#first load some needed libraries
library("psych")
library("lessR")
## Warning: package 'lessR' was built under R version 3.6.3
##
## lessR 3.9.6 feedback: gerbing@pdx.edu web: lessRstats.com/new
## -----------------------------------------------------------------
## > d <- Read("") Read text, Excel, SPSS, SAS or R data file
## d is default data frame, no need for data= in analysis routines
##
## > vignette("topic") for help on the following topics
## "Read": read data and variable labels, write data to a file
## "BarChart", "Histogram", "Plot": visualizations
## "Means": analyze means with t-tests and ANOVA
## "Regression": least-squares, logistic regression
## "Customize": custom color palettes and more customization
## "Extract": general, simple data frame subsetting
## "pivot": 1-d and 2-d simply created pivot tables
##
## Attaching package: 'lessR'
## The following objects are masked from 'package:psych':
##
## reflect, scree
library("lavaan")
## This is lavaan 0.6-7
## lavaan is BETA software! Please report any bugs.
##
## Attaching package: 'lavaan'
## The following object is masked from 'package:lessR':
##
## cfa
## The following object is masked from 'package:psych':
##
## cor2cov
library("lavaanPlot")
Ex <- read.csv("C:/Users/Courtney/Desktop/Ex.csv")
ExPar <- parcels(Ex, size = 3, max = TRUE, flip=TRUE,congruence = FALSE)
keysort(ExPar)
ExFac <- 'P =~ P1 + P2 + P3 + P4 + P5 + P6'
lavaanify(model=ExFac)
## id lhs op rhs user block group free ustart exo label plabel
## 1 1 P =~ P1 1 1 1 1 NA 0 .p1.
## 2 2 P =~ P2 1 1 1 2 NA 0 .p2.
## 3 3 P =~ P3 1 1 1 3 NA 0 .p3.
## 4 4 P =~ P4 1 1 1 4 NA 0 .p4.
## 5 5 P =~ P5 1 1 1 5 NA 0 .p5.
## 6 6 P =~ P6 1 1 1 6 NA 0 .p6.
## 7 7 P1 ~~ P1 0 1 1 0 0 0 .p7.
## 8 8 P2 ~~ P2 0 1 1 0 0 0 .p8.
## 9 9 P3 ~~ P3 0 1 1 0 0 0 .p9.
## 10 10 P4 ~~ P4 0 1 1 0 0 0 .p10.
## 11 11 P5 ~~ P5 0 1 1 0 0 0 .p11.
## 12 12 P6 ~~ P6 0 1 1 0 0 0 .p12.
## 13 13 P ~~ P 0 1 1 0 0 0 .p13.
ex.out <-cfa(ExFac, data=ExPar)
lavaanPlot(model = ex.out)
summary(ex.out, fit.measures=TRUE, standardized = TRUE)
## lavaan 0.6-7 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 12
##
## Number of observations 20
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 9
## P-value (Chi-square) 1.000
##
## Model Test Baseline Model:
##
## Test statistic 26.549
## Degrees of freedom 15
## P-value 0.033
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 2.299
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -33.420
## Loglikelihood unrestricted model (H1) -33.420
##
## Akaike (AIC) 90.839
## Bayesian (BIC) 102.788
## Sample-size adjusted Bayesian (BIC) 65.795
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## P =~
## P1 1.000 NA NA
## P2 1.000 0.667 1.500 0.134 NA NA
## P3 1.000 0.667 1.500 0.134 NA NA
## P4 1.000 0.667 1.500 0.134 NA NA
## P5 1.000 0.667 1.500 0.134 NA NA
## P6 1.000 0.667 1.500 0.134 NA NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .P1 0.150 0.057 2.626 0.009 0.150 1.176
## .P2 0.150 0.057 2.626 0.009 0.150 1.176
## .P3 0.150 0.057 2.626 0.009 0.150 1.176
## .P4 0.150 0.057 2.626 0.009 0.150 1.176
## .P5 0.150 0.057 2.626 0.009 0.150 1.176
## .P6 0.150 0.057 2.626 0.009 0.150 1.176
## P -0.022 0.020 -1.142 0.253 NA NA