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 drc tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
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 |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
The tibble has one row for each curve and term in the regression.
The curveid
column indicates the curve.
Other drc tidiers:
augment.drc()
,
glance.drc()
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.
Index identifying the curve.
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#> # 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 -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.#> # A tibble: 1 x 4 #> AIC BIC logLik df.residual #> <dbl> <dbl> <logLik> <int> #> 1 768. 778. -376.2099 17#> # 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