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 modelling function. This includes the name of the modelling function or any arguments passed to the modelling 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.

# S3 method for factanal
glance(x, ...)

Arguments

x

A factanal object created by stats::factanal().

...

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. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also

Value

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

converged

Logical indicating if the model fitting procedure was succesful and converged.

df

Degrees of freedom used by the model.

method

Needs custom info.

n

Needs custom info.

n.factors

The number of fitted factors

statistic

Needs custom info.

total.variance

Total cumulative proportion of variance accounted for by all factors

Examples

mod <- factanal(mtcars, 3, scores = "regression") glance(mod)
#> # A tibble: 1 x 8 #> n.factors total.variance statistic p.value df n method converged #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr> <lgl> #> 1 3 0.862 30.5 0.205 25 32 mle TRUE
tidy(mod)
#> # A tibble: 11 x 5 #> variable uniqueness fl1 fl2 fl3 #> * <chr> <dbl> <dbl> <dbl> <dbl> #> 1 mpg 0.135 0.643 -0.478 -0.473 #> 2 cyl 0.0555 -0.618 0.703 0.261 #> 3 disp 0.0898 -0.719 0.537 0.323 #> 4 hp 0.127 -0.291 0.725 0.513 #> 5 drat 0.290 0.804 -0.241 -0.0684 #> 6 wt 0.0596 -0.778 0.248 0.524 #> 7 qsec 0.0515 -0.177 -0.946 -0.151 #> 8 vs 0.223 0.295 -0.805 -0.204 #> 9 am 0.208 0.880 0.0884 -0.0927 #> 10 gear 0.125 0.908 0.0211 0.224 #> 11 carb 0.158 0.114 0.559 0.719
augment(mod)
#> # A tibble: 32 x 4 #> .rownames .fs1 .fs2 .fs3 #> * <fct> <dbl> <dbl> <dbl> #> 1 Mazda RX4 0.847 0.672 -0.278 #> 2 Mazda RX4 Wag 0.722 0.384 0.0246 #> 3 Datsun 710 0.686 -0.592 -0.564 #> 4 Hornet 4 Drive -0.866 -0.673 -0.767 #> 5 Hornet Sportabout -0.893 0.862 -1.01 #> 6 Valiant -1.06 -1.07 -0.383 #> 7 Duster 360 -0.559 1.24 -0.199 #> 8 Merc 240D 0.0774 -1.50 0.409 #> 9 Merc 230 -0.242 -2.61 1.23 #> 10 Merc 280 0.183 -0.591 0.910 #> # ... with 22 more rows
augment(mod, mtcars)
#> Warning: Column `.rownames` joining factor and character vector, coercing into character vector
#> # A tibble: 32 x 15 #> .rownames mpg cyl disp hp drat wt qsec vs am gear carb #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 Mazda RX… 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 Datsun 7… 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 Hornet 4… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 Hornet S… 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 Duster 3… 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # ... with 22 more rows, and 3 more variables: .fs1 <dbl>, .fs2 <dbl>, #> # .fs3 <dbl>