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.

Usage

# S3 method for class '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. 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(), mgcv::gam()

Other mgcv tidiers: tidy.gam()

Value

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

adj.r.squared

Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.

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.

npar

Number of parameters in the model.

Examples

# load libraries for models and data
library(mgcv)

# fit model
g <- gam(mpg ~ s(hp) + am + qsec, data = mtcars)

# summarize model fit with tidiers
tidy(g)
#> # A tibble: 1 × 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 × 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 × 9
#>      df logLik   AIC   BIC deviance df.residual  nobs adj.r.squared
#>   <dbl>  <dbl> <dbl> <dbl>    <dbl>       <dbl> <int>         <dbl>
#> 1  5.36  -74.4  162.  171.     196.        26.6    32         0.797
#> # ℹ 1 more variable: npar <int>
augment(g)
#> # A tibble: 32 × 11
#>    .rownames        mpg    am  qsec    hp .fitted .se.fit .resid   .hat
#>    <chr>          <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl>  <dbl>  <dbl>
#>  1 Mazda RX4       21       1  16.5   110    24.3   1.03  -3.25  0.145 
#>  2 Mazda RX4 Wag   21       1  17.0   110    24.3   0.925 -3.30  0.116 
#>  3 Datsun 710      22.8     1  18.6    93    26.0   0.894 -3.22  0.109 
#>  4 Hornet 4 Drive  21.4     0  19.4   110    20.2   0.827  1.25  0.0930
#>  5 Hornet Sporta…  18.7     0  17.0   175    15.7   0.815  3.02  0.0902
#>  6 Valiant         18.1     0  20.2   105    20.7   0.914 -2.56  0.113 
#>  7 Duster 360      14.3     0  15.8   245    12.7   1.11   1.63  0.167 
#>  8 Merc 240D       24.4     0  20      62    25.0   1.45  -0.618 0.287 
#>  9 Merc 230        22.8     0  22.9    95    21.8   1.81   0.959 0.446 
#> 10 Merc 280        19.2     0  18.3   123    19.0   0.864  0.211 0.102 
#> # ℹ 22 more rows
#> # ℹ 2 more variables: .sigma <lgl>, .cooksd <dbl>