model_parameters.PCA {parameters}R Documentation

Parameters from PCA, FA, CFA, SEM

Description

Format structural models from the psych or FactoMineR packages.

Usage

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

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

## 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,
  ...
)

## S3 method for class 'omega'
model_parameters(model, verbose = TRUE, ...)

## S3 method for class 'sem'
model_parameters(
  model,
  ci = 0.95,
  ci_method = NULL,
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  exponentiate = FALSE,
  p_adjust = NULL,
  summary = getOption("parameters_summary", FALSE),
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  vcov = NULL,
  vcov_args = NULL,
  ...
)

Arguments

model

Model object.

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.

verbose

Toggle warnings and messages.

...

Arguments passed to or from other methods.

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.

ci_method

Method for computing degrees of freedom for confidence intervals (CI) and the related p-values. Allowed are following options (which vary depending on the model class): "residual", "normal", "likelihood", "satterthwaite", "kenward", "wald", "profile", "boot", "uniroot", "ml1", "betwithin", "hdi", "quantile", "ci", "eti", "si", "bci", or "bcai". See section Confidence intervals and approximation of degrees of freedom in model_parameters() for further details. When ci_method=NULL, in most cases "wald" is used then.

bootstrap

Should estimates be based on bootstrapped model? If TRUE, then arguments of Bayesian regressions apply (see also bootstrap_parameters()).

iterations

The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.

exponentiate

Logical, indicating whether or not to exponentiate the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. It is also recommended to use exponentiate = TRUE for models with log-transformed response values. Note: Delta-method standard errors are also computed (by multiplying the standard errors by the transformed coefficients). This is to mimic behaviour of other software packages, such as Stata, but these standard errors poorly estimate uncertainty for the transformed coefficient. The transformed confidence interval more clearly captures this uncertainty. For compare_parameters(), exponentiate = "nongaussian" will only exponentiate coefficients from non-Gaussian families.

p_adjust

Character vector, if not NULL, indicates the method to adjust p-values. See stats::p.adjust() for details. Further possible adjustment methods are "tukey", "scheffe", "sidak" and "none" to explicitly disable adjustment for emmGrid objects (from emmeans).

summary

Logical, if TRUE, prints summary information about the model (model formula, number of observations, residual standard deviation and more).

vcov

Variance-covariance matrix used to compute uncertainty estimates (e.g., for robust standard errors). This argument accepts a covariance matrix, a function which returns a covariance matrix, or a string which identifies the function to be used to compute the covariance matrix.

  • A covariance matrix

  • A function which returns a covariance matrix (e.g., stats::vcov())

  • A string which indicates the kind of uncertainty estimates to return.

    • Heteroskedasticity-consistent: "vcovHC", "HC", "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", "HC5". See ?sandwich::vcovHC.

    • Cluster-robust: "vcovCR", "CR0", "CR1", "CR1p", "CR1S", "CR2", "CR3". See ?clubSandwich::vcovCR.

    • Bootstrap: "vcovBS", "xy", "residual", "wild", "mammen", "webb". See ?sandwich::vcovBS.

    • Other sandwich package functions: "vcovHAC", "vcovPC", "vcovCL", "vcovPL".

vcov_args

List of arguments to be passed to the function identified by the vcov argument. This function is typically supplied by the sandwich or clubSandwich packages. Please refer to their documentation (e.g., ?sandwich::vcovHAC) to see the list of available arguments.

Details

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

Value

A data frame of indices or loadings.

Note

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

References

Examples


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)
}


# 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)
}


[Package parameters version 0.20.3 Index]