A tibble: 1 x 4 #> AIC BIC logLik df.residual #> <dbl> <dbl> <dbl> <int> #> 1 768. 778. -376. 17
augment(mod, selenium)
#> # A tibble: 25 x 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 #> # … with 15 more rows