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

Arguments

x

A summary.lm object created by stats::summary.lm().

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.

Details

The tidy.summary.lm() method is a potentially useful alternative to tidy.lm(). For instance, if users have already converted large lm objects into their leaner summary.lm equivalents to conserve memory.

See also

tidy(), stats::summary.lm()

Other lm tidiers: augment.glm(), augment.lm(), glance.glm(), glance.lm(), glance.summary.lm(), glance.svyglm(), tidy.glm(), tidy.lm(), tidy.lm.beta(), tidy.mlm()

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

# fit model
mod <- lm(mpg ~ wt + qsec, data = mtcars)
modsumm <- summary(mod)

# summarize model fit with tidiers
tidy(mod, conf.int = TRUE)
#> # A tibble: 3 × 7
#>   term        estimate std.error statistic  p.value conf.low conf.high
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 (Intercept)   19.7       5.25       3.76 7.65e- 4    9.00      30.5 
#> 2 wt            -5.05      0.484    -10.4  2.52e-11   -6.04      -4.06
#> 3 qsec           0.929     0.265      3.51 1.50e- 3    0.387      1.47

# equivalent to the above
tidy(modsumm, conf.int = TRUE)
#> # A tibble: 3 × 7
#>   term        estimate std.error statistic  p.value conf.low conf.high
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 (Intercept)   19.7       5.25       3.76 7.65e- 4    9.00      30.5 
#> 2 wt            -5.05      0.484    -10.4  2.52e-11   -6.04      -4.06
#> 3 qsec           0.929     0.265      3.51 1.50e- 3    0.387      1.47

glance(mod)
#> # A tibble: 1 × 12
#>   r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC
#>       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>  <dbl> <dbl>
#> 1     0.826         0.814  2.60      69.0 9.39e-12     2  -74.4  157.
#> # ℹ 4 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>,
#> #   nobs <int>

# mostly the same, except for a few missing columns
glance(modsumm)
#> # A tibble: 1 × 8
#>   r.squared adj.r.squared sigma statistic  p.value    df df.residual
#>       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>       <int>
#> 1     0.826         0.814  2.60      69.0 9.39e-12     2          29
#> # ℹ 1 more variable: nobs <dbl>