Comprehensive factor analysis toolkit with BlueSky R integration. Includes maximum likelihood factor analysis, principal axis factoring, scree plots, multiple rotation methods, and factor score extraction. Essential for dimensionality reduction, scale development, and multivariate analysis in clinical and pathological research.
Usage
factoranalysis(
data,
vars,
method = "ml",
nFactorsMethod = "kaiser",
nFactors = 1,
rotation = "varimax",
scores = "none",
saveScores = FALSE,
scoresPrefix = "Factor",
showUnrotated = FALSE,
showRotated = TRUE,
hideSmallLoadings = FALSE,
loadingsCutoff = 0.3,
screePlot = TRUE,
loadingsPlot = FALSE,
biplot = FALSE,
priorCommunalities = "smc",
maxIterations = 25,
convergence = 0.001,
adequacyTests = TRUE,
bartlettTest = TRUE,
kmoTest = TRUE,
factorCorrelations = TRUE,
communalities = TRUE,
eigenvalues = TRUE,
bluesky_integration = TRUE,
comprehensive_output = FALSE,
clinical_interpretation = TRUE,
parallel_iterations = 100,
parallel_percentile = 95,
plotWidth = 600,
plotHeight = 400
)Arguments
- data
The data as a data frame.
- vars
Numeric variables for factor analysis
- method
Method for factor extraction
- nFactorsMethod
Method for determining number of factors
- nFactors
Number of factors to extract (when method = manual)
- rotation
Factor rotation method
- scores
Method for computing factor scores
- saveScores
Save factor scores to dataset
- scoresPrefix
Prefix for saved factor score variables
- showUnrotated
Display unrotated factor loadings
- showRotated
Display rotated factor loadings
- hideSmallLoadings
Hide loadings below cutoff
- loadingsCutoff
Cutoff for hiding small loadings
- screePlot
Display scree plot of eigenvalues
- loadingsPlot
Display factor loadings plot
- biplot
Display factor biplot (first 2 factors)
- priorCommunalities
Method for prior communality estimates
- maxIterations
Maximum iterations for factor extraction
- convergence
Convergence criterion for factor extraction
- adequacyTests
Perform sampling adequacy tests
- bartlettTest
Test if correlation matrix is identity matrix
- kmoTest
Measure of sampling adequacy
- factorCorrelations
Show correlations between factors (oblique rotation)
- communalities
Show initial and extracted communalities
- eigenvalues
Show eigenvalues and variance explained
- bluesky_integration
Use BlueSky R statistical environment features
- comprehensive_output
Include comprehensive statistical details
- clinical_interpretation
Provide clinical context for factor analysis results
- parallel_iterations
Number of iterations for parallel analysis
- parallel_percentile
Percentile for parallel analysis threshold
- plotWidth
Width of plots
- plotHeight
Height of plots
Value
A results object containing:
results$results$instructions | a html | ||||
results$results$adequacyTable | Tests for appropriateness of factor analysis | ||||
results$results$eigenvaluesTable | Eigenvalues and percentage of variance explained | ||||
results$results$parallelAnalysis | Comparison of eigenvalues with random data | ||||
results$results$communalitiesTable | Initial and extracted communalities | ||||
results$results$unrotatedLoadings | Factor loadings before rotation | ||||
results$results$rotatedLoadings | Factor loadings after rotation | ||||
results$results$factorLoadings | Factor loadings matrix | ||||
results$results$factorCorrelationsTable | Correlations between factors (oblique rotation only) | ||||
results$results$factorScoresStats | Descriptive statistics for factor scores | ||||
results$results$goodnessOfFit | Chi-square test of model fit | ||||
results$results$comprehensiveResults | Enhanced statistical output with BlueSky integration | ||||
results$results$clinicalInterpretation | a html | ||||
results$results$methodDetails | a html | ||||
results$results$screePlotImage | Plot of eigenvalues | ||||
results$results$loadingsPlotImage | Visualization of factor loadings | ||||
results$results$biplotImage | Biplot of first two factors | ||||
results$results$parallelPlot | Comparison of actual vs random eigenvalues |