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 cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for factanal tidy(x, ...)
x | A |
---|---|
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Other factanal tidiers: augment.factanal
,
glance.factanal
A tibble::tibble()
with columns:
Factor loading of term on factor X. There will be as many columns of this format as there were factors fitted.
Needs custom info.
Proportion of residual, or unexplained variance
#> # 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 TRUEtidy(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 0.719augment(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#> 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>