Tidy a(n) biglm object

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

# S3 method for class 'biglm'
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

Arguments

x

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

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

exponentiate

Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.

...

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 columns:

conf.high: Upper bound on the confidence interval for the estimate.
conf.low: Lower bound on the confidence interval for the estimate.
estimate: The estimated value of the regression term.
p.value: The two-sided p-value associated with the observed statistic.
statistic: The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
std.error: The standard error of the regression term.
term: The name of the regression term.

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