Glance at a(n) biglm 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 '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. 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.

Value

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

AIC: Akaike's Information Criterion for the model.
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


# load modeling library
library(biglm)

# fit model -- linear regression
bfit <- biglm(mpg ~ wt + disp, mtcars)

# summarize model fit with tidiers
tidy(bfit)
#> # A tibble: 3 × 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 × 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 × 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 × 5
#>   r.squared   AIC deviance df.residual  nobs
#>       <dbl> <dbl>    <dbl>       <int> <int>
#> 1     0.781  253.     247.          29    32

# fit model -- logistic regression
bgfit <- bigglm(am ~ mpg, mtcars, family = binomial())

# summarize model fit with tidiers
tidy(bgfit)
#> # A tibble: 2 × 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 × 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 × 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 × 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 × 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 × 5
#>   r.squared   AIC deviance df.residual  nobs
#>       <dbl> <dbl>    <dbl>       <dbl> <dbl>
#> 1     0.175  33.7     29.7          30    32

Usage

Arguments

See also

Value

Examples