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Parameters from PCA, FA, CFA, SEM

Source: R/methods_lavaan.R, R/methods_psych.R
model_parameters.principal.Rd

Format structural models from the psych or FactoMineR packages.

Usage

# S3 method for class 'lavaan'
model_parameters(
  model,
  ci = 0.95,
  standardize = FALSE,
  component = c("regression", "correlation", "loading", "defined"),
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

# S3 method for class 'principal'
model_parameters(
  model,
  sort = FALSE,
  threshold = NULL,
  labels = NULL,
  verbose = TRUE,
  ...
)

Arguments

model

Model object.

ci

Confidence Interval (CI) level. Default to 0.95 (95%).

standardize

Return standardized parameters (standardized coefficients). Can be TRUE (or "all" or "std.all") for standardized estimates based on both the variances of observed and latent variables; "latent" (or "std.lv") for standardized estimates based on the variances of the latent variables only; or "no_exogenous" (or "std.nox") for standardized estimates based on both the variances of observed and latent variables, but not the variances of exogenous covariates. See lavaan::standardizedsolution for details.

component

What type of links to return. Can be "all" or some of c("regression", "correlation", "loading", "variance", "mean").

keep

Character containing a regular expression pattern that describes the parameters that should be included (for keep) or excluded (for drop) in the returned data frame. keep may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If keep is a character vector, every parameter name in the "Parameter" column that matches the regular expression in keep will be selected from the returned data frame (and vice versa, all parameter names matching drop will be excluded). Furthermore, if keep has more than one element, these will be merged with an OR operator into a regular expression pattern like this: "(one|two|three)". If keep is a named list of regular expression patterns, the names of the list-element should equal the column name where selection should be applied. This is useful for model objects where model_parameters() returns multiple columns with parameter components, like in model_parameters.lavaan(). Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the $Parameter column of the parameters table to get the exact parameter names.

drop

See keep.

verbose

Toggle warnings.

...

Arguments passed to or from other methods.

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 "max", in which case it will only display the maximum loading per variable (the most simple structure).

labels

A character vector containing labels to be added to the loadings data. Usually, the question related to the item.

Value

A data frame of indices or loadings.

Details

For the structural models obtained with psych, the following indices are present:

  • Complexity (Hoffman's, 1978; Pettersson and Turkheimer, 2010) 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.

  • 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).

Note

There is also a plot()-method for lavaan models implemented in the see-package.

References

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

  • Pettersson, E., and Turkheimer, E. (2010). Item selection, evaluation, and simple structure in personality data. Journal of research in personality, 44(4), 407-420.

  • Revelle, W. (2016). How To: Use the psych package for Factor Analysis and data reduction.

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

  • Rosseel Y (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.

  • Merkle EC , Rosseel Y (2018). blavaan: Bayesian Structural Equation Models via Parameter Expansion. Journal of Statistical Software, 85(4), 1-30. http://www.jstatsoft.org/v85/i04/

Examples

# \donttest{
library(parameters)
if (require("psych", quietly = TRUE)) {
  # Principal Component Analysis (PCA) ---------
  pca <- psych::principal(attitude)
  model_parameters(pca)

  pca <- psych::principal(attitude, nfactors = 3, rotate = "none")
  model_parameters(pca, sort = TRUE, threshold = 0.2)

  principal_components(attitude, n = 3, sort = TRUE, threshold = 0.2)


  # Exploratory Factor Analysis (EFA) ---------
  efa <- psych::fa(attitude, nfactors = 3)
  model_parameters(efa,
    threshold = "max", sort = TRUE,
    labels = as.character(1:ncol(attitude))
  )


  # Omega ---------
  omega <- psych::omega(mtcars, nfactors = 3)
  params <- model_parameters(omega)
  params
  summary(params)
}

#> Composite | Total Variance (%) | Variance due to General Factor (%) | Variance due to Group Factor (%)
#> ------------------------------------------------------------------------------------------------------
#> g         |              97.28 |                              56.64 |                            26.42
#> F1*       |              90.12 |                              31.07 |                            59.05
#> F2*       |              91.37 |                              69.32 |                            22.04
#> F3*       |              87.36 |                              59.65 |                            27.71
# }

# lavaan

library(parameters)

# lavaan -------------------------------------
if (require("lavaan", quietly = TRUE)) {
  # Confirmatory Factor Analysis (CFA) ---------

  structure <- " visual  =~ x1 + x2 + x3
                 textual =~ x4 + x5 + x6
                 speed   =~ x7 + x8 + x9 "
  model <- lavaan::cfa(structure, data = HolzingerSwineford1939)
  model_parameters(model)
  model_parameters(model, standardize = TRUE)

  # filter parameters
  model_parameters(
    model,
    parameters = list(
      To = "^(?!visual)",
      From = "^(?!(x7|x8))"
    )
  )

  # Structural Equation Model (SEM) ------------

  structure <- "
    # latent variable definitions
      ind60 =~ x1 + x2 + x3
      dem60 =~ y1 + a*y2 + b*y3 + c*y4
      dem65 =~ y5 + a*y6 + b*y7 + c*y8
    # regressions
      dem60 ~ ind60
      dem65 ~ ind60 + dem60
    # residual correlations
      y1 ~~ y5
      y2 ~~ y4 + y6
      y3 ~~ y7
      y4 ~~ y8
      y6 ~~ y8
  "
  model <- lavaan::sem(structure, data = PoliticalDemocracy)
  model_parameters(model)
  model_parameters(model, standardize = TRUE)
}
#> # Loading 
#> 
#> Link            | Coefficient |   SE |       95% CI |     z |      p
#> --------------------------------------------------------------------
#> ind60 =~ x1     |        0.92 | 0.02 | [0.88, 0.97] | 40.08 | < .001
#> ind60 =~ x2     |        0.97 | 0.02 | [0.94, 1.01] | 59.14 | < .001
#> ind60 =~ x3     |        0.87 | 0.03 | [0.81, 0.93] | 28.09 | < .001
#> dem60 =~ y1     |        0.85 | 0.04 | [0.77, 0.93] | 20.92 | < .001
#> dem60 =~ y2 (a) |        0.69 | 0.06 | [0.57, 0.81] | 11.58 | < .001
#> dem60 =~ y3 (b) |        0.76 | 0.05 | [0.66, 0.86] | 14.70 | < .001
#> dem60 =~ y4 (c) |        0.84 | 0.04 | [0.76, 0.92] | 20.12 | < .001
#> dem65 =~ y5     |        0.82 | 0.04 | [0.73, 0.90] | 18.52 | < .001
#> dem65 =~ y6 (a) |        0.75 | 0.05 | [0.65, 0.86] | 14.01 | < .001
#> dem65 =~ y7 (b) |        0.80 | 0.05 | [0.71, 0.89] | 17.40 | < .001
#> dem65 =~ y8 (c) |        0.83 | 0.04 | [0.75, 0.91] | 19.79 | < .001
#> 
#> # Regression 
#> 
#> Link          | Coefficient |   SE |       95% CI |     z |      p
#> ------------------------------------------------------------------
#> dem60 ~ ind60 |        0.45 | 0.10 | [0.25, 0.65] |  4.33 | < .001
#> dem65 ~ ind60 |        0.19 | 0.07 | [0.05, 0.33] |  2.64 | 0.008 
#> dem65 ~ dem60 |        0.88 | 0.05 | [0.78, 0.98] | 17.24 | < .001
#> 
#> # Correlation 
#> 
#> Link     | Coefficient |   SE |        95% CI |    z |      p
#> -------------------------------------------------------------
#> y1 ~~ y5 |        0.28 | 0.14 | [ 0.00, 0.56] | 1.97 | 0.049 
#> y2 ~~ y4 |        0.29 | 0.11 | [ 0.07, 0.52] | 2.55 | 0.011 
#> y2 ~~ y6 |        0.36 | 0.10 | [ 0.17, 0.54] | 3.71 | < .001
#> y3 ~~ y7 |        0.17 | 0.13 | [-0.09, 0.43] | 1.26 | 0.208 
#> y4 ~~ y8 |        0.11 | 0.13 | [-0.14, 0.36] | 0.86 | 0.388 
#> y6 ~~ y8 |        0.34 | 0.11 | [ 0.12, 0.55] | 3.08 | 0.002