setwd("C:/Users/Nina/Desktop/Essays/UC Riverside/Latent Variable Analysis/Homework 1")
library(GAIPE)
## Warning: package 'GAIPE' was built under R version 4.0.3
library(moments)
## Warning: package 'moments' was built under R version 4.0.3
library(lavaan)
## Warning: package 'lavaan' was built under R version 4.0.4
## This is lavaan 0.6-8
## lavaan is FREE software! Please report any bugs.
DataHW1 <- read.csv("Fast Friends vs. Free Talk HW Filtered.csv")
Correlation and Covariance
cor(DataHW1)
## pre_affect affect how_enjoyable i_like_you
## pre_affect 1.00000000 0.38909502 0.21378748 0.1818490
## affect 0.38909502 1.00000000 0.72447696 0.5405703
## how_enjoyable 0.21378748 0.72447696 1.00000000 0.6930464
## i_like_you 0.18184902 0.54057028 0.69304643 1.0000000
## you_like_me 0.15706575 0.60903714 0.68281019 0.6092762
## in_common 0.25095692 0.22705144 0.28072303 0.4072911
## i_disclosed -0.05504011 0.05001432 0.06616528 0.2060782
## you_disclosed 0.06939033 0.15631380 0.19426200 0.2933759
## i_am_good_listener 0.16676844 0.15215238 0.16112944 0.1662772
## you_are_good_listener 0.30351551 0.34588996 0.41245760 0.4404369
## you_like_me in_common i_disclosed you_disclosed
## pre_affect 0.1570658 0.25095692 -0.05504011 0.06939033
## affect 0.6090371 0.22705144 0.05001432 0.15631380
## how_enjoyable 0.6828102 0.28072303 0.06616528 0.19426200
## i_like_you 0.6092762 0.40729108 0.20607824 0.29337587
## you_like_me 1.0000000 0.32682857 0.10577374 0.23979796
## in_common 0.3268286 1.00000000 0.14242846 0.16121825
## i_disclosed 0.1057737 0.14242846 1.00000000 0.65160443
## you_disclosed 0.2397980 0.16121825 0.65160443 1.00000000
## i_am_good_listener 0.1677160 -0.02260743 -0.17273330 0.06441420
## you_are_good_listener 0.2995710 0.11745768 0.03760725 0.08281489
## i_am_good_listener you_are_good_listener
## pre_affect 0.16676844 0.30351551
## affect 0.15215238 0.34588996
## how_enjoyable 0.16112944 0.41245760
## i_like_you 0.16627719 0.44043689
## you_like_me 0.16771601 0.29957104
## in_common -0.02260743 0.11745768
## i_disclosed -0.17273330 0.03760725
## you_disclosed 0.06441420 0.08281489
## i_am_good_listener 1.00000000 0.57118196
## you_are_good_listener 0.57118196 1.00000000
cov(DataHW1)
## pre_affect affect how_enjoyable i_like_you you_like_me
## pre_affect 1.9050597 0.6588872 0.3814414 0.2046303 0.2480396
## affect 0.6588872 1.5052278 1.1489918 0.5407020 0.8549291
## how_enjoyable 0.3814414 1.1489918 1.6710232 0.7303958 1.0098954
## i_like_you 0.2046303 0.5407020 0.7303958 0.6646751 0.5683346
## you_like_me 0.2480396 0.8549291 1.0098954 0.5683346 1.3090926
## in_common 0.5936333 0.4774085 0.6219193 0.5690814 0.6408701
## i_disclosed -0.1208925 0.0976475 0.1361090 0.2673637 0.1925878
## you_disclosed 0.1573002 0.3149739 0.4124347 0.3928305 0.4506161
## i_am_good_listener 0.2345967 0.1902539 0.2122853 0.1381628 0.1955751
## you_are_good_listener 0.3737864 0.3786408 0.4757282 0.3203883 0.3058252
## in_common i_disclosed you_disclosed i_am_good_listener
## pre_affect 0.59363331 -0.12089246 0.1573002 0.23459671
## affect 0.47740851 0.09764750 0.3149739 0.19025392
## how_enjoyable 0.62191934 0.13610904 0.4124347 0.21228529
## i_like_you 0.56908140 0.26736370 0.3928305 0.13816281
## you_like_me 0.64087005 0.19258775 0.4506161 0.19557506
## in_common 2.93717326 0.38844287 0.4537901 -0.03948842
## i_disclosed 0.38844287 2.53239358 1.7030433 -0.28015310
## you_disclosed 0.45379014 1.70304332 2.6974421 0.10782300
## i_am_good_listener -0.03948842 -0.28015310 0.1078230 1.03874160
## you_are_good_listener 0.17961165 0.05339806 0.1213592 0.51941748
## you_are_good_listener
## pre_affect 0.37378641
## affect 0.37864078
## how_enjoyable 0.47572816
## i_like_you 0.32038835
## you_like_me 0.30582524
## in_common 0.17961165
## i_disclosed 0.05339806
## you_disclosed 0.12135922
## i_am_good_listener 0.51941748
## you_are_good_listener 0.79611650
Residual Correlation and Covariance
attach(DataHW1)
HW.model <- ' enjoyment =~ how_enjoyable + i_like_you + you_like_me + in_common
disclosure =~ i_disclosed + you_disclosed +
i_am_good_listener + you_are_good_listener
'
fit <- cfa(HW.model, data = DataHW1)
## Warning in lavaan::lavaan(model = HW.model, data = DataHW1, model.type = "cfa", : lavaan WARNING:
## the optimizer warns that a solution has NOT been found!
lavResiduals(fit)
## $type
## [1] "cor.bentler"
##
## $cov
## hw_njy i_lk_y y_lk_m in_cmm i_dscl y_dscl i_m_g_ y_r_g_
## how_enjoyable 0.000
## i_like_you 0.002 0.000
## you_like_me 0.016 -0.024 0.000
## in_common -0.065 0.079 0.010 0.000
## i_disclosed 0.066 0.206 0.105 0.142 0.000
## you_disclosed 0.003 0.112 0.064 0.070 -0.019 -0.084
## i_am_good_listener 0.161 0.166 0.168 -0.023 -0.173 -0.176 0.000
## you_are_good_listener 0.413 0.441 0.300 0.117 0.038 0.253 0.571 0.000
##
## $cov.z
## hw_njy i_lk_y y_lk_m in_cmm i_dscl y_dscl i_m_g_ y_r_g_
## how_enjoyable 0.000
## i_like_you 0.249 0.000
## you_like_me 1.609 -2.030 0.000
## in_common -2.076 1.763 0.209 0.000
## i_disclosed 0.670 2.055 1.070 1.437 -0.003
## you_disclosed 0.040 1.533 0.872 0.789 -0.160 -0.776
## i_am_good_listener 1.621 1.672 1.686 -0.231 -1.740 -2.414 0.000
## you_are_good_listener 3.889 4.111 2.927 1.190 0.386 3.294 5.058 0.000
##
## $summary
## cov
## srmr 0.176
## srmr.se 0.020
## srmr.exactfit.z 4.923
## srmr.exactfit.pvalue 0.000
## usrmr 0.160
## usrmr.se 0.033
## usrmr.ci.lower 0.105
## usrmr.ci.upper 0.215
## usrmr.closefit.h0.value 0.050
## usrmr.closefit.z 3.311
## usrmr.closefit.pvalue 0.000