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 'factanal'
tidy(x, ...)Arguments
- x
A
factanalobject created bystats::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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
See also
Other factanal tidiers:
augment.factanal(),
glance.factanal()
Value
A tibble::tibble() with columns:
- variable
Variable under consideration.
- uniqueness
Proportion of residual, or unexplained variance
- flX
Factor loading for level X.
Examples
set.seed(123)
# generate data
library(dplyr)
library(purrr)
m1 <- tibble(
v1 = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 4, 5, 6),
v2 = c(1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 3, 4, 3, 3, 3, 4, 6, 5),
v3 = c(3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 4, 6),
v4 = c(3, 3, 4, 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 5, 6, 4),
v5 = c(1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 6, 4, 5),
v6 = c(1, 1, 1, 2, 1, 3, 3, 3, 4, 3, 1, 1, 1, 2, 1, 6, 5, 4)
)
# new data
m2 <- map_dfr(m1, rev)
# factor analysis objects
fit1 <- factanal(m1, factors = 3, scores = "Bartlett")
fit2 <- factanal(m1, factors = 3, scores = "regression")
# tidying the object
tidy(fit1)
#> # A tibble: 6 × 5
#> variable uniqueness fl1 fl2 fl3
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 v1 0.005 0.944 0.182 0.267
#> 2 v2 0.101 0.905 0.235 0.159
#> 3 v3 0.005 0.236 0.210 0.946
#> 4 v4 0.224 0.180 0.242 0.828
#> 5 v5 0.0843 0.242 0.881 0.286
#> 6 v6 0.005 0.193 0.959 0.196
tidy(fit2)
#> # A tibble: 6 × 5
#> variable uniqueness fl1 fl2 fl3
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 v1 0.005 0.944 0.182 0.267
#> 2 v2 0.101 0.905 0.235 0.159
#> 3 v3 0.005 0.236 0.210 0.946
#> 4 v4 0.224 0.180 0.242 0.828
#> 5 v5 0.0843 0.242 0.881 0.286
#> 6 v6 0.005 0.193 0.959 0.196
# augmented dataframe
augment(fit1)
#> # A tibble: 18 × 4
#> .rownames .fs1 .fs2 .fs3
#> <chr> <dbl> <dbl> <dbl>
#> 1 1 -0.904 -0.931 0.948
#> 2 2 -0.869 -0.933 0.935
#> 3 3 -0.908 -0.932 0.962
#> 4 4 -1.00 -0.253 0.818
#> 5 5 -0.904 -0.931 0.948
#> 6 6 -0.745 0.727 -0.788
#> 7 7 -0.710 0.725 -0.801
#> 8 8 -0.750 0.726 -0.774
#> 9 9 -0.808 1.40 -0.930
#> 10 10 -0.745 0.727 -0.788
#> 11 11 0.927 -0.931 -0.837
#> 12 12 0.963 -0.933 -0.849
#> 13 13 0.923 -0.932 -0.823
#> 14 14 0.829 -0.253 -0.967
#> 15 15 0.927 -0.931 -0.837
#> 16 16 0.422 2.05 1.29
#> 17 17 1.47 1.29 0.545
#> 18 18 1.88 0.309 1.95
augment(fit2)
#> # A tibble: 18 × 4
#> .rownames .fs1 .fs2 .fs3
#> <chr> <dbl> <dbl> <dbl>
#> 1 1 -0.897 -0.925 0.936
#> 2 2 -0.861 -0.927 0.924
#> 3 3 -0.901 -0.926 0.950
#> 4 4 -0.993 -0.251 0.809
#> 5 5 -0.897 -0.925 0.936
#> 6 6 -0.741 0.720 -0.784
#> 7 7 -0.706 0.718 -0.796
#> 8 8 -0.745 0.719 -0.770
#> 9 9 -0.803 1.39 -0.923
#> 10 10 -0.741 0.720 -0.784
#> 11 11 0.917 -0.925 -0.830
#> 12 12 0.952 -0.927 -0.842
#> 13 13 0.913 -0.926 -0.816
#> 14 14 0.820 -0.252 -0.958
#> 15 15 0.917 -0.925 -0.830
#> 16 16 0.426 2.04 1.28
#> 17 17 1.46 1.29 0.548
#> 18 18 1.88 0.314 1.95
# augmented dataframe (with new data)
augment(fit1, data = m2)
#> # A tibble: 18 × 10
#> .rownames v1 v2 v3 v4 v5 v6 .fs1 .fs2 .fs3
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 6 5 6 4 5 4 -0.904 -0.931 0.948
#> 2 2 5 6 4 6 4 5 -0.869 -0.933 0.935
#> 3 3 4 4 5 5 6 6 -0.908 -0.932 0.962
#> 4 4 3 3 1 1 1 1 -1.00 -0.253 0.818
#> 5 5 3 3 1 1 1 2 -0.904 -0.931 0.948
#> 6 6 3 3 1 2 1 1 -0.745 0.727 -0.788
#> 7 7 3 4 1 1 1 1 -0.710 0.725 -0.801
#> 8 8 3 3 1 1 1 1 -0.750 0.726 -0.774
#> 9 9 1 1 1 1 3 3 -0.808 1.40 -0.930
#> 10 10 1 2 1 1 3 4 -0.745 0.727 -0.788
#> 11 11 1 1 1 2 3 3 0.927 -0.931 -0.837
#> 12 12 1 2 1 1 3 3 0.963 -0.933 -0.849
#> 13 13 1 1 1 1 3 3 0.923 -0.932 -0.823
#> 14 14 1 1 3 3 1 1 0.829 -0.253 -0.967
#> 15 15 1 1 3 3 1 2 0.927 -0.931 -0.837
#> 16 16 1 1 3 4 1 1 0.422 2.05 1.29
#> 17 17 1 2 3 3 1 1 1.47 1.29 0.545
#> 18 18 1 1 3 3 1 1 1.88 0.309 1.95
augment(fit2, data = m2)
#> # A tibble: 18 × 10
#> .rownames v1 v2 v3 v4 v5 v6 .fs1 .fs2 .fs3
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 6 5 6 4 5 4 -0.897 -0.925 0.936
#> 2 2 5 6 4 6 4 5 -0.861 -0.927 0.924
#> 3 3 4 4 5 5 6 6 -0.901 -0.926 0.950
#> 4 4 3 3 1 1 1 1 -0.993 -0.251 0.809
#> 5 5 3 3 1 1 1 2 -0.897 -0.925 0.936
#> 6 6 3 3 1 2 1 1 -0.741 0.720 -0.784
#> 7 7 3 4 1 1 1 1 -0.706 0.718 -0.796
#> 8 8 3 3 1 1 1 1 -0.745 0.719 -0.770
#> 9 9 1 1 1 1 3 3 -0.803 1.39 -0.923
#> 10 10 1 2 1 1 3 4 -0.741 0.720 -0.784
#> 11 11 1 1 1 2 3 3 0.917 -0.925 -0.830
#> 12 12 1 2 1 1 3 3 0.952 -0.927 -0.842
#> 13 13 1 1 1 1 3 3 0.913 -0.926 -0.816
#> 14 14 1 1 3 3 1 1 0.820 -0.252 -0.958
#> 15 15 1 1 3 3 1 2 0.917 -0.925 -0.830
#> 16 16 1 1 3 4 1 1 0.426 2.04 1.28
#> 17 17 1 2 3 3 1 1 1.46 1.29 0.548
#> 18 18 1 1 3 3 1 1 1.88 0.314 1.95