Tidy a(n) speedlm 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 'speedlm'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

A speedlm object returned from speedglm::speedlm().

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.

...

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

speedglm::speedlm(), tidy.lm()

Other speedlm tidiers: augment.speedlm(), glance.speedglm(), glance.speedlm(), tidy.speedglm()

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(speedglm)

# fit model
mod <- speedlm(mpg ~ wt + qsec, data = mtcars, fitted = TRUE)

# summarize model fit with tidiers
tidy(mod)
#> # A tibble: 3 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   19.7       5.25       3.76 7.65e- 4
#> 2 wt            -5.05      0.484    -10.4  2.52e-11
#> 3 qsec           0.929     0.265      3.51 1.50e- 3
glance(mod)
#> # A tibble: 1 × 11
#>   r.squared adj.r.squared statistic  p.value    df logLik   AIC   BIC
#>       <dbl>         <dbl>     <dbl>    <dbl> <int>  <dbl> <dbl> <dbl>
#> 1     0.826         0.814      69.0 9.39e-12     3  -74.4  157.  163.
#> # ℹ 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>
augment(mod)
#> # A tibble: 32 × 6
#>    .rownames           mpg    wt  qsec .fitted  .resid
#>    <chr>             <dbl> <dbl> <dbl>   <dbl>   <dbl>
#>  1 Mazda RX4          21    2.62  16.5    21.8 -0.815 
#>  2 Mazda RX4 Wag      21    2.88  17.0    21.0 -0.0482
#>  3 Datsun 710         22.8  2.32  18.6    25.3 -2.53  
#>  4 Hornet 4 Drive     21.4  3.22  19.4    21.6 -0.181 
#>  5 Hornet Sportabout  18.7  3.44  17.0    18.2  0.504 
#>  6 Valiant            18.1  3.46  20.2    21.1 -2.97  
#>  7 Duster 360         14.3  3.57  15.8    16.4 -2.14  
#>  8 Merc 240D          24.4  3.19  20      22.2  2.17  
#>  9 Merc 230           22.8  3.15  22.9    25.1 -2.32  
#> 10 Merc 280           19.2  3.44  18.3    19.4 -0.185 
#> # ℹ 22 more rows