Tidy a(n) factanal 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 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, ...)

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.

Value

A tibble::tibble with one row for each variable used in the analysis and columns:

variable

The variable being estimated in the factor analysis

uniqueness

Proportion of residual, or unexplained variance

flX

Factor loading of term on factor X. There will be as many columns of this format as there were factors fitted.

See also

tidy(), stats::factanal()

Other factanal tidiers: augment.factanal, glance.factanal

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>