`R/factor_analysis.R`

, `R/principal_components.R`

, `R/utils_pca_efa.R`

`principal_components.Rd`

The functions `principal_components()`

and `factor_analysis()`

can
be used to perform a principal component analysis (PCA) or a factor analysis
(FA). They return the loadings as a data frame, and various methods and
functions are available to access / display other information (see the
Details section).

```
factor_analysis(
x,
n = "auto",
rotation = "none",
sort = FALSE,
threshold = NULL,
standardize = TRUE,
cor = NULL,
...
)
principal_components(
x,
n = "auto",
rotation = "none",
sort = FALSE,
threshold = NULL,
standardize = TRUE,
...
)
closest_component(pca_results)
rotated_data(pca_results)
# S3 method for parameters_efa
predict(object, newdata = NULL, names = NULL, keep_na = TRUE, ...)
# S3 method for parameters_efa
print(x, digits = 2, sort = FALSE, threshold = NULL, labels = NULL, ...)
# S3 method for parameters_efa
sort(x, ...)
```

x | A data frame or a statistical model. |
---|---|

n | Number of components to extract. If |

rotation | If not |

sort | Sort the loadings. |

threshold | A value between 0 and 1 indicates which (absolute) values
from the loadings should be removed. An integer higher than 1 indicates the
n strongest loadings to retain. Can also be |

standardize | A logical value indicating whether the variables should be standardized (centered and scaled) to have unit variance before the analysis (in general, such scaling is advisable). |

cor | An optional correlation matrix that can be used (note that the
data must still be passed as the first argument). If |

... | Arguments passed to or from other methods. |

pca_results | The output of the |

object | An object of class |

newdata | An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. |

names | Optional character vector to name columns of the returned data frame. |

keep_na | Logical, if |

digits, labels | Arguments for |

A data frame of loadings.

`n_components`

and`n_factors`

automatically estimate the optimal number of dimensions to retain.`check_factorstructure`

checks the suitability of the data for factor analysis using the`sphericity`

and the`sphericity`

KMO measure.`check_itemscale`

computes various measures of internal consistencies applied to the (sub)scales (i.e., components) extracted from the PCA.Running

`summary`

returns information related to each component/factor, such as the explained variance and the Eivenvalues.Running

`get_scores`

computes scores for each subscale.Running

`closest_component`

will return a numeric vector with the assigned component index for each column from the original data frame.Running

`rotated_data`

will return the rotated data, including missing values, so it matches the original data frame.Running

`plot()`

visually displays the loadings (that requires the see package to work).

Complexity represents the number of latent components needed to account for the observed variables. Whereas a perfect simple structure solution has a complexity of 1 in that each item would only load on one factor, a solution with evenly distributed items has a complexity greater than 1 (Hofman, 1978; Pettersson and Turkheimer, 2010) .

Uniqueness represents the variance that is 'unique' to the variable and
not shared with other variables. It is equal to `1 – communality`

(variance that is shared with other variables). A uniqueness of `0.20`

suggests that 20% or that variable's variance is not shared with other
variables in the overall factor model. The greater 'uniqueness' the lower
the relevance of the variable in the factor model.

MSA represents the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (Kaiser and Rice, 1974) for each item. It indicates whether there is enough data for each factor give reliable results for the PCA. The value should be > 0.6, and desirable values are > 0.8 (Tabachnick and Fidell, 2013).

There is a simplified rule of thumb that may help do decide whether to run a factor analysis or a principal component analysis:

Run

*factor analysis*if you assume or wish to test a theoretical model of*latent factors*causing observed variables.Run

*principal component analysis*If you want to simply*reduce*your correlated observed variables to a smaller set of important independent composite variables.

(Source: CrossValidated)

Use `get_scores`

to compute scores for the "subscales"
represented by the extracted principal components. `get_scores()`

takes the results from `principal_components()`

and extracts the
variables for each component found by the PCA. Then, for each of these
"subscales", raw means are calculated (which equals adding up the single
items and dividing by the number of items). This results in a sum score
for each component from the PCA, which is on the same scale as the
original, single items that were used to compute the PCA.
One can also use `predict()`

to back-predict scores for each component,
to which one can provide `newdata`

or a vector of `names`

for the
components.

Use `summary()`

to get the Eigenvalues and the explained variance
for each extracted component. The eigenvectors and eigenvalues represent
the "core" of a PCA: The eigenvectors (the principal components)
determine the directions of the new feature space, and the eigenvalues
determine their magnitude. In other words, the eigenvalues explain the
variance of the data along the new feature axes.

Kaiser, H.F. and Rice. J. (1974). Little jiffy, mark iv. Educational and Psychological Measurement, 34(1):111–117

Hofmann, R. (1978). Complexity and simplicity as objective indices descriptive of factor solutions. Multivariate Behavioral Research, 13:2, 247-250, doi: 10.1207/s15327906mbr1302_9

Pettersson, E., & Turkheimer, E. (2010). Item selection, evaluation, and simple structure in personality data. Journal of research in personality, 44(4), 407-420, doi: 10.1016/j.jrp.2010.03.002

Tabachnick, B. G., and Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Pearson Education.

```
library(parameters)
# \donttest{
# Principal Component Analysis (PCA) -------------------
if (require("psych")) {
principal_components(mtcars[, 1:7], n = "all", threshold = 0.2)
principal_components(mtcars[, 1:7],
n = 2, rotation = "oblimin",
threshold = "max", sort = TRUE
)
principal_components(mtcars[, 1:7], n = 2, threshold = 2, sort = TRUE)
pca <- principal_components(mtcars[, 1:5], n = 2, rotation = "varimax")
pca # Print loadings
summary(pca) # Print information about the factors
predict(pca, names = c("Component1", "Component2")) # Back-predict scores
# which variables from the original data belong to which extracted component?
closest_component(pca)
# rotated_data(pca) # TODO: doesn't work
}
#> mpg cyl disp hp drat
#> 1 1 1 1 2
# Automated number of components
principal_components(mtcars[, 1:4], n = "auto")
#> # Loadings from Principal Component Analysis (no rotation)
#>
#> Variable | PC1 | Complexity
#> -----------------------------
#> mpg | -0.93 | 1.00
#> cyl | 0.96 | 1.00
#> disp | 0.95 | 1.00
#> hp | 0.91 | 1.00
#>
#> The unique principal component accounted for 87.55% of the total variance of the original data.
#>
# }
# Factor Analysis (FA) ------------------------
if (require("psych")) {
factor_analysis(mtcars[, 1:7], n = "all", threshold = 0.2)
factor_analysis(mtcars[, 1:7], n = 2, rotation = "oblimin", threshold = "max", sort = TRUE)
factor_analysis(mtcars[, 1:7], n = 2, threshold = 2, sort = TRUE)
efa <- factor_analysis(mtcars[, 1:5], n = 2)
summary(efa)
predict(efa)
# \donttest{
# Automated number of components
factor_analysis(mtcars[, 1:4], n = "auto")
# }
}
#> Warning: Could not retrieve information about missing data. Returning only complete
#> cases.
#> # Loadings from Factor Analysis (no rotation)
#>
#> Variable | MR1 | Complexity | Uniqueness
#> ------------------------------------------
#> mpg | -0.90 | 1.00 | 0.19
#> cyl | 0.96 | 1.00 | 0.08
#> disp | 0.93 | 1.00 | 0.13
#> hp | 0.86 | 1.00 | 0.26
#>
#> The unique latent factor accounted for 83.55% of the total variance of the original data.
#>
```