# Confirmatory Factor Analysis ```r 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 ## Returns 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 ```r 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 # ----------------------------------------------- # ```

cfa function