# HEALTHY PERSONALITY DEVELOPMENT - CLPS DATA #
# SPRING 2021
library(readr)
library(dplyr)
library(jmv)
library(ggplot2)
library(corrplot)
library(ggpubr)
library(lavaan)
library(semPlot)
library(tidyr)
library(stringr)
library(tidyverse)
library(growthcurver)
library(nlme)
library(PerformanceAnalytics)
library(psych)
library(tidySEM)
hpi1 <- read_csv("hpi.all.csv")
hpi.wb <- read_csv("hpi.full.csv")
#exploring HPI by year
descriptives(hpi.wb, vars = vars(hpi, life.soc, life.rec, life.emp, gaf), splitBy=time, missing=TRUE)
## name type n missing unique mean median
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## 3 neodate character 2755 0.206051873 1798 NA NA
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## 327 2.0000000 <NA> 14.17813566 NA 0.0000000 77.75000
## 328 2.1250000 <NA> 16.65870762 NA 0.0000000 80.00000
## 329 2.5000000 <NA> 13.51565467 NA -4.8571429 78.00000
## 330 2.6250000 <NA> 13.19000255 NA -4.3589744 72.56410
## 331 2.7500000 <NA> 12.31965482 NA -0.6976744 71.39535
## 332 2.0000000 <NA> 0.57514491 NA 0.1250000 4.00000
## 333 2.3750000 <NA> 0.77152476 NA 0.0000000 4.00000
## 334 2.6250000 <NA> 0.64930335 NA 0.1250000 4.00000
## 335 2.1250000 <NA> 0.79250813 NA 0.0000000 4.00000
## 336 2.5000000 <NA> 0.69178741 NA 0.0000000 4.00000
## 337 2.7500000 <NA> 0.64885235 NA 0.0000000 4.00000
## 338 2.1250000 <NA> 0.66410107 NA 0.0000000 4.00000
## 339 2.5000000 <NA> 0.59731814 NA 0.1250000 4.00000
## 340 2.6250000 <NA> 0.55701948 NA 0.0000000 4.00000
## 341 2.3750000 <NA> 0.63521528 NA 0.0000000 4.00000
## 342 2.1250000 <NA> 0.64388403 NA 0.0000000 4.00000
## 343 2.5000000 <NA> 0.58811376 NA 0.2500000 4.00000
## 344 2.1250000 <NA> 0.68959626 NA 0.1250000 4.00000
## 345 2.1250000 <NA> 0.76919590 NA 0.0000000 4.00000
## 346 2.1250000 <NA> 0.66852755 NA 0.0000000 3.87500
## 347 165.8500000 <NA> 7.08796791 NA 1.0000000 184.10000
## 348 497.9059375 <NA> 32.14885305 NA 1.0000000 604.53875
## 349 446.9000000 <NA> 45.70761881 NA 1.0000000 579.31500
## 350 -0.3545219 <NA> 0.29468333 NA -0.8743604 1.00000
## 351 -0.3545219 <NA> 0.29468333 NA -0.8743604 1.00000
## 352 2.0000000 <NA> 1.27719369 NA 1.0000000 5.00000
## 353 2.0000000 <NA> 1.17672525 NA 1.0000000 5.00000
## 354 3.0000000 <NA> 1.08585906 NA 1.0000000 5.00000
## 355 60.0000000 <NA> 12.69310882 NA 4.0000000 95.00000
## range skew skew_2se kurt kurt_2se
## 1 NA NA NA NA NA
## 2 5.00000 0.2320256527 2.79114718 -1.185598e+00 -7.133109973
## 3 NA NA NA NA NA
## 4 4.00000 -0.6693052811 -7.26537908 -5.895716e-01 -3.201060710
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## 7 4.00000 0.1168492900 1.26728951 -9.771614e-01 -5.300778465
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## 31 4.00000 -0.9133497333 -9.90398412 5.302232e-01 2.875776648
## 32 4.00000 -0.7260234673 -7.87827365 4.372673e-01 2.373290057
## 33 4.00000 0.9605360186 10.42118740 2.309594e-01 1.253322249
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## 39 4.00000 0.2364377164 2.56474048 -9.586955e-01 -5.201527337
## 40 4.00000 0.5504261455 5.97176358 -2.909894e-01 -1.579079971
## 41 4.00000 -0.5124350766 -5.56155298 -6.708515e-01 -3.641723823
## 42 4.00000 -0.3868584744 -4.19790415 -8.903025e-01 -4.832160931
## 43 4.00000 -0.3556118389 -3.86020384 -7.971852e-01 -4.328292050
## 44 4.00000 -0.3994163078 -4.33417254 -1.002843e+00 -5.442981565
## 45 4.00000 0.9366763693 10.15872789 3.458492e-01 1.876118087
## 46 4.00000 -0.7775108145 -8.43249713 -1.492195e-01 -0.809466617
## 47 4.00000 -0.9980085528 -10.82773988 1.373978e+00 7.456016172
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## 50 4.00000 -0.2571873641 -2.78586635 -6.010104e-01 -3.256241316
## 51 4.00000 -0.8524521162 -9.24527336 9.723547e-01 5.274703700
## 52 4.00000 0.7831551095 8.49220785 -5.551587e-02 -0.301101979
## 53 4.00000 0.0323987350 0.35150508 -9.292972e-01 -5.042914987
## 54 4.00000 -0.4167123633 -4.52025626 -7.862905e-01 -4.266121558
## 55 4.00000 0.0016683159 0.01809052 -1.254478e+00 -6.803925266
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## 58 4.00000 -0.0362713520 -0.39331143 -1.131697e+00 -6.137998850
## 59 4.00000 0.6146823741 6.66299732 -5.178121e-01 -2.807468149
## 60 4.00000 -0.3909967543 -4.23604827 -8.126852e-01 -4.403865114
## 61 4.00000 -0.7832688401 -8.49645029 7.943645e-01 4.309927757
## 62 4.00000 0.1368482062 1.48129341 -8.925465e-01 -4.832334455
## 63 4.00000 -0.8031451228 -8.70742804 3.091246e-01 1.676305030
## 64 4.00000 -0.5723348222 -6.20176048 -5.199353e-01 -2.817980565
## 65 4.00000 -0.6530999565 -7.07189861 1.426207e-01 0.772436812
## 66 4.00000 -0.0745134895 -0.80770688 -1.015599e+00 -5.506361168
## 67 4.00000 0.0699329320 0.75832359 -1.157011e+00 -6.275291663
## 68 4.00000 -0.8028241645 -8.70086235 4.204079e-01 2.278958855
## 69 4.00000 0.5601234077 6.06836186 -6.328040e-01 -3.429105515
## 70 4.00000 0.2118699369 2.29580055 -1.001824e+00 -5.429752348
## 71 4.00000 -0.1881325405 -2.03822286 -1.099673e+00 -5.959027710
## 72 4.00000 0.3864408781 4.19040394 -7.925024e-01 -4.298303157
## 73 4.00000 -0.2309111822 -2.49635141 -6.942761e-01 -3.754201169
## 74 4.00000 -0.6050667671 -6.55527700 -5.945231e-01 -3.221664919
## 75 4.00000 0.1402128093 1.51960094 -1.052980e+00 -5.708022998
## 76 4.00000 -0.9771032624 -10.59342341 1.405980e+00 7.624278809
## 77 4.00000 -0.6292840495 -6.81764638 -3.087926e-01 -1.673318012
## 78 4.00000 -0.1596643129 -1.73041280 -1.061637e+00 -5.754953279
## 79 4.00000 -0.2867537004 -3.10888673 -1.093680e+00 -5.930750169
## 80 4.00000 -0.1182748911 -1.28229640 -1.005194e+00 -5.450913117
## 81 4.00000 0.8132471743 8.81382504 4.014975e-01 2.176449202
## 82 4.00000 -0.0025802616 -0.02798920 -1.191611e+00 -6.465240224
## 83 4.00000 0.2103314454 2.27791613 -9.161591e-01 -4.962819203
## 84 4.00000 -0.2594161390 -2.81200358 -9.112423e-01 -4.940563276
## 85 4.00000 -0.2139930108 -2.32003991 -1.100108e+00 -5.965609266
## 86 4.00000 -0.4577805827 -4.96310239 -7.191395e-01 -3.899713420
## 87 4.00000 -0.6213085260 -6.72884985 -8.713608e-02 -0.472014721
## 88 4.00000 -0.4084012042 -4.42853257 -5.102613e-01 -2.767509073
## 89 4.00000 -0.5031134781 -5.45458633 -7.700491e-01 -4.175783279
## 90 4.00000 0.2411700456 2.61422067 -8.347399e-01 -4.525783556
## 91 4.00000 -0.3039711378 -3.29438393 -6.792346e-01 -3.682014189
## 92 4.00000 -0.5119100477 -5.55192279 -3.510652e-01 -1.904412692
## 93 4.00000 0.5635728235 6.11006612 -6.549046e-01 -3.551383437
## 94 4.00000 -0.1803125681 -1.95558079 -1.058088e+00 -5.739778593
## 95 4.00000 -0.5007991399 -5.43141938 -5.108246e-01 -2.771055089
## 96 4.00000 -0.2766324521 -3.00021854 -8.224820e-01 -4.461693381
## 97 4.00000 -0.5054188406 -5.48540447 -6.621776e-01 -3.594637126
## 98 4.00000 -0.0114397485 -0.12417967 -1.091452e+00 -5.926003371
## 99 4.00000 0.0239233382 0.25959850 -1.036374e+00 -5.624969527
## 100 4.00000 -0.0985958541 -1.07007910 -1.114481e+00 -6.049968183
## 101 4.00000 -0.5890816702 -6.39115073 -3.119471e-01 -1.692809201
## 102 4.00000 -0.5266700090 -5.71200202 -4.696894e-01 -2.547910066
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## 104 4.00000 -0.5539633174 -6.01226669 -6.774892e-01 -3.677756668
## 105 4.00000 0.1951948007 2.11773589 -9.970967e-01 -5.410835157
## 106 4.00000 -0.7265913724 -7.88443614 -1.022915e-01 -0.555192042
## 107 4.00000 -0.9734010126 -10.56076416 2.015654e+00 10.938130348
## 108 4.00000 -0.1972193558 -2.13894343 -8.866076e-01 -4.809553563
## 109 4.00000 -0.5069937874 -5.49762948 -7.355960e-01 -3.989659386
## 110 4.00000 0.4905076282 5.32357032 -6.411477e-01 -3.480476228
## 111 4.00000 -0.2654886772 -2.88139788 -9.848247e-01 -5.346129888
## 112 4.00000 -0.4927326014 -5.34298712 -5.839557e-01 -3.167206593
## 113 4.00000 -0.5761441300 -6.25078688 -2.660260e-01 -1.443614257
## 114 4.00000 -0.5052524906 -5.47874758 -6.961128e-01 -3.775513670
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## 116 4.00000 0.0003506359 0.00380215 -1.065588e+00 -5.779440745
## 117 4.00000 -0.8285106754 -8.98084058 9.275883e-01 5.029188103
## 118 4.00000 0.3396970500 3.68483840 -9.414678e-01 -5.108055907
## 119 4.00000 0.6327768162 6.86035242 -1.711901e-01 -0.928320877
## 120 4.00000 -0.0418947270 -0.45444992 -1.084548e+00 -5.884354551
## 121 4.00000 -0.7919242450 -8.58120857 6.230204e-01 3.376687795
## 122 4.00000 -0.7378558933 -7.99674804 1.616252e-01 0.876142272
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## 125 4.00000 -0.4888658664 -5.30293600 -3.080935e-01 -1.671601452
## 126 4.00000 -0.0891102018 -0.96644511 -9.935946e-01 -5.389922752
## 127 4.00000 -0.3430836927 -3.72355098 -9.057665e-01 -4.916962060
## 128 4.00000 -0.3312796952 -3.59162252 -5.397582e-01 -2.926973369
## 129 4.00000 0.1517689374 1.64659368 -9.640362e-01 -5.231429025
## 130 4.00000 0.3585726111 3.88958964 -9.313690e-01 -5.053263581
## 131 4.00000 -0.4541251323 -4.92783254 -7.625434e-01 -4.138742248
## 132 4.00000 -0.9179265469 -9.95890250 1.256709e+00 6.819643728
## 133 4.00000 -0.0395248366 -0.42897039 -1.200658e+00 -6.517780729
## 134 4.00000 -0.3311647104 -3.59419206 -8.343799e-01 -4.529438994
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## 211 4.00000 -0.7661673527 -8.30652755 1.994379e-01 1.081501946
## 212 4.00000 -1.0764926633 -11.67717382 2.003290e+00 10.869109532
## 213 4.00000 -0.5938535224 -6.43607652 -2.783076e-01 -1.508657567
## 214 4.00000 -0.5654139862 -6.12676702 -5.464887e-01 -2.961896045
## 215 4.00000 -0.5347450368 -5.79546950 -3.755191e-01 -2.035624534
## 216 4.00000 -0.5367480072 -5.81820902 -4.844267e-01 -2.626459245
## 217 4.00000 -0.3314771273 -3.59439992 -6.506343e-01 -3.528851766
## 218 4.00000 -0.7254975169 -7.86838667 1.909476e-01 1.035827982
## 219 4.00000 -0.6296738953 -6.83034058 -3.880914e-01 -2.105640177
## 220 4.00000 0.1521586846 1.64994541 -9.987957e-01 -5.417178184
## 221 4.00000 -0.0364824313 -0.39567038 -8.364960e-01 -4.537714845
## 222 4.00000 -0.3547457727 -3.84262389 -4.427571e-01 -2.398833572
## 223 4.00000 -0.0489169178 -0.53052866 -1.228887e+00 -6.666307860
## 224 4.00000 -0.2187997657 -2.37383431 -1.086036e+00 -5.893473808
## 225 4.00000 -0.2303985817 -2.49701795 -9.827433e-01 -5.327282630
## 226 4.00000 -0.1695135213 -1.83911081 -9.758553e-01 -5.295566846
## 227 4.00000 -0.7089510499 -7.68893188 3.086448e-01 1.674296023
## 228 4.00000 -0.0038617901 -0.04189048 -1.175553e+00 -6.378114049
## 229 4.00000 -0.4996926800 -5.41557843 -6.843883e-01 -3.709951684
## 230 4.00000 -0.0836062264 -0.90610908 -9.512063e-01 -5.156325955
## 231 4.00000 0.2475203591 2.68210481 -1.100906e+00 -5.966763020
## 232 4.00000 -0.6953957394 -7.52988591 -2.133861e-01 -1.155703836
## 233 4.00000 0.3533943506 3.82866484 -8.671316e-01 -4.698905035
## 234 4.00000 -0.3920075704 -4.25151991 -8.062893e-01 -4.373853496
## 235 4.00000 0.1051317392 1.13980279 -1.101415e+00 -5.972695094
## 236 4.00000 -0.5115667798 -5.54525105 -4.907930e-01 -2.660975885
## 237 4.00000 -0.5324560403 -5.77066177 -9.609134e-02 -0.520894656
## 238 4.00000 -0.0232506550 -0.25176280 -1.025133e+00 -5.552143002
## 239 4.00000 0.0274518731 0.29746564 -1.079155e+00 -5.848876230
## 240 4.00000 -0.4295609608 -4.65632927 -6.741867e-01 -3.655297829
## 241 4.00000 -0.1295882176 -1.40071199 -2.585755e-01 -1.397961950
## 242 4.00000 -0.1443081934 -1.56204295 -4.097438e-01 -2.218393188
## 243 4.00000 -0.5802420782 -6.29190344 -2.904053e-01 -1.575074046
## 244 4.00000 -2.4154075691 -23.15202642 5.788480e+00 27.754261532
## 245 1.00000 8.2355858552 78.86763349 6.585484e+01 315.470574570
## 246 1.00000 13.4105565221 128.39627683 1.779240e+02 852.132546978
## 247 32.00000 -0.2878126774 -3.09147486 -2.459952e-01 -1.321627277
## 248 32.00000 -0.4835096233 -5.19913314 9.533799e-02 0.512764583
## 249 32.00000 -0.0675571449 -0.72695894 -4.494745e-01 -2.419188375
## 250 32.00000 -0.2005754790 -2.16142736 -4.757674e-01 -2.564383918
## 251 32.00000 -0.2560900543 -2.75172670 -5.798024e-02 -0.311615475
## 252 32.00000 0.0517552789 0.55782164 -4.324135e-01 -2.331124290
## 253 32.00000 0.0898603877 0.96799967 -3.421985e-01 -1.843786614
## 254 32.00000 -0.2415341150 -2.60046712 -4.218380e-01 -2.271665701
## 255 32.00000 -0.2967271216 -3.18895345 -1.536297e-01 -0.825833285
## 256 32.00000 -0.0964576288 -1.03738660 -1.562867e-01 -0.840721527
## 257 32.00000 -0.3261034730 -3.50466377 -5.528044e-01 -2.971589278
## 258 32.00000 0.0697719086 0.74835580 -3.348063e-01 -1.796172824
## 259 31.00000 -0.4142692529 -4.46261709 2.260581e-01 1.218015128
## 260 31.00000 -0.4471434705 -4.80809134 4.588755e-01 2.468009768
## 261 31.00000 -0.1464378182 -1.57463197 9.782690e-02 0.526150932
## 262 83.39623 2.1066765983 22.63656141 4.133769e+00 22.216991205
## 263 86.48148 2.3551117662 25.29690108 4.855272e+00 26.085305675
## 264 85.81818 2.4528290661 26.40353655 5.740476e+00 30.907837543
## 265 71.87500 2.0128195049 21.62024607 4.559086e+00 24.494024575
## 266 74.90909 1.9550941302 20.99640963 4.492309e+00 24.130904215
## 267 73.00000 1.9547368045 21.03045813 5.239913e+00 28.197498695
## 268 77.16981 2.1917752889 23.61037848 4.563033e+00 24.585903642
## 269 84.05405 1.7170918278 18.44042332 4.061138e+00 21.814823986
## 270 76.56098 2.1574764881 23.18241397 4.704436e+00 25.284047921
## 271 75.47619 1.8346383493 19.74190526 4.634790e+00 24.945637943
## 272 74.28571 2.0412766972 21.97732161 4.283295e+00 23.066235789
## 273 78.80952 1.9623462240 21.08570952 3.853977e+00 20.713247086
## 274 79.95238 2.2126383906 23.70639775 6.275304e+00 33.629256148
## 275 75.25000 2.5149535592 26.98940506 7.815448e+00 41.951170217
## 276 82.92683 2.1930297391 23.61540767 5.174106e+00 27.868400262
## 277 184.75000 -0.9607021893 -10.03385723 3.119954e-01 1.629907245
## 278 159.75000 -1.0362279718 -10.82473757 5.106730e-01 2.668335886
## 279 183.50000 -1.3984887767 -14.60901888 1.288306e+00 6.731571640
## 280 179.00000 -1.5252247285 -15.89639583 1.480797e+00 7.719633471
## 281 179.00000 -1.0296717138 -10.75008672 5.555117e-01 2.900962135
## 282 99.82783 -1.2766960957 -13.32909661 9.512893e-01 4.967770229
## 283 77.12500 -0.6732908475 -7.03339123 -8.497756e-02 -0.444019308
## 284 91.92775 -1.0254630284 -10.71023855 5.462152e-01 2.853503879
## 285 93.67089 -0.7684372752 -8.01044295 1.374549e-02 0.071671160
## 286 89.77273 -0.3525616487 -3.68436709 -4.273478e-01 -2.233801907
## 287 82.64623 -1.4271694917 -15.34898306 1.285813e+00 6.916846270
## 288 92.10870 -1.1364048697 -12.23065756 8.915053e-01 4.799175121
## 289 86.48148 -1.3793411264 -14.82925169 1.135903e+00 6.108223719
## 290 89.72727 -1.1293381117 -12.13709971 7.611848e-01 4.091734645
## 291 86.56818 -1.1551812323 -12.43721123 8.728779e-01 4.700588198
## 292 105.41026 -0.4063105760 -4.37138009 -7.373166e-02 -0.396771522
## 293 80.00000 -0.2756671288 -2.96208535 -5.489714e-01 -2.950453304
## 294 81.29167 -0.1690596112 -1.81919168 -5.104615e-01 -2.747436255
## 295 83.46809 -0.2933237285 -3.14896336 -1.793065e-01 -0.962815965
## 296 81.72727 -0.1630571590 -1.75144164 -3.233279e-01 -1.737102656
## 297 80.83673 -0.7416157971 -7.98887995 2.544968e-01 1.371244690
## 298 73.00000 0.0596733325 0.64223950 -6.030838e-01 -3.246536854
## 299 80.42857 -0.5240820678 -5.64047944 -2.182807e-01 -1.175054435
## 300 76.16981 -0.7288776880 -7.85448046 -6.193229e-02 -0.333814715
## 301 78.25000 -0.7213253298 -7.77309527 5.129718e-02 0.276491537
## 302 92.16216 -0.4012390953 -4.31992422 -3.112695e-02 -0.167623654
## 303 76.00000 -0.4792672627 -5.14794105 -3.287001e-01 -1.765965419
## 304 77.56098 -0.8240004512 -8.81718870 2.799498e-01 1.498340928
## 305 76.19048 -0.0576459544 -0.62075433 -8.295756e-01 -4.468200572
## 306 73.54545 -0.3714141754 -3.99450095 -5.566900e-01 -2.994633044
## 307 187.85714 1.8565405797 19.88391389 5.053839e+00 27.073620046
## 308 162.00000 1.4689635397 15.72145395 3.229257e+00 17.286697993
## 309 176.00000 1.9432808396 20.79779342 4.382974e+00 23.462716063
## 310 175.85714 1.8781918394 20.10118408 3.835463e+00 20.531805093
## 311 188.42857 1.7317709704 18.53412752 3.992396e+00 21.371895595
## 312 96.12264 0.1450880437 1.55448576 1.976170e-01 1.059026543
## 313 83.48941 -0.1298721095 -1.39120838 1.261473e-01 0.675898580
## 314 87.38150 -0.0518826288 -0.55537029 -2.862750e-01 -1.532751410
## 315 82.27848 -0.1293782304 -1.38792937 -4.019181e-01 -2.156605369
## 316 84.36585 -0.1397379099 -1.49825044 -1.683820e-01 -0.903010888
## 317 92.60870 2.7369103618 29.40321237 6.072417e+00 32.630391154
## 318 87.95455 2.7229695663 29.34307305 5.961897e+00 32.134594507
## 319 106.41026 2.7893535626 29.98824963 6.499542e+00 34.950757145
## 320 75.00000 2.8755127769 30.91454455 6.904829e+00 37.130154359
## 321 84.34309 2.7976455894 30.13156124 6.598002e+00 35.544064949
## 322 81.83673 2.7439036786 29.49961940 6.116965e+00 32.893480166
## 323 81.42857 2.7393173409 29.43438269 6.026946e+00 32.391894285
## 324 79.25000 2.6878772073 28.94412004 5.691170e+00 30.653370598
## 325 76.00000 2.7805309765 29.83406985 6.292228e+00 33.768838859
## 326 74.54545 2.7063445219 29.06958839 5.815098e+00 31.242043681
## 327 77.75000 2.8673853924 30.86603794 6.988776e+00 37.628923761
## 328 80.00000 2.7135989819 29.18432958 5.848210e+00 31.459599477
## 329 82.85714 2.9867197174 32.13905221 7.826274e+00 42.123034603
## 330 76.92308 2.9195959795 31.45629985 7.411181e+00 39.939065438
## 331 72.09302 3.1455462655 33.85421890 8.870480e+00 47.751805359
## 332 3.87500 0.1988551356 2.14173508 1.219484e-01 0.656947904
## 333 4.00000 -0.1982381608 -2.13125203 -3.183235e-01 -1.711758743
## 334 3.87500 -0.5538427873 -5.93391919 2.151852e-01 1.153173883
## 335 4.00000 -0.2465383050 -2.65625206 -5.124713e-01 -2.761722557
## 336 4.00000 -0.3706584957 -3.98852717 -1.695766e-01 -0.912705102
## 337 4.00000 -0.5895352734 -6.33578937 3.605031e-01 1.937877530
## 338 4.00000 -0.0886038262 -0.95137422 -8.368087e-02 -0.449419341
## 339 3.87500 -0.2823291712 -3.03531176 2.064197e-01 1.110004919
## 340 4.00000 -0.4302006973 -4.61422359 7.060717e-01 3.787942476
## 341 4.00000 -0.2200035420 -2.36567723 -1.412723e-01 -0.759816873
## 342 4.00000 -0.0847251418 -0.91104142 -1.456661e-01 -0.783448913
## 343 3.75000 -0.1395619432 -1.50042592 7.911756e-02 0.425448201
## 344 3.87500 -0.0712438665 -0.76552621 -3.231990e-01 -1.737037093
## 345 4.00000 -0.1493054091 -1.60633449 -4.101699e-01 -2.207243526
## 346 3.87500 -0.1359973309 -1.46210289 -2.169939e-01 -1.166867219
## 347 183.10000 -5.8092977780 -54.86890033 1.354257e+02 639.846865931
## 348 603.53875 -1.3966726909 -13.19159348 2.642241e+01 124.838183076
## 349 578.31500 -0.3839087331 -3.62602346 3.829792e+00 18.094647788
## 350 1.87436 0.6061548250 5.72514096 -2.856678e-01 -1.349696984
## 351 1.87436 0.6061548250 5.72514096 -2.856678e-01 -1.349696984
## 352 4.00000 0.8569852225 8.75181377 -2.851497e-01 -1.456601829
## 353 4.00000 0.3860918108 4.61765874 -6.910477e-01 -4.133668161
## 354 4.00000 -0.0564338810 -0.67573528 -7.174006e-01 -4.296298589
## 355 91.00000 -0.0968458658 -1.15979401 1.077033e-01 0.645096542
### person-level (variable averages - over time correlations) ###
hpi.corr2<- hpi1[,c("hpi0", "hpi1", "hpi2", "hpi3", "hpi4", "hpi5", "global.wb_0", "global.wb_1",
"global.wb_2", "global.wb_3", "global.wb_4", "global.wb_5")]
cor2<- cor.mtest(hpi.corr2, use="pairwise.complete.obs", conf.level = 0.95)
cor2
## $p
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.000000e+00 2.022878e-50 7.119720e-46 1.062735e-33 3.935544e-34
## [2,] 2.022878e-50 0.000000e+00 4.952628e-69 1.079241e-40 6.074851e-32
## [3,] 7.119720e-46 4.952628e-69 0.000000e+00 4.628201e-62 1.805425e-46
## [4,] 1.062735e-33 1.079241e-40 4.628201e-62 0.000000e+00 3.729997e-48
## [5,] 3.935544e-34 6.074851e-32 1.805425e-46 3.729997e-48 0.000000e+00
## [6,] 2.456001e-30 6.635512e-35 5.348333e-51 1.043763e-51 2.737402e-58
## [7,] 6.860447e-18 1.486280e-09 1.453679e-05 2.740806e-05 7.998538e-04
## [8,] 1.935553e-14 5.569374e-20 1.395121e-12 1.091750e-12 2.476730e-06
## [9,] 2.522821e-15 8.643476e-15 3.516391e-18 1.518657e-18 1.064637e-07
## [10,] 2.190030e-09 2.962397e-10 1.675528e-12 9.257842e-20 3.537378e-08
## [11,] 2.862374e-15 1.539735e-11 5.609500e-13 6.714640e-15 1.581888e-12
## [12,] 1.820768e-11 5.945478e-09 4.552733e-09 4.706247e-09 1.239913e-10
## [,6] [,7] [,8] [,9] [,10]
## [1,] 2.456001e-30 6.860447e-18 1.935553e-14 2.522821e-15 2.190030e-09
## [2,] 6.635512e-35 1.486280e-09 5.569374e-20 8.643476e-15 2.962397e-10
## [3,] 5.348333e-51 1.453679e-05 1.395121e-12 3.516391e-18 1.675528e-12
## [4,] 1.043763e-51 2.740806e-05 1.091750e-12 1.518657e-18 9.257842e-20
## [5,] 2.737402e-58 7.998538e-04 2.476730e-06 1.064637e-07 3.537378e-08
## [6,] 0.000000e+00 6.681257e-03 9.261769e-05 9.771407e-11 4.672458e-10
## [7,] 6.681257e-03 0.000000e+00 5.576464e-49 5.439593e-21 2.082879e-21
## [8,] 9.261769e-05 5.576464e-49 0.000000e+00 2.528753e-46 2.697394e-42
## [9,] 9.771407e-11 5.439593e-21 2.528753e-46 0.000000e+00 5.042286e-60
## [10,] 4.672458e-10 2.082879e-21 2.697394e-42 5.042286e-60 0.000000e+00
## [11,] 7.677773e-13 8.065674e-21 1.131464e-36 8.249151e-45 2.292739e-62
## [12,] 2.127856e-09 1.165884e-20 6.131050e-33 8.509097e-38 1.307361e-46
## [,11] [,12]
## [1,] 2.862374e-15 1.820768e-11
## [2,] 1.539735e-11 5.945478e-09
## [3,] 5.609500e-13 4.552733e-09
## [4,] 6.714640e-15 4.706247e-09
## [5,] 1.581888e-12 1.239913e-10
## [6,] 7.677773e-13 2.127856e-09
## [7,] 8.065674e-21 1.165884e-20
## [8,] 1.131464e-36 6.131050e-33
## [9,] 8.249151e-45 8.509097e-38
## [10,] 2.292739e-62 1.307361e-46
## [11,] 0.000000e+00 4.178045e-82
## [12,] 4.178045e-82 0.000000e+00
##
## $lowCI
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 1.0000000 0.6721960 0.6298933 0.5761450 0.66863461 0.59890201 0.28842720
## [2,] 0.6721960 1.0000000 0.7964454 0.6967603 0.70487721 0.69128725 0.21098161
## [3,] 0.6298933 0.7964454 1.0000000 0.7768753 0.78634048 0.77767167 0.11892827
## [4,] 0.5761450 0.6967603 0.7768753 1.0000000 0.80224195 0.80513631 0.12338709
## [5,] 0.6686346 0.7048772 0.7863405 0.8022420 1.00000000 0.87157425 0.09203687
## [6,] 0.5989020 0.6912873 0.7776717 0.8051363 0.87157425 1.00000000 0.04606191
## [7,] 0.2884272 0.2109816 0.1189283 0.1233871 0.09203687 0.04606191 1.00000000
## [8,] 0.2581804 0.3640256 0.2543396 0.2813402 0.18106293 0.11917975 0.48399982
## [9,] 0.2831666 0.3152485 0.3394782 0.3675536 0.22027792 0.27368313 0.31009729
## [10,] 0.1997043 0.2445948 0.2673086 0.3830756 0.23296274 0.25901626 0.32318208
## [11,] 0.3095144 0.2860755 0.2904845 0.3346512 0.33096114 0.31370261 0.33681052
## [12,] 0.2646274 0.2428524 0.2247626 0.2400780 0.30688914 0.24470214 0.35311954
## [,8] [,9] [,10] [,11] [,12]
## [1,] 0.2581804 0.2831666 0.1997043 0.3095144 0.2646274
## [2,] 0.3640256 0.3152485 0.2445948 0.2860755 0.2428524
## [3,] 0.2543396 0.3394782 0.2673086 0.2904845 0.2247626
## [4,] 0.2813402 0.3675536 0.3830756 0.3346512 0.2400780
## [5,] 0.1810629 0.2202779 0.2329627 0.3309611 0.3068891
## [6,] 0.1191798 0.2736831 0.2590163 0.3137026 0.2447021
## [7,] 0.4839998 0.3100973 0.3231821 0.3368105 0.3531195
## [8,] 1.0000000 0.4947649 0.4832172 0.4726505 0.4677662
## [9,] 0.4947649 1.0000000 0.5778245 0.5265270 0.5050127
## [10,] 0.4832172 0.5778245 1.0000000 0.6182774 0.5624279
## [11,] 0.4726505 0.5265270 0.6182774 1.0000000 0.7241038
## [12,] 0.4677662 0.5050127 0.5624279 0.7241038 1.0000000
##
## $uppCI
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 1.0000000 0.7795157 0.7461605 0.7150648 0.7989465 0.7445689 0.4374570
## [2,] 0.7795157 1.0000000 0.8702942 0.8124146 0.8330624 0.8166698 0.3947693
## [3,] 0.7461605 0.8702942 1.0000000 0.8585691 0.8771570 0.8668247 0.3054234
## [4,] 0.7150648 0.8124146 0.8585691 1.0000000 0.8875020 0.8870016 0.3275949
## [5,] 0.7989465 0.8330624 0.8771570 0.8875020 1.0000000 0.9300272 0.3365621
## [6,] 0.7445689 0.8166698 0.8668247 0.8870016 0.9300272 1.0000000 0.2776157
## [7,] 0.4374570 0.3947693 0.3054234 0.3275949 0.3365621 0.2776157 1.0000000
## [8,] 0.4166563 0.5261146 0.4270548 0.4662356 0.4144678 0.3441135 0.5949990
## [9,] 0.4461937 0.4951078 0.5043492 0.5382612 0.4477324 0.4782683 0.4517486
## [10,] 0.3779708 0.4397608 0.4473862 0.5509692 0.4583593 0.4656499 0.4671661
## [11,] 0.4834470 0.4862201 0.4765949 0.5205264 0.5383431 0.5110091 0.4874156
## [12,] 0.4549276 0.4587454 0.4262964 0.4522255 0.5291080 0.4535951 0.5090339
## [,8] [,9] [,10] [,11] [,12]
## [1,] 0.4166563 0.4461937 0.3779708 0.4834470 0.4549276
## [2,] 0.5261146 0.4951078 0.4397608 0.4862201 0.4587454
## [3,] 0.4270548 0.5043492 0.4473862 0.4765949 0.4262964
## [4,] 0.4662356 0.5382612 0.5509692 0.5205264 0.4522255
## [5,] 0.4144678 0.4477324 0.4583593 0.5383431 0.5291080
## [6,] 0.3441135 0.4782683 0.4656499 0.5110091 0.4535951
## [7,] 0.5949990 0.4517486 0.4671661 0.4874156 0.5090339
## [8,] 1.0000000 0.6099816 0.6033613 0.6016009 0.6041321
## [9,] 0.6099816 1.0000000 0.6807965 0.6455897 0.6343526
## [10,] 0.6033613 0.6807965 1.0000000 0.7184269 0.6798307
## [11,] 0.6016009 0.6455897 0.7184269 1.0000000 0.8035814
## [12,] 0.6041321 0.6343526 0.6798307 0.8035814 1.0000000
#Correlation Matrix
corrplot(cor(hpi.corr2, use="pairwise.complete.obs"), method="number", type="upper",
addCoef.col = "black", tl.col="black", tl.srt=30, p.mat = cor2$p,
sig.level = 0.05, insig = "blank", diag=FALSE)
# pooled within-person level correlations
hpi.corr3<- hpi.wb[,c("time", "hpi", "life.emp", "life.rec", "life.soc", "gaf")]
statsBy(hpi.corr3, c("time"), cors = TRUE, method="pearson")
## Statistics within and between groups
## Call: statsBy(data = hpi.corr3, group = c("time"), cors = TRUE, method = "pearson")
## Intraclass Correlation 1 (Percentage of variance due to groups)
## time hpi life.emp life.rec life.soc gaf
## 1.00 0.03 0.12 0.06 0.11 0.01
## Intraclass Correlation 2 (Reliability of group differences)
## time hpi life.emp life.rec life.soc gaf
## 1.00 0.91 0.98 0.97 0.99 0.83
## eta^2 between groups
## hpi.bg life.emp.bg life.rec.bg life.soc.bg gaf.bg
## 0.02 0.11 0.05 0.10 0.01
##
## To see the correlations between and within groups, use the short=FALSE option in your print statement.
## Many results are not shown directly. To see specific objects select from the following list:
## mean sd n F ICC1 ICC2 ci1 ci2 r r.ci within pooled sd.r raw rbg ci.bg pbg rwg nw ci.wg pwg etabg etawg nwg nG Call
# variables for correlations
hpi.corr<- hpi.wb[,c("hpi", "gaf", "life.soc", "life.rec", "life.emp")]
#Calculating correlations and CIs
cor1 <- cor.mtest(hpi.corr, use="pairwise.complete.obs", conf.level = 0.95)
cor1
## $p
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.000000e+00 1.280433e-93 7.035124e-85 3.284000e-65 3.682346e-30
## [2,] 1.280433e-93 0.000000e+00 0.000000e+00 7.114060e-242 2.652454e-202
## [3,] 7.035124e-85 0.000000e+00 0.000000e+00 3.586150e-317 3.765401e-186
## [4,] 3.284000e-65 7.114060e-242 3.586150e-317 0.000000e+00 1.176423e-83
## [5,] 3.682346e-30 2.652454e-202 3.765401e-186 1.176423e-83 0.000000e+00
##
## $lowCI
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.0000000 0.3895574 -0.4411884 -0.3953785 -0.3291125
## [2,] 0.3895574 1.0000000 -0.7711394 -0.5484127 -0.5818873
## [3,] -0.4411884 -0.7711394 1.0000000 0.5648878 0.5076915
## [4,] -0.3953785 -0.5484127 0.5648878 1.0000000 0.3398503
## [5,] -0.3291125 -0.5818873 0.5076915 0.3398503 1.0000000
##
## $uppCI
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.0000000 0.4594031 -0.3700052 -0.3210544 -0.2375746
## [2,] 0.4594031 1.0000000 -0.7425994 -0.4998811 -0.5276378
## [3,] -0.3700052 -0.7425994 1.0000000 0.6087567 0.5635774
## [4,] -0.3210544 -0.4998811 0.6087567 1.0000000 0.4073910
## [5,] -0.2375746 -0.5276378 0.5635774 0.4073910 1.0000000
#Correlation Matrix
corrplot(cor(hpi.corr, use="pairwise.complete.obs"), method="number", type="upper",
addCoef.col = "black", tl.col="black", tl.srt=45, p.mat = cor1$p,
sig.level = 0.05, insig = "blank", diag=FALSE)
chart.Correlation(hpi.corr, histogram=TRUE)
## LIFE (emp, rec, soc) are reverse scored
# ranges from 1-5 but LIFE scale publication ranges from 1-6
#visualizing hpi over time
ggboxplot(hpi.wb, x = "time", y = "hpi",
color = "time")
ggstripchart(hpi.wb, x = "time", y = "hpi",
add = "mean_sd")
gghistogram(hpi.wb, x = "hpi", bins = 5,
add = "mean", facet.by = "time", add_density = TRUE)
plot(hpi.wb$time, hpi.wb$hpi, xlab = "time", ylab = "hpi", pch = 16, col = "pink",
main = " Scatterplot of HPI over Time")
coef(lm(hpi ~ time, data = hpi.wb))
## (Intercept) time
## -0.34981132 0.02553922
ggplot(hpi.wb) +
geom_point(aes(x = time, y = hpi), col = "pink", size = 6) +
geom_abline(aes(intercept = -0.34981132, slope = 0.02553922), col = "purple") +
ggtitle("Scatterplot of HPI over Time With The Best Fit Line")
mean.hpi <- tapply(hpi.wb$hpi, hpi.wb$time, FUN = mean, na.rm = T)
hpi.data <- data.frame(hpi = mean.hpi, time = 0:5)
plot(mean.hpi ~ time, hpi.data, type = "b", xlab = "time", ylab = "hpi")
# growth model for healthy personality index
hpi.model = '
# intercept and slope with fixed coefficients
i =~ 1*hpi0 + 1*hpi1 + 1*hpi2 + 1*hpi3 + 1*hpi4 + 1*hpi5
s =~ 0*hpi0 + 1*hpi1 + 2*hpi2 + 3*hpi3 + 4*hpi4 + 5*hpi5
i ~~s'
hpi.fit <- lavaan::growth(hpi.model, data=hpi1, missing = "fiml")
summary(hpi.fit)
## lavaan 0.6-8 ended normally after 57 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 11
##
## Number of observations 666
## Number of missing patterns 60
##
## Model Test User Model:
##
## Test statistic 63.903
## Degrees of freedom 16
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## i =~
## hpi0 1.000
## hpi1 1.000
## hpi2 1.000
## hpi3 1.000
## hpi4 1.000
## hpi5 1.000
## s =~
## hpi0 0.000
## hpi1 1.000
## hpi2 2.000
## hpi3 3.000
## hpi4 4.000
## hpi5 5.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## i ~~
## s 0.001 0.001 1.191 0.234
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .hpi0 0.000
## .hpi1 0.000
## .hpi2 0.000
## .hpi3 0.000
## .hpi4 0.000
## .hpi5 0.000
## i -0.351 0.011 -33.036 0.000
## s 0.023 0.002 9.284 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hpi0 0.022 0.002 9.627 0.000
## .hpi1 0.019 0.002 9.312 0.000
## .hpi2 0.015 0.002 9.756 0.000
## .hpi3 0.018 0.002 9.579 0.000
## .hpi4 0.009 0.002 5.763 0.000
## .hpi5 0.010 0.002 5.080 0.000
## i 0.056 0.004 13.459 0.000
## s 0.001 0.000 5.768 0.000
graph_sem(model = hpi.fit)
# growth model for global functioning
gaf.model <- '
# intercept and slope with fixed coefficients
i =~ 1*global.wb_0 + 1*global.wb_1 + 1*global.wb_2 + 1*global.wb_3 + 1*global.wb_4 + 1*global.wb_5
s =~ 0*global.wb_0 + 1*global.wb_1 + 2*global.wb_2 + 3*global.wb_3 + 4*global.wb_4 + 5*global.wb_5
i ~ s'
gaf.fit <- lavaan::growth(gaf.model, data=hpi1, missing = "fiml")
summary(gaf.fit)
## lavaan 0.6-8 ended normally after 145 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 11
##
## Number of observations 666
## Number of missing patterns 8
##
## Model Test User Model:
##
## Test statistic 80.381
## Degrees of freedom 16
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## i =~
## global.wb_0 1.000
## global.wb_1 1.000
## global.wb_2 1.000
## global.wb_3 1.000
## global.wb_4 1.000
## global.wb_5 1.000
## s =~
## global.wb_0 0.000
## global.wb_1 1.000
## global.wb_2 2.000
## global.wb_3 3.000
## global.wb_4 4.000
## global.wb_5 5.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## i ~
## s -0.168 0.388 -0.432 0.666
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .global.wb_0 0.000
## .global.wb_1 0.000
## .global.wb_2 0.000
## .global.wb_3 0.000
## .global.wb_4 0.000
## .global.wb_5 0.000
## .i 58.883 0.460 127.950 0.000
## s 0.595 0.115 5.163 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .global.wb_0 63.910 5.453 11.720 0.000
## .global.wb_1 85.709 5.921 14.477 0.000
## .global.wb_2 69.969 4.987 14.029 0.000
## .global.wb_3 75.960 5.496 13.822 0.000
## .global.wb_4 40.013 3.885 10.299 0.000
## .global.wb_5 42.366 5.103 8.303 0.000
## .i 71.240 6.055 11.765 0.000
## s 3.382 0.474 7.139 0.000
graph_sem(model = gaf.fit)
