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 biglm
glance(x, ...)

Arguments

x

A biglm object created by a call to biglm::biglm() or biglm::bigglm().

...

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:

deviance

Deviance of the model.

df.residual

Residual degrees of freedom.

nobs

Number of observations used.

r.squared

R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.

Examples

library(biglm)
#> Loading required package: DBI
bfit <- biglm(mpg ~ wt + disp, mtcars) tidy(bfit)
#> # A tibble: 3 x 4 #> term estimate std.error p.value #> <chr> <dbl> <dbl> <dbl> #> 1 (Intercept) 35.0 2.16 1.11e-58 #> 2 wt -3.35 1.16 4.00e- 3 #> 3 disp -0.0177 0.00919 5.38e- 2
tidy(bfit, conf.int = TRUE)
#> # A tibble: 3 x 6 #> term estimate std.error p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 35.0 2.16 1.11e-58 30.7 39.2 #> 2 wt -3.35 1.16 4.00e- 3 -5.63 -1.07 #> 3 disp -0.0177 0.00919 5.38e- 2 -0.0357 0.000288
tidy(bfit, conf.int = TRUE, conf.level = .9)
#> # A tibble: 3 x 6 #> term estimate std.error p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 35.0 2.16 1.11e-58 31.4 38.5 #> 2 wt -3.35 1.16 4.00e- 3 -5.27 -1.44 #> 3 disp -0.0177 0.00919 5.38e- 2 -0.0328 -0.00261
glance(bfit)
#> # A tibble: 1 x 5 #> r.squared AIC deviance df.residual nobs #> <dbl> <dbl> <dbl> <int> <int> #> 1 0.781 253. 247. 29 32
# bigglm: logistic regression bgfit <- bigglm(am ~ mpg, mtcars, family = binomial()) tidy(bgfit)
#> # A tibble: 2 x 4 #> term estimate std.error p.value #> <chr> <dbl> <dbl> <dbl> #> 1 (Intercept) -6.60 2.35 0.00498 #> 2 mpg 0.307 0.115 0.00751
tidy(bgfit, exponentiate = TRUE)
#> # A tibble: 2 x 4 #> term estimate std.error p.value #> <chr> <dbl> <dbl> <dbl> #> 1 (Intercept) 0.00136 2.35 0.00498 #> 2 mpg 1.36 0.115 0.00751
tidy(bgfit, conf.int = TRUE)
#> # A tibble: 2 x 6 #> term estimate std.error p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) -6.60 2.35 0.00498 -11.2 -1.99 #> 2 mpg 0.307 0.115 0.00751 0.0819 0.532
tidy(bgfit, conf.int = TRUE, conf.level = .9)
#> # A tibble: 2 x 6 #> term estimate std.error p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) -6.60 2.35 0.00498 -10.5 -2.74 #> 2 mpg 0.307 0.115 0.00751 0.118 0.496
tidy(bgfit, conf.int = TRUE, conf.level = .9, exponentiate = TRUE)
#> # A tibble: 2 x 6 #> term estimate std.error p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 0.00136 2.35 0.00498 0.0000283 0.0648 #> 2 mpg 1.36 0.115 0.00751 1.13 1.64
glance(bgfit)
#> # A tibble: 1 x 5 #> r.squared AIC deviance df.residual nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 0.175 33.7 29.7 30 32