Glance at a(n) drc object

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 modeling function. This includes the name of the modeling function or any arguments passed to the modeling 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.

Glance returns the same number of columns regardless of whether the model matrix is rank-deficient or not. If so, entries in columns that no longer have a well-defined value are filled in with an NA of the appropriate type.

Usage

# S3 method for class '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. Two exceptions here are:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

See also

glance(), drc::drm()

Other drc tidiers: augment.drc(), tidy.drc()

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

# 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