This function creates a data frame of factor loadings from
a factor analysis. It can be on an object that was created with
psych::fa() or it can be used on a data frame. If used on a
data frame, it will run it on all columns in the data frame. Also
works on grouped data frame if you want to check how the factor
loadings may change along the different levels of a group.
Usage
get_loadings(
data,
cols,
group,
labels = NULL,
threshold = 0.4,
print = "short",
nfactors = 1,
fm = "pa",
rotate = "oblimin",
sort = TRUE,
decimals = 3,
na.rm = FALSE
)Arguments
- data
Either a data created using
psych::fa()or a data.frame- cols
<
tidy-select> The variables you want to get the correlations for.- group
<
tidy-select> A selection of columns to group the data by in addition totreats. This operates very similarly to.byfrom dplyr (for more info on that see ?dplyr_by). See examples to see how it operates.- labels
Either a character vector or data frame. Creates a new column called "labels" for each variable in the factor analysis.
- threshold
The threshold with which to not show the factor loadings. Default is 0.4.
The printing method. Default is "short" which only prints a dataframe of the factor loadings. The alternative is "long" but that has not been created yet.
- nfactors
Number of factors to extract, default is 1.
- fm
Factoring method used in the factor analysis. Default is "pa". For more information on the various methods see the
fmargument in thepsych::fa()function.- rotate
The type of factor rotation to perform when conducting the factor analysis. Default is "oblimin". For more information on the different rotation methods available, check the documentation for the
rotateargument inpsych::fa().- sort
Logical. If
TRUE, the default, the loadings are sorted largest to smallest. IfFALSE, no sorting occurs.- decimals
Number of decimals each number should be rounded to. Default is 3.
- na.rm
Logical. Default is
TRUEwhich removes NAs prior to calculation.
Examples
library(dplyr)
library(psych)
# first lets get our data set
data <- test_data %>%
select(top:run)
# now create the fa object
model <- fa(data, nfactors = 1, fm = "pa", rotate = "oblimin")
# get just the loadings
get_loadings(model)
#> # A tibble: 9 × 4
#> variables PA1 communality uniqueness
#> * <chr> <dbl> <dbl> <dbl>
#> 1 controlled 0.685 0.469 0.531
#> 2 small 0.654 0.428 0.572
#> 3 run 0.61 0.373 0.627
#> 4 top NA 0.086 0.914
#> 5 inferior NA 0.122 0.878
#> 6 dominate NA 0.021 0.979
#> 7 deserving NA 0.001 0.999
#> 8 special NA 0.088 0.912
#> 9 harder NA 0.069 0.931
# get the loadings with the variable labels based on data object
get_loadings(model, data)
#> `cols` is only valid when supplying an object of class <data.frame>
#> You've supplied an object with class <psych/fa>
#> # A tibble: 9 × 4
#> variables PA1 communality uniqueness
#> * <chr> <dbl> <dbl> <dbl>
#> 1 controlled 0.685 0.469 0.531
#> 2 small 0.654 0.428 0.572
#> 3 run 0.61 0.373 0.627
#> 4 top NA 0.086 0.914
#> 5 inferior NA 0.122 0.878
#> 6 dominate NA 0.021 0.979
#> 7 deserving NA 0.001 0.999
#> 8 special NA 0.088 0.912
#> 9 harder NA 0.069 0.931
# we can do all of this in one step with pipes
test_data %>%
# select only the variables we want in the factor analysis
select(top:run) %>%
# run the factor analysis
fa(., nfactors = 1, fm = "pa", rotate = "oblimin") %>%
# get the loadings
get_loadings()
#> # A tibble: 9 × 4
#> variables PA1 communality uniqueness
#> * <chr> <dbl> <dbl> <dbl>
#> 1 controlled 0.685 0.469 0.531
#> 2 small 0.654 0.428 0.572
#> 3 run 0.61 0.373 0.627
#> 4 top NA 0.086 0.914
#> 5 inferior NA 0.122 0.878
#> 6 dominate NA 0.021 0.979
#> 7 deserving NA 0.001 0.999
#> 8 special NA 0.088 0.912
#> 9 harder NA 0.069 0.931
# Now let's remove the threshold for the loadings and include labels
test_data %>%
# select only the variables we want in the factor analysis
select(top:run) %>%
# run the factor analysis
fa(., nfactors = 1, fm = "pa", rotate = "oblimin") %>%
# specify threshold is 0
get_loadings(threshold = 0, labels = data)
#> # A tibble: 9 × 5
#> variables labels PA1 communality uniqueness
#> * <chr> <chr> <dbl> <dbl> <dbl>
#> 1 controlled "Much of our lives are being control… 0.685 0.469 0.531
#> 2 small "Even though we live in a democracy,… 0.654 0.428 0.572
#> 3 run "The people who really \"run\" the c… 0.61 0.373 0.627
#> 4 inferior "Some groups of people are simply in… 0.35 0.122 0.878
#> 5 special "It is unfair for some groups in soc… 0.297 0.088 0.912
#> 6 top "An ideal society requires some grou… 0.294 0.086 0.914
#> 7 harder "I have a harder time succeeding tha… 0.262 0.069 0.931
#> 8 dominate "No one group should dominate in soc… 0.146 0.021 0.979
#> 9 deserving "Groups at the bottom are just as de… -0.033 0.001 0.999
# alternatively, we could skip the fa step entirely like so
test_data %>%
# select only the variables we want in the factor analysis
select(top:run) %>%
# specify number of factors, rotation, and factor method
get_loadings()
#> # A tibble: 9 × 5
#> variables labels PA1 communality uniqueness
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 controlled "Much of our lives are being control… 0.685 0.469 0.531
#> 2 small "Even though we live in a democracy,… 0.654 0.428 0.572
#> 3 run "The people who really \"run\" the c… 0.61 0.373 0.627
#> 4 top "An ideal society requires some grou… NA 0.086 0.914
#> 5 inferior "Some groups of people are simply in… NA 0.122 0.878
#> 6 dominate "No one group should dominate in soc… NA 0.021 0.979
#> 7 deserving "Groups at the bottom are just as de… NA 0.001 0.999
#> 8 special "It is unfair for some groups in soc… NA 0.088 0.912
#> 9 harder "I have a harder time succeeding tha… NA 0.069 0.931
# we can also specify the number of factors, rotation, and factoring method
test_data %>%
# select only the variables we want in the factor analysis
select(top:run) %>%
# specify number of factors, rotation, factor method, and threshold
get_loadings(nfactors = 2, rotate = "varimax", fm = "minres", threshold = 0.2)
#> # A tibble: 9 × 6
#> variables labels MR1 MR2 communality uniqueness
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 run "The people who really \"run\… 0.692 NA 0.503 0.497
#> 2 small "Even though we live in a dem… 0.656 NA 0.43 0.57
#> 3 controlled "Much of our lives are being … 0.642 NA 0.425 0.575
#> 4 special "It is unfair for some groups… 0.294 NA 0.089 0.911
#> 5 inferior "Some groups of people are si… 0.292 0.646 0.503 0.497
#> 6 dominate "No one group should dominate… 0.263 -0.415 0.241 0.759
#> 7 harder "I have a harder time succeed… 0.254 NA 0.065 0.935
#> 8 top "An ideal society requires so… 0.225 0.587 0.395 0.605
#> 9 deserving "Groups at the bottom are jus… NA -0.429 0.187 0.813
# we can also calculate the factor loadings by a grouping variable
test_data %>%
# select the grouping variable and the variables to be used in factor analysis
select(edu_f2, top:run) %>%
# group the data
group_by(edu_f2) %>%
# specify number of factors, and the threshold
get_loadings(nfactors = 2, threshold = 0.2)
#> Loading required namespace: GPArotation
#> # A tibble: 18 × 7
#> edu_f2 variables labels PA1 PA2 communality uniqueness
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 No College Degree run "The … 0.73 NA 0.526 0.474
#> 2 No College Degree small "Even… 0.661 NA 0.454 0.546
#> 3 No College Degree controll… "Much… 0.569 0.22 0.402 0.598
#> 4 No College Degree dominate "No o… 0.484 -0.329 0.304 0.696
#> 5 No College Degree harder "I ha… 0.287 NA 0.106 0.894
#> 6 No College Degree special "It i… 0.218 NA 0.096 0.904
#> 7 No College Degree inferior "Some… NA 0.687 0.48 0.52
#> 8 No College Degree top "An i… NA 0.639 0.415 0.585
#> 9 No College Degree deserving "Grou… NA -0.489 0.236 0.764
#> 10 At Least a Bachelor's … run "The … 0.654 NA 0.413 0.587
#> 11 At Least a Bachelor's … small "Even… 0.637 NA 0.429 0.571
#> 12 At Least a Bachelor's … controll… "Much… 0.633 NA 0.457 0.543
#> 13 At Least a Bachelor's … special "It i… 0.336 NA 0.112 0.888
#> 14 At Least a Bachelor's … deserving "Grou… 0.298 -0.433 0.229 0.771
#> 15 At Least a Bachelor's … inferior "Some… 0.252 0.561 0.431 0.569
#> 16 At Least a Bachelor's … dominate "No o… 0.228 -0.53 0.289 0.711
#> 17 At Least a Bachelor's … top "An i… NA 0.557 0.367 0.633
#> 18 At Least a Bachelor's … harder "I ha… NA NA 0.022 0.978
# let's repeat the previous analysis, but use internal arguments to select the
# columns to include in the factor analysis and the grouping variables
test_data %>%
# group indicates we want to run the factor analysis across each level in edu_f2
# nfactors specifies we want two factors
# threshold sets the cut off for the loadings
get_loadings(
# specify the variables to include in the factor analysis
cols = c(top:run),
# specify the group variable, run separate factor analyses for each level
group = edu_f2,
# specify two factors
nfactors = 2,
# specify the loadings are cut off at |0.2|
threshold = 0.2
)
#> # A tibble: 18 × 7
#> edu_f2 variables labels PA1 PA2 communality uniqueness
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 No College Degree run "The … 0.73 NA 0.526 0.474
#> 2 No College Degree small "Even… 0.661 NA 0.454 0.546
#> 3 No College Degree controll… "Much… 0.569 0.22 0.402 0.598
#> 4 No College Degree dominate "No o… 0.484 -0.329 0.304 0.696
#> 5 No College Degree harder "I ha… 0.287 NA 0.106 0.894
#> 6 No College Degree special "It i… 0.218 NA 0.096 0.904
#> 7 No College Degree inferior "Some… NA 0.687 0.48 0.52
#> 8 No College Degree top "An i… NA 0.639 0.415 0.585
#> 9 No College Degree deserving "Grou… NA -0.489 0.236 0.764
#> 10 At Least a Bachelor's … run "The … 0.654 NA 0.413 0.587
#> 11 At Least a Bachelor's … small "Even… 0.637 NA 0.429 0.571
#> 12 At Least a Bachelor's … controll… "Much… 0.633 NA 0.457 0.543
#> 13 At Least a Bachelor's … special "It i… 0.336 NA 0.112 0.888
#> 14 At Least a Bachelor's … deserving "Grou… 0.298 -0.433 0.229 0.771
#> 15 At Least a Bachelor's … inferior "Some… 0.252 0.561 0.431 0.569
#> 16 At Least a Bachelor's … dominate "No o… 0.228 -0.53 0.289 0.711
#> 17 At Least a Bachelor's … top "An i… NA 0.557 0.367 0.633
#> 18 At Least a Bachelor's … harder "I ha… NA NA 0.022 0.978