Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modelling function. This includes the name of the modelling function or any arguments passed to the modelling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

# S3 method for drc
glance(x, ...)

Arguments

x

A drc object produced by a call to drc::drm().

...

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.

See also

Value

A tibble::tibble() with exactly one row and columns:

AIC

Akaike's Information Criterion for the model.

BIC

Bayesian Information Criterion for the model.

df.residual

Residual degrees of freedom.

logLik

The log-likelihood of the model. [stats::logLik()] may be a useful reference.

AICc

AIC corrected for small samples

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