Glance at a(n) betareg 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 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 betareg
glance(x, ...)

Arguments

x	A `betareg` object produced by a call to `betareg::betareg()`.
...	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.

A betareg object produced by a call to betareg::betareg().

...

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.

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.null

Degrees of freedom used by the null model.

df.residual

Residual degrees of freedom.

logLik

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

nobs

Number of observations used.

pseudo.r.squared

Like the R squared statistic, but for situations when the R squared statistic isn't defined.

Examples


library(betareg)
data("GasolineYield", package = "betareg")

mod <- betareg(yield ~ batch + temp, data = GasolineYield)

mod
#> 
#> Call:
#> betareg(formula = yield ~ batch + temp, data = GasolineYield)
#> 
#> Coefficients (mean model with logit link):
#> (Intercept)       batch1       batch2       batch3       batch4       batch5  
#>    -6.15957      1.72773      1.32260      1.57231      1.05971      1.13375  
#>      batch6       batch7       batch8       batch9         temp  
#>     1.04016      0.54369      0.49590      0.38579      0.01097  
#> 
#> Phi coefficients (precision model with identity link):
#> (phi)  
#> 440.3  
#> 
tidy(mod)
#> # A tibble: 12 x 6
#>    component term        estimate  std.error statistic   p.value
#>    <chr>     <chr>          <dbl>      <dbl>     <dbl>     <dbl>
#>  1 mean      (Intercept)  -6.16     0.182       -33.8  3.44e-250
#>  2 mean      batch1        1.73     0.101        17.1  2.59e- 65
#>  3 mean      batch2        1.32     0.118        11.2  3.34e- 29
#>  4 mean      batch3        1.57     0.116        13.5  8.81e- 42
#>  5 mean      batch4        1.06     0.102        10.4  4.06e- 25
#>  6 mean      batch5        1.13     0.104        11.0  6.52e- 28
#>  7 mean      batch6        1.04     0.106         9.81 1.03e- 22
#>  8 mean      batch7        0.544    0.109         4.98 6.29e-  7
#>  9 mean      batch8        0.496    0.109         4.55 5.30e-  6
#> 10 mean      batch9        0.386    0.119         3.25 1.14e-  3
#> 11 mean      temp          0.0110   0.000413     26.6  1.26e-155
#> 12 precision (phi)       440.     110.            4.00 6.29e-  5
tidy(mod, conf.int = TRUE)
#> # A tibble: 12 x 8
#>    component term      estimate std.error statistic   p.value conf.low conf.high
#>    <chr>     <chr>        <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
#>  1 mean      (Interce…  -6.16     1.82e-1    -33.8  3.44e-250  -6.52     -5.80  
#>  2 mean      batch1      1.73     1.01e-1     17.1  2.59e- 65   1.53      1.93  
#>  3 mean      batch2      1.32     1.18e-1     11.2  3.34e- 29   1.09      1.55  
#>  4 mean      batch3      1.57     1.16e-1     13.5  8.81e- 42   1.34      1.80  
#>  5 mean      batch4      1.06     1.02e-1     10.4  4.06e- 25   0.859     1.26  
#>  6 mean      batch5      1.13     1.04e-1     11.0  6.52e- 28   0.931     1.34  
#>  7 mean      batch6      1.04     1.06e-1      9.81 1.03e- 22   0.832     1.25  
#>  8 mean      batch7      0.544    1.09e-1      4.98 6.29e-  7   0.330     0.758 
#>  9 mean      batch8      0.496    1.09e-1      4.55 5.30e-  6   0.282     0.709 
#> 10 mean      batch9      0.386    1.19e-1      3.25 1.14e-  3   0.153     0.618 
#> 11 mean      temp        0.0110   4.13e-4     26.6  1.26e-155   0.0102    0.0118
#> 12 precision (phi)     440.       1.10e+2      4.00 6.29e-  5 225.      656.    
tidy(mod, conf.int = TRUE, conf.level = .99)
#> # A tibble: 12 x 8
#>    component term     estimate  std.error statistic   p.value conf.low conf.high
#>    <chr>     <chr>       <dbl>      <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
#>  1 mean      (Interc…  -6.16     0.182       -33.8  3.44e-250 -6.63e+0   -5.69  
#>  2 mean      batch1     1.73     0.101        17.1  2.59e- 65  1.47e+0    1.99  
#>  3 mean      batch2     1.32     0.118        11.2  3.34e- 29  1.02e+0    1.63  
#>  4 mean      batch3     1.57     0.116        13.5  8.81e- 42  1.27e+0    1.87  
#>  5 mean      batch4     1.06     0.102        10.4  4.06e- 25  7.96e-1    1.32  
#>  6 mean      batch5     1.13     0.104        11.0  6.52e- 28  8.67e-1    1.40  
#>  7 mean      batch6     1.04     0.106         9.81 1.03e- 22  7.67e-1    1.31  
#>  8 mean      batch7     0.544    0.109         4.98 6.29e-  7  2.63e-1    0.825 
#>  9 mean      batch8     0.496    0.109         4.55 5.30e-  6  2.15e-1    0.776 
#> 10 mean      batch9     0.386    0.119         3.25 1.14e-  3  8.03e-2    0.691 
#> 11 mean      temp       0.0110   0.000413     26.6  1.26e-155  9.90e-3    0.0120
#> 12 precision (phi)    440.     110.            4.00 6.29e-  5  1.57e+2  724.    

augment(mod)
#> # A tibble: 32 x 6
#>    yield batch  temp .fitted .resid   .cooksd
#>    <dbl> <fct> <dbl>   <dbl>  <dbl>     <dbl>
#>  1 0.122 1       205  0.101   1.59  0.0791   
#>  2 0.223 1       275  0.195   1.66  0.0917   
#>  3 0.347 1       345  0.343   0.211 0.00155  
#>  4 0.457 1       407  0.508  -2.88  0.606    
#>  5 0.08  2       218  0.0797  0.109 0.0000168
#>  6 0.131 2       273  0.137  -0.365 0.00731  
#>  7 0.266 2       347  0.263   0.260 0.00523  
#>  8 0.074 3       212  0.0943 -1.77  0.0805   
#>  9 0.182 3       272  0.167   1.02  0.0441   
#> 10 0.304 3       340  0.298   0.446 0.0170   
#> # … with 22 more rows

glance(mod)
#> # A tibble: 1 x 7
#>   pseudo.r.squared df.null logLik   AIC   BIC df.residual  nobs
#>              <dbl>   <dbl>  <dbl> <dbl> <dbl>       <int> <int>
#> 1            0.962      30   84.8 -146. -128.          20    32

Arguments

See also

Value

Examples