Glance at a(n) gam 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.

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

Arguments

x

A gam object returned from a call to mgcv::gam().

...

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

glance(), mgcv::gam()

Other mgcv tidiers: tidy.gam()

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.

deviance

Deviance of the model.

df

Degrees of freedom used by the 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.

Examples

g <- mgcv::gam(mpg ~ s(hp) + am + qsec, data = mtcars)

tidy(g)
#> # A tibble: 1 x 5
#>   term    edf ref.df statistic p.value
#>   <chr> <dbl>  <dbl>     <dbl>   <dbl>
#> 1 s(hp)  2.36   3.02      6.34 0.00218
tidy(g, parametric = TRUE)
#> # A tibble: 3 x 5
#>   term        estimate std.error statistic p.value
#>   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
#> 1 (Intercept)  16.7        9.83      1.70  0.101  
#> 2 am            4.37       1.56      2.81  0.00918
#> 3 qsec          0.0904     0.525     0.172 0.865  
glance(g)
#> # A tibble: 1 x 7
#>      df logLik   AIC   BIC deviance df.residual  nobs
#>   <dbl>  <dbl> <dbl> <dbl>    <dbl>       <dbl> <int>
#> 1  5.36  -74.4  162.  171.     196.        26.6    32
augment(g)
#> # A tibble: 32 x 11
#>    .rownames          mpg    am  qsec    hp .fitted .se.fit .resid   .hat .sigma
#>    <chr>            <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl>  <dbl>  <dbl> <lgl> 
#>  1 Mazda RX4         21       1  16.5   110    24.3   1.03  -3.25  0.145  NA    
#>  2 Mazda RX4 Wag     21       1  17.0   110    24.3   0.925 -3.30  0.116  NA    
#>  3 Datsun 710        22.8     1  18.6    93    26.0   0.894 -3.22  0.109  NA    
#>  4 Hornet 4 Drive    21.4     0  19.4   110    20.2   0.827  1.25  0.0930 NA    
#>  5 Hornet Sportabo…  18.7     0  17.0   175    15.7   0.815  3.02  0.0902 NA    
#>  6 Valiant           18.1     0  20.2   105    20.7   0.914 -2.56  0.113  NA    
#>  7 Duster 360        14.3     0  15.8   245    12.7   1.11   1.63  0.167  NA    
#>  8 Merc 240D         24.4     0  20      62    25.0   1.45  -0.618 0.287  NA    
#>  9 Merc 230          22.8     0  22.9    95    21.8   1.81   0.959 0.446  NA    
#> 10 Merc 280          19.2     0  18.3   123    19.0   0.864  0.211 0.102  NA    
#> # … with 22 more rows, and 1 more variable: .cooksd <dbl>