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jmv

cfa

Confirmatory Factor Analysis

Description

Confirmatory Factor Analysis

Usage

cfa(
  data,
  factors = list(list(label = "Factor 1", vars = list())),
  resCov,
  miss = "fiml",
  constrain = "facVar",
  estTest = TRUE,
  ci = FALSE,
  ciWidth = 95,
  stdEst = FALSE,
  factCovEst = TRUE,
  factInterceptEst = FALSE,
  resCovEst = FALSE,
  resInterceptEst = FALSE,
  fitMeasures = list("cfi", "tli", "rmsea"),
  modelTest = TRUE,
  pathDiagram = FALSE,
  corRes = FALSE,
  hlCorRes = 0.1,
  mi = FALSE,
  hlMI = 3
)

Arguments

`data`	the data as a data frame
`factors`	a list containing named lists that define the `label` of the factor and the `vars` that belong to that factor
`resCov`	a list of lists specifying the residual covariances that need to be estimated
`miss`	`'listwise'` or `'fiml'`, how to handle missing values; `'listwise'` excludes a row from all analyses if one of its entries is missing, `'fiml'` uses a full information maximum likelihood method to estimate the model.
`constrain`	`'facVar'` or `'facInd'`, how to contrain the model; `'facVar'` fixes the factor variances to one, `'facInd'` fixes each factor to the scale of its first indicator.
`estTest`	`TRUE` (default) or `FALSE`, provide 'Z' and 'p' values for the model estimates
`ci`	`TRUE` or `FALSE` (default), provide a confidence interval for the model estimates
`ciWidth`	a number between 50 and 99.9 (default: 95) specifying the confidence interval width that is used as `'ci'`
`stdEst`	`TRUE` or `FALSE` (default), provide a standardized estimate for the model estimates
`factCovEst`	`TRUE` (default) or `FALSE`, provide estimates for the factor (co)variances
`factInterceptEst`	`TRUE` or `FALSE` (default), provide estimates for the factor intercepts
`resCovEst`	`TRUE` (default) or `FALSE`, provide estimates for the residual (co)variances
`resInterceptEst`	`TRUE` or `FALSE` (default), provide estimates for the residual intercepts
`fitMeasures`	one or more of `'cfi'`, `'tli'`, `'srmr'`, `'rmsea'`, `'aic'`, or `'bic'`; use CFI, TLI, SRMR, RMSEA + 90% confidence interval, adjusted AIC, and BIC model fit measures, respectively
`modelTest`	`TRUE` (default) or `FALSE`, provide a chi-square test for exact fit that compares the model with the perfect fitting model
`pathDiagram`	`TRUE` or `FALSE` (default), provide a path diagram of the model
`corRes`	`TRUE` or `FALSE` (default), provide the residuals for the observed correlation matrix (i.e., the difference between the expected correlation matrix and the observed correlation matrix)
`hlCorRes`	a number (default: 0.1), highlight values in the `'corRes'` table above this value
`mi`	`TRUE` or `FALSE` (default), provide modification indices for the parameters not included in the model
`hlMI`	a number (default: 3), highlight values in the `'modIndices'` tables above this value

Value

A results object containing:

`results$factorLoadings`					a table containing the factor loadings
`results$factorEst$factorCov`					a table containing factor covariances estimates
`results$factorEst$factorIntercept`					a table containing factor intercept estimates
`results$resEst$resCov`					a table containing residual covariances estimates
`results$resEst$resIntercept`					a table containing residual intercept estimates
`results$modelFit$test`					a table containing the chi-square test for exact fit
`results$modelFit$fitMeasures`					a table containing fit measures
`results$modelPerformance$corRes`					a table containing residuals for the observed correlation matrix
`results$modelPerformance$modIndices`					a group
`results$pathDiagram`					an image containing the model path diagram
`results$modelSyntax`					the lavaan syntax used to fit the model

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$factorLoadings$asDF

as.data.frame(results$factorLoadings)

Examples

data <- lavaan::HolzingerSwineford1939

jmv::cfa(
    data = data,
    factors = list(
        list(label="Visual", vars=c("x1", "x2", "x3")),
        list(label="Textual", vars=c("x4", "x5", "x6")),
        list(label="Speed", vars=c("x7", "x8", "x9"))),
    resCov = NULL)

#
#  CONFIRMATORY FACTOR ANALYSIS
#
#  Factor Loadings
#  -----------------------------------------------------------------
#    Factor     Indicator    Estimate    SE        Z        p
#  -----------------------------------------------------------------
#    Visual     x1              0.900    0.0832    10.81    < .001
#               x2              0.498    0.0808     6.16    < .001
#               x3              0.656    0.0776     8.46    < .001
#    Textual    x4              0.990    0.0567    17.46    < .001
#               x5              1.102    0.0626    17.60    < .001
#               x6              0.917    0.0538    17.05    < .001
#    Speed      x7              0.619    0.0743     8.34    < .001
#               x8              0.731    0.0755     9.68    < .001
#               x9              0.670    0.0775     8.64    < .001
#  -----------------------------------------------------------------
#
#
#  FACTOR ESTIMATES
#
#  Factor Covariances
#  --------------------------------------------------------------
#                          Estimate    SE        Z       p
#  --------------------------------------------------------------
#    Visual     Visual      1.000 a
#               Textual     0.459      0.0635    7.22    < .001
#               Speed       0.471      0.0862    5.46    < .001
#    Textual    Textual     1.000 a
#               Speed       0.283      0.0715    3.96    < .001
#    Speed      Speed       1.000 a
#  --------------------------------------------------------------
#    a fixed parameter
#
#
#  MODEL FIT
#
#  Test for Exact Fit
#  ------------------------
#    X²      df    p
#  ------------------------
#    85.3    24    < .001
#  ------------------------
#
#
#  Fit Measures
#  -----------------------------------------------
#    CFI      TLI      RMSEA     Lower     Upper
#  -----------------------------------------------
#    0.931    0.896    0.0921    0.0714    0.114
#  -----------------------------------------------
#

jmv

The 'jamovi' Analyses

v1.2.23

GPL (>= 2)

Authors

Ravi Selker, Jonathon Love, Damian Dropmann

Initial release

2020-06-26

cfa

Description

Usage

Arguments

Value

Examples

jmv

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