| pca {jmv} | R Documentation |
Principal Component Analysis
pca(data, vars, nFactorMethod = "parallel", nFactors = 1, minEigen = 1, rotation = "varimax", hideLoadings = 0.3, screePlot = FALSE, eigen = FALSE, factorCor = FALSE, factorSummary = FALSE, kmo = FALSE, bartlett = FALSE)
data |
the data as a data frame |
vars |
a vector of strings naming the variables of interest in
|
nFactorMethod |
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nFactors |
an integer (default: 1), the number of components in the model |
minEigen |
a number (default: 1), the minimal eigenvalue for a component to be included in the model |
rotation |
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hideLoadings |
a number (default: 0.3), hide loadings below this value |
screePlot |
|
eigen |
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factorCor |
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factorSummary |
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kmo |
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bartlett |
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A results object containing:
results$loadings | a table | ||||
results$factorStats$factorSummary | a table | ||||
results$factorStats$factorCor | a table | ||||
results$modelFit$fit | a table | ||||
results$assump$bartlett | a table | ||||
results$assump$kmo | a table | ||||
results$eigen$initEigen | a table | ||||
results$eigen$screePlot | an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$loadings$asDF
as.data.frame(results$loadings)
data('iris')
pca(iris, vars = c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width'))
#
# PRINCIPAL COMPONENT ANALYSIS
#
# Component Loadings
# ----------------------------------------
# 1 Uniqueness
# ----------------------------------------
# Sepal.Length 0.890 0.2076
# Sepal.Width -0.460 0.7883
# Petal.Length 0.992 0.0168
# Petal.Width 0.965 0.0688
# ----------------------------------------
# Note. 'varimax' rotation was used
#