Glance at an mlm object

This function takes an "mlm" object, fit by lm with a multivariate response. The goal is to return something analogous to glance.lm for a univariate response linear model.

Usage

# S3 method for class 'mlm'
glance(x, ...)

Arguments

x

An mlm object created by lm, i.e., with a multivariate response.

...

Additional arguments. Not used.

Value

A tibble with one row for each response variable and the columns:

r.squared

R squared statistic, or the percent of variation explained by the model.

adj.r.squared

Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.

sigma

Estimated standard error of the residuals

fstatitic

Overall F statistic for the model

numdf

Numerator degrees of freedom for the overall test

dendf

Denominator degrees of freedom for the overall test

p.value

P-value corresponding to the F statistic

nobs

Number of observations used

Details

In the multivariate case, it returns a tibble with one row for each response variable, containing goodness of fit measures, F-tests and p-values.

Examples

iris.mod <- lm(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) ~ Species, data=iris)
glance(iris.mod)
#> # A tibble: 4 × 9
#>   response   r.squared adj.r.squared sigma fstatistic numdf dendf  p.value  nobs
#>   <chr>          <dbl>         <dbl> <dbl>      <dbl> <dbl> <dbl>    <dbl> <int>
#> 1 Sepal.Len…     0.619         0.614 0.515      119.      2   147 1.67e-31   150
#> 2 Sepal.Wid…     0.401         0.393 0.340       49.2     2   147 4.49e-17   150
#> 3 Petal.Len…     0.941         0.941 0.430     1180.      2   147 2.86e-91   150
#> 4 Petal.Wid…     0.929         0.928 0.205      960.      2   147 4.17e-85   150