HW 1 CFA
2023-04-21
library(psych)
library(lavaan)## This is lavaan 0.6-15
## lavaan is FREE software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
##
## cor2covlibrary(semTools)## ## ################################################################################# This is semTools 0.5-6## All users of R (or SEM) are invited to submit functions or ideas for functions.## #################################################################################
## Attaching package: 'semTools'## The following objects are masked from 'package:psych':
##
## reliability, skewlibrary(semPlot)
library(tibble)#read in data
cfadata <- read.csv ('/Users/misschelsita/Documents/Spring 2023/PSY214/HW1_Data/WLS_HW1_CFA_rev23.csv')# Descriptives: gender (1=male, 2=female)
Gender <- factor(cfadata$sexrsp, levels = 1:2, labels = c("Male", "Female"))
summary(Gender)## Male Female
## 4637 5051# Descriptives: age
library(dplyr) ##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionCFA_data <- mutate(cfadata, Age = 103-(cfadata$byear))
psych::describe(CFA_data$Age) ## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9688 64.16 0.51 64 64.1 0 63 66 3 1.1 2.71 0.01#average years of education
library(tidyverse) ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.5
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ ggplot2::%+%() masks psych::%+%()
## ✖ ggplot2::alpha() masks psych::alpha()
## ✖ readr::clipboard() masks semTools::clipboard()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()CFA_data$educ92 <- as.numeric(CFA_data$educ92)
psych::describe(CFA_data$educ92)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 8452 13.61 2.26 12 13.21 0 12 21 9 1.14 0.04 0.02# Nationality on Father's side
glimpse(CFA_data$ge096fa) ## int [1:9688] NA 14 4 NA 2 14 14 NA 14 14 ...CFA_data$ge096fa <- as.factor(CFA_data$ge096fa)
summary(CFA_data$ge096fa)## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 633 108 52 463 549 166 128 182 6 82 81 231 4 3417 474 202
## 17 18 19 20 22 23 24 25 26 27 28 29 30 31 32 33
## 43 21 47 4 20 10 91 3 8 21 55 2 1 1 1 1
## 34 35 36 37 38 39 40 41 42 NA's
## 1 5 16 11 20 9 16 11 4 2488# Nationality on Mother's side
glimpse(CFA_data$ge095ma) ## int [1:9688] NA 4 1 NA 41 14 4 NA 14 14 ...CFA_data$ge095ma <- as.factor(CFA_data$ge095ma)
summary(CFA_data$ge095ma)## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 622 91 38 495 554 151 104 168 28 75 81 218 14 3106 527 192
## 17 18 19 20 22 23 24 25 26 27 28 30 32 33 35 36
## 48 31 59 3 50 7 80 5 8 32 36 1 1 1 2 16
## 37 38 39 40 41 42 43 44 46 47 NA's
## 8 58 26 27 32 11 3 1 1 1 2676#item means, SDs, Skewness etc…
describe(cfadata$in501rer)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6508 4.92 1.86 5 5.15 1.48 1 7 6 -0.82 -0.36 0.02describe(cfadata$in502rer)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6628 4.69 2.09 5 4.86 2.97 1 7 6 -0.5 -1.09 0.03describe(cfadata$in503rer)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6581 5.84 1.47 6 6.12 1.48 1 7 6 -1.55 2.04 0.02describe(cfadata$in504rer)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6574 3.83 1.73 4 3.83 1.48 1 7 6 -0.02 -0.87 0.02describe(cfadata$in505rer)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6489 3.27 1.82 3 3.15 2.97 1 7 6 0.33 -0.98 0.02describe(cfadata$in506rer)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6569 3.17 1.81 3 3.04 1.48 1 7 6 0.39 -0.93 0.02describe(cfadata$in507rer)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6573 3.18 1.9 3 3.02 2.97 1 7 6 0.43 -0.96 0.02#TWOFACTOR MODEL
CFA_PII <-
'F1 = ~ in501rer + in502rer + in503rer
F2 = ~ in504rer + in505rer + in506rer + in507rer'#TWOFACTOR MODEL FIT INDICES
CFA_PIIfit <- cfa(CFA_PII, data = cfadata)
summary(CFA_PIIfit, standard = TRUE, fit.measures = TRUE)## lavaan 0.6.15 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 15
##
## Used Total
## Number of observations 6312 9688
##
## Model Test User Model:
##
## Test statistic 1283.951
## Degrees of freedom 13
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 11105.204
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.885
## Tucker-Lewis Index (TLI) 0.815
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -83717.758
## Loglikelihood unrestricted model (H1) -83075.783
##
## Akaike (AIC) 167465.517
## Bayesian (BIC) 167566.770
## Sample-size adjusted Bayesian (SABIC) 167519.104
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.124
## 90 Percent confidence interval - lower 0.119
## 90 Percent confidence interval - upper 0.130
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.058
##
## 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
## F1 =~
## in501rer 1.000 0.984 0.530
## in502rer 1.352 0.047 28.747 0.000 1.330 0.637
## in503rer 0.839 0.030 27.522 0.000 0.825 0.562
## F2 =~
## in504rer 1.000 1.315 0.762
## in505rer 1.122 0.021 52.958 0.000 1.475 0.811
## in506rer 0.754 0.019 39.452 0.000 0.991 0.550
## in507rer 0.755 0.020 37.750 0.000 0.992 0.526
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## F1 ~~
## F2 0.896 0.035 25.726 0.000 0.693 0.693
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .in501rer 2.484 0.054 45.658 0.000 2.484 0.720
## .in502rer 2.594 0.069 37.437 0.000 2.594 0.595
## .in503rer 1.474 0.034 43.616 0.000 1.474 0.684
## .in504rer 1.249 0.034 36.875 0.000 1.249 0.419
## .in505rer 1.134 0.037 30.436 0.000 1.134 0.342
## .in506rer 2.269 0.045 50.640 0.000 2.269 0.698
## .in507rer 2.580 0.050 51.314 0.000 2.580 0.724
## F1 0.968 0.053 18.210 0.000 1.000 1.000
## F2 1.729 0.054 31.734 0.000 1.000 1.000semPaths(CFA_PIIfit)
inspect(cfa(CFA_PIIfit, cfadata), what ="std")## $lambda
## F1 F2
## in501rer 0.530 0.000
## in502rer 0.637 0.000
## in503rer 0.562 0.000
## in504rer 0.000 0.762
## in505rer 0.000 0.811
## in506rer 0.000 0.550
## in507rer 0.000 0.526
##
## $theta
## in501r in502r in503r in504r in505r in506r in507r
## in501rer 0.720
## in502rer 0.000 0.595
## in503rer 0.000 0.000 0.684
## in504rer 0.000 0.000 0.000 0.419
## in505rer 0.000 0.000 0.000 0.000 0.342
## in506rer 0.000 0.000 0.000 0.000 0.000 0.698
## in507rer 0.000 0.000 0.000 0.000 0.000 0.000 0.724
##
## $psi
## F1 F2
## F1 1.000
## F2 0.693 1.000#NULL MODEL
PII_0factor <-
'
in501rer ~~ in501rer
in502rer ~~ in502rer
in503rer ~~ in503rer
in504rer ~~ in504rer
in505rer ~~ in505rer
in506rer ~~ in506rer
in507rer ~~ in507rer
'
(PII_0factor)## [1] "\n in501rer ~~ in501rer\n in502rer ~~ in502rer\n in503rer ~~ in503rer\n in504rer ~~ in504rer\n in505rer ~~ in505rer\n in506rer ~~ in506rer\n in507rer ~~ in507rer\n"PII_0factorfit <- cfa(PII_0factor, data = cfadata)
summary(PII_0factorfit, standard = TRUE, fit.measures = TRUE)## lavaan 0.6.15 ended normally after 9 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
##
## Used Total
## Number of observations 6312 9688
##
## Model Test User Model:
##
## Test statistic 11105.204
## Degrees of freedom 21
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 11105.204
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.000
## Tucker-Lewis Index (TLI) -0.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -88628.385
## Loglikelihood unrestricted model (H1) -83075.783
##
## Akaike (AIC) 177270.770
## Bayesian (BIC) 177318.022
## Sample-size adjusted Bayesian (SABIC) 177295.778
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.289
## 90 Percent confidence interval - lower 0.285
## 90 Percent confidence interval - upper 0.294
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.300
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## in501rer 3.451 0.061 56.178 0.000 3.451 1.000
## in502rer 4.361 0.078 56.178 0.000 4.361 1.000
## in503rer 2.154 0.038 56.178 0.000 2.154 1.000
## in504rer 2.978 0.053 56.178 0.000 2.978 1.000
## in505rer 3.310 0.059 56.178 0.000 3.310 1.000
## in506rer 3.251 0.058 56.178 0.000 3.251 1.000
## in507rer 3.565 0.063 56.178 0.000 3.565 1.000semPaths(PII_0factorfit)
#ONE FACTOR MODEL
CFA_PIIwhole <-
'
F1 = ~ in501rer + in502rer + in503rer + in504rer + in505rer + in506rer + in507rer'CFA_PIIwholefit <- cfa(CFA_PIIwhole, data = cfadata)
summary(CFA_PIIwholefit, standard = TRUE, fit.measures = TRUE)## lavaan 0.6.15 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Used Total
## Number of observations 6312 9688
##
## Model Test User Model:
##
## Test statistic 1858.735
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 11105.204
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.834
## Tucker-Lewis Index (TLI) 0.750
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -84005.150
## Loglikelihood unrestricted model (H1) -83075.783
##
## Akaike (AIC) 168038.301
## Bayesian (BIC) 168132.804
## Sample-size adjusted Bayesian (SABIC) 168088.315
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.144
## 90 Percent confidence interval - lower 0.139
## 90 Percent confidence interval - upper 0.150
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.072
##
## 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
## F1 =~
## in501rer 1.000 0.840 0.452
## in502rer 1.241 0.046 26.990 0.000 1.042 0.499
## in503rer 0.689 0.030 23.317 0.000 0.579 0.394
## in504rer 1.563 0.048 32.499 0.000 1.312 0.760
## in505rer 1.693 0.052 32.716 0.000 1.421 0.781
## in506rer 1.170 0.041 28.303 0.000 0.982 0.545
## in507rer 1.198 0.043 27.973 0.000 1.006 0.533
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .in501rer 2.746 0.052 52.954 0.000 2.746 0.796
## .in502rer 3.276 0.063 51.999 0.000 3.276 0.751
## .in503rer 1.820 0.034 53.875 0.000 1.820 0.845
## .in504rer 1.256 0.033 38.064 0.000 1.256 0.422
## .in505rer 1.291 0.036 35.663 0.000 1.291 0.390
## .in506rer 2.286 0.045 50.833 0.000 2.286 0.703
## .in507rer 2.554 0.050 51.167 0.000 2.554 0.716
## F1 0.705 0.041 17.178 0.000 1.000 1.000semPaths(CFA_PIIwholefit)
#CORRELATION MATRIX
corMat <- cor(cfadata, use ="complete.obs", method = "pearson")
corMat## idpub sexrsp byear educ92 natfth
## idpub 1.000000000 -0.001726446 -0.0019951064 -0.002791839 0.013576254
## sexrsp -0.001726446 1.000000000 0.1116239357 -0.161011874 0.023622399
## byear -0.001995106 0.111623936 1.0000000000 0.126919993 -0.016599634
## educ92 -0.002791839 -0.161011874 0.1269199928 1.000000000 -0.053486500
## natfth 0.013576254 0.023622399 -0.0165996340 -0.053486500 1.000000000
## ge096fa 0.013576254 0.023622399 -0.0165996340 -0.053486500 1.000000000
## ge095ma 0.022276442 0.004042665 -0.0026156014 -0.035639753 0.248740762
## in401rer 0.011689778 -0.070828519 -0.0541280361 -0.148256981 0.016761058
## in402rer -0.014994311 -0.042282321 0.0471153024 0.181576614 -0.024932426
## in403rer 0.017916090 0.046420778 0.0023702204 -0.081582133 0.019443023
## in404rer 0.007922960 0.080476219 0.0068846762 -0.036623500 -0.001492366
## in405rer 0.008152574 0.050904412 -0.0011701778 -0.056675205 0.020357581
## in406rer 0.028592601 -0.110937045 -0.0266344898 -0.061693844 0.009501048
## in501rer 0.007832217 -0.065899957 -0.0024193977 0.058085592 0.005006225
## in502rer -0.001718329 0.190509083 -0.0006636198 -0.136921648 0.053711174
## in503rer 0.003687468 0.061433678 0.0031019034 -0.041380011 0.034019816
## in504rer 0.004939038 0.049190399 0.0089640857 0.001373321 0.009180015
## in505rer 0.007775497 -0.021386917 -0.0335469047 -0.031195484 0.012667865
## in506rer -0.004671209 -0.050598070 -0.0353592293 0.053139685 0.019404320
## in507rer 0.019102923 -0.016984536 -0.0533448837 -0.036126078 0.048248008
## ge096fa ge095ma in401rer in402rer in403rer
## idpub 0.013576254 0.022276442 0.01168978 -0.014994311 0.01791609
## sexrsp 0.023622399 0.004042665 -0.07082852 -0.042282321 0.04642078
## byear -0.016599634 -0.002615601 -0.05412804 0.047115302 0.00237022
## educ92 -0.053486500 -0.035639753 -0.14825698 0.181576614 -0.08158213
## natfth 1.000000000 0.248740762 0.01676106 -0.024932426 0.01944302
## ge096fa 1.000000000 0.248740762 0.01676106 -0.024932426 0.01944302
## ge095ma 0.248740762 1.000000000 0.03564340 -0.008407295 0.01298388
## in401rer 0.016761058 0.035643400 1.00000000 -0.259856705 0.24375347
## in402rer -0.024932426 -0.008407295 -0.25985670 1.000000000 -0.37302253
## in403rer 0.019443023 0.012983876 0.24375347 -0.373022533 1.00000000
## in404rer -0.001492366 -0.019799795 0.23909409 -0.273672094 0.49393932
## in405rer 0.020357581 -0.003661223 0.26388558 -0.306511998 0.45583077
## in406rer 0.009501048 0.017255210 0.56331158 -0.229012735 0.25102206
## in501rer 0.005006225 -0.010789866 -0.17150589 0.113850977 -0.11617398
## in502rer 0.053711174 0.009539007 -0.09764104 0.022444270 -0.03477849
## in503rer 0.034019816 0.015590934 -0.12767719 0.100362821 -0.10067675
## in504rer 0.009180015 -0.004178790 -0.17768112 0.094579107 -0.11671487
## in505rer 0.012667865 -0.004464709 -0.13222632 0.078099241 -0.12704148
## in506rer 0.019404320 -0.011416499 -0.09401632 0.077849385 -0.10645859
## in507rer 0.048248008 0.049526690 -0.06648286 0.031627589 -0.08951338
## in404rer in405rer in406rer in501rer in502rer
## idpub 0.007922960 0.008152574 0.028592601 0.007832217 -0.0017183287
## sexrsp 0.080476219 0.050904412 -0.110937045 -0.065899957 0.1905090826
## byear 0.006884676 -0.001170178 -0.026634490 -0.002419398 -0.0006636198
## educ92 -0.036623500 -0.056675205 -0.061693844 0.058085592 -0.1369216476
## natfth -0.001492366 0.020357581 0.009501048 0.005006225 0.0537111742
## ge096fa -0.001492366 0.020357581 0.009501048 0.005006225 0.0537111742
## ge095ma -0.019799795 -0.003661223 0.017255210 -0.010789866 0.0095390070
## in401rer 0.239094091 0.263885583 0.563311582 -0.171505887 -0.0976410437
## in402rer -0.273672094 -0.306511998 -0.229012735 0.113850977 0.0224442704
## in403rer 0.493939323 0.455830765 0.251022064 -0.116173979 -0.0347784923
## in404rer 1.000000000 0.721371807 0.293800519 -0.110351700 -0.0163471671
## in405rer 0.721371807 1.000000000 0.318702226 -0.127356351 -0.0388363230
## in406rer 0.293800519 0.318702226 1.000000000 -0.163439779 -0.1451720455
## in501rer -0.110351700 -0.127356351 -0.163439779 1.000000000 0.2719723130
## in502rer -0.016347167 -0.038836323 -0.145172046 0.271972313 1.0000000000
## in503rer -0.071101408 -0.109544346 -0.162672525 0.308770901 0.3940479566
## in504rer -0.137113375 -0.140284465 -0.212909146 0.309063136 0.4032461105
## in505rer -0.153247727 -0.149609500 -0.151379742 0.311410414 0.3148317182
## in506rer -0.110471407 -0.113232718 -0.093054410 0.223492083 0.2125153479
## in507rer -0.118920736 -0.123175880 -0.084948162 0.253413367 0.2770119659
## in503rer in504rer in505rer in506rer in507rer
## idpub 0.003687468 0.004939038 0.007775497 -0.004671209 0.01910292
## sexrsp 0.061433678 0.049190399 -0.021386917 -0.050598070 -0.01698454
## byear 0.003101903 0.008964086 -0.033546905 -0.035359229 -0.05334488
## educ92 -0.041380011 0.001373321 -0.031195484 0.053139685 -0.03612608
## natfth 0.034019816 0.009180015 0.012667865 0.019404320 0.04824801
## ge096fa 0.034019816 0.009180015 0.012667865 0.019404320 0.04824801
## ge095ma 0.015590934 -0.004178790 -0.004464709 -0.011416499 0.04952669
## in401rer -0.127677188 -0.177681122 -0.132226322 -0.094016316 -0.06648286
## in402rer 0.100362821 0.094579107 0.078099241 0.077849385 0.03162759
## in403rer -0.100676749 -0.116714874 -0.127041480 -0.106458587 -0.08951338
## in404rer -0.071101408 -0.137113375 -0.153247727 -0.110471407 -0.11892074
## in405rer -0.109544346 -0.140284465 -0.149609500 -0.113232718 -0.12317588
## in406rer -0.162672525 -0.212909146 -0.151379742 -0.093054410 -0.08494816
## in501rer 0.308770901 0.309063136 0.311410414 0.223492083 0.25341337
## in502rer 0.394047957 0.403246110 0.314831718 0.212515348 0.27701197
## in503rer 1.000000000 0.299388850 0.220135060 0.153220731 0.19328911
## in504rer 0.299388850 1.000000000 0.644585142 0.333868249 0.31029653
## in505rer 0.220135060 0.644585142 1.000000000 0.436586090 0.38371253
## in506rer 0.153220731 0.333868249 0.436586090 1.000000000 0.49556653
## in507rer 0.193289114 0.310296528 0.383712532 0.495566531 1.00000000#COVARIANCE MATRIX
covMat <- cov(cfadata, use ="complete.obs", method = "pearson")
covMat## idpub sexrsp byear educ92 natfth
## idpub 9.601165e+07 -8.44410429 -9.5122234297 -63.401050685 824.594883490
## sexrsp -8.444104e+00 0.24915939 0.0271112781 -0.186269050 0.073090575
## byear -9.512223e+00 0.02711128 0.2367598489 0.143129195 -0.050066966
## educ92 -6.340105e+01 -0.18626905 0.1431291948 5.371410266 -0.768399502
## natfth 8.245949e+02 0.07309057 -0.0500669660 -0.768399502 38.423548464
## ge096fa 8.245949e+02 0.07309057 -0.0500669660 -0.768399502 38.423548464
## ge095ma 1.549590e+03 0.01432569 -0.0090351370 -0.586391950 10.945984952
## in401rer 7.727461e+01 -0.02385148 -0.0177682607 -0.231807741 0.070092055
## in402rer -1.223841e+02 -0.01758061 0.0190964425 0.350542384 -0.128735917
## in403rer 1.264859e+02 0.01669505 0.0008309589 -0.136231022 0.086835904
## in404rer 5.471779e+01 0.02831297 0.0023611127 -0.059825065 -0.006520084
## in405rer 5.286358e+01 0.01681489 -0.0003767955 -0.086923482 0.083507361
## in406rer 1.876218e+02 -0.03708368 -0.0086789265 -0.095753266 0.039440086
## in501rer 1.412809e+02 -0.06055648 -0.0021671958 0.247827467 0.057127598
## in502rer -3.500094e+01 0.19768141 -0.0006712509 -0.659671602 0.692109629
## in503rer 5.218306e+01 0.04428784 0.0021798252 -0.138507773 0.304558049
## in504rer 8.292900e+01 0.04207470 0.0074741553 0.005454038 0.097508893
## in505rer 1.384102e+02 -0.01939388 -0.0296540761 -0.131345078 0.142652723
## in506rer -8.240193e+01 -0.04546934 -0.0309744018 0.221722237 0.216542566
## in507rer 3.520843e+02 -0.01594693 -0.0488238093 -0.157488860 0.562552399
## ge096fa ge095ma in401rer in402rer in403rer
## idpub 824.594883490 1.549590e+03 77.27461330 -122.38407821 1.264859e+02
## sexrsp 0.073090575 1.432569e-02 -0.02385148 -0.01758061 1.669505e-02
## byear -0.050066966 -9.035137e-03 -0.01776826 0.01909644 8.309589e-04
## educ92 -0.768399502 -5.863920e-01 -0.23180774 0.35054238 -1.362310e-01
## natfth 38.423548464 1.094598e+01 0.07009206 -0.12873592 8.683590e-02
## ge096fa 38.423548464 1.094598e+01 0.07009206 -0.12873592 8.683590e-02
## ge095ma 10.945984952 5.039858e+01 0.17070919 -0.04971666 6.641257e-02
## in401rer 0.070092055 1.707092e-01 0.45513186 -0.14602897 1.184830e-01
## in402rer -0.128735917 -4.971666e-02 -0.14602897 0.69386171 -2.238764e-01
## in403rer 0.086835904 6.641257e-02 0.11848305 -0.22387641 5.191270e-01
## in404rer -0.006520084 -9.907157e-02 0.11368853 -0.16067420 2.508358e-01
## in405rer 0.083507361 -1.720027e-02 0.11781054 -0.16895996 2.173403e-01
## in406rer 0.039440086 8.203459e-02 0.25449831 -0.12775082 1.211201e-01
## in501rer 0.057127598 -1.410139e-01 -0.21300241 0.17458627 -1.540929e-01
## in502rer 0.692109629 1.407744e-01 -0.13693439 0.03886453 -5.209056e-02
## in503rer 0.304558049 1.598530e-01 -0.12440027 0.12073931 -1.047624e-01
## in504rer 0.097508893 -5.083488e-02 -0.20540547 0.13500000 -1.441004e-01
## in505rer 0.142652723 -5.758113e-02 -0.16205575 0.11818468 -1.662877e-01
## in506rer 0.216542566 -1.459111e-01 -0.11418734 0.11674486 -1.380904e-01
## in507rer 0.562552399 6.613530e-01 -0.08436517 0.04955499 -1.213136e-01
## in404rer in405rer in406rer in501rer in502rer
## idpub 54.717792883 52.8635774804 187.621768731 141.280929188 -3.500094e+01
## sexrsp 0.028312966 0.0168148855 -0.037083680 -0.060556480 1.976814e-01
## byear 0.002361113 -0.0003767955 -0.008678926 -0.002167196 -6.712509e-04
## educ92 -0.059825065 -0.0869234816 -0.095753266 0.247827467 -6.596716e-01
## natfth -0.006520084 0.0835073614 0.039440086 0.057127598 6.921096e-01
## ge096fa -0.006520084 0.0835073614 0.039440086 0.057127598 6.921096e-01
## ge095ma -0.099071565 -0.0172002664 0.082034587 -0.141013942 1.407744e-01
## in401rer 0.113688527 0.1178105379 0.254498309 -0.213002406 -1.369344e-01
## in402rer -0.160674202 -0.1689599582 -0.127750819 0.174586267 3.886453e-02
## in403rer 0.250835832 0.2173403109 0.121120120 -0.154092861 -5.209056e-02
## in404rer 0.496773421 0.3364636830 0.138675370 -0.143184188 -2.395153e-02
## in405rer 0.336463683 0.4379249681 0.141238322 -0.155151979 -5.342561e-02
## in406rer 0.138675370 0.1412383224 0.448471862 -0.201494049 -2.020980e-01
## in501rer -0.143184188 -0.1551519785 -0.201494049 3.389016730 1.040814e+00
## in502rer -0.023951526 -0.0534256073 -0.202098042 1.040813847 4.321386e+00
## in503rer -0.072376364 -0.1046957864 -0.157333494 0.820942240 1.183041e+00
## in504rer -0.165600289 -0.1590785147 -0.244322808 0.974958561 1.436428e+00
## in505rer -0.196223552 -0.1798609738 -0.184167620 1.041472733 1.188962e+00
## in506rer -0.140176486 -0.1349018462 -0.112189101 0.740704716 7.953310e-01
## in507rer -0.157660037 -0.1533240876 -0.107005586 0.877508307 1.083166e+00
## in503rer in504rer in505rer in506rer in507rer
## idpub 52.183060703 82.929004524 138.41017741 -82.40192954 352.08427852
## sexrsp 0.044287844 0.042074704 -0.01939388 -0.04546934 -0.01594693
## byear 0.002179825 0.007474155 -0.02965408 -0.03097440 -0.04882381
## educ92 -0.138507773 0.005454038 -0.13134508 0.22172224 -0.15748886
## natfth 0.304558049 0.097508893 0.14265272 0.21654257 0.56255240
## ge096fa 0.304558049 0.097508893 0.14265272 0.21654257 0.56255240
## ge095ma 0.159852956 -0.050834877 -0.05758113 -0.14591106 0.66135298
## in401rer -0.124400266 -0.205405469 -0.16205575 -0.11418734 -0.08436517
## in402rer 0.120739313 0.134999998 0.11818468 0.11674486 0.04955499
## in403rer -0.104762381 -0.144100371 -0.16628775 -0.13809044 -0.12131364
## in404rer -0.072376364 -0.165600289 -0.19622355 -0.14017649 -0.15766004
## in405rer -0.104695786 -0.159078515 -0.17986097 -0.13490185 -0.15332409
## in406rer -0.157333494 -0.244322808 -0.18416762 -0.11218910 -0.10700559
## in501rer 0.820942240 0.974958561 1.04147273 0.74070472 0.87750831
## in502rer 1.183041477 1.436427764 1.18896181 0.79533102 1.08316570
## in503rer 2.085829293 0.740929859 0.57757251 0.39838504 0.52508745
## in504rer 0.740929859 2.936325435 2.00659765 1.02996703 1.00014708
## in505rer 0.577572509 2.006597651 3.30031833 1.42788726 1.31119946
## in506rer 0.398385040 1.029967028 1.42788726 3.24109842 1.67815835
## in507rer 0.525087448 1.000147079 1.31119946 1.67815835 3.53809689cmat=matrix(covMat,ncol=20)
cat("The Determinant of Cov is", det(cmat))## The Determinant of Cov is 0mean(cfadata$byear, na.rm=TRUE)## [1] 38.84486#38.84
mean(cfadata$educ92, na.rm=TRUE)## [1] 13.60968#13.61#PATH ANALYSIS - I WANTED TO PRACTICE BUT DID NOT HAVE TIME TO WRITE IT UP DEDICATE MOST OF MY TIME TO CFA
#full model
library(lavaan);
modelData <- read.csv ('/Users/misschelsita/Documents/Spring 2023/PSY214/HW1_Data/WLS_HW1_PathAnalysis.csv', header = TRUE) ;
model<-"
! regressions
TotRecall_2011 ~ TotRecall_2003__TotRecall_2011*TotRecall_2003
TotRecall_2011 ~ 1.0*HUI3_MemProb_03
TotRecall_2003 ~ sexrsp__TotRecall_2003*sexrsp
HUI3_MemProb_03 ~ 1.0*TotRecall_2003
TotRecall_2003 ~ byear__TotRecall_2003*byear
TotRecall_2003 ~ educ92__TotRecall_2003*educ92
TotRecall_2003 ~ Simil92__TotRecall_2003*Simil92
! residuals, variances and covariances
sexrsp ~~ 1.0*sexrsp
byear ~~ 1.0*byear
educ92 ~~ 1.0*educ92
Simil92 ~~ 1.0*Simil92
HUI3_MemProb_03 ~~ VAR_HUI3_MemProb_03*HUI3_MemProb_03
TotRecall_2003 ~~ VAR_TotRecall_2003*TotRecall_2003
TotRecall_2011 ~~ VAR_TotRecall_2011*TotRecall_2011
educ92 ~~ COV_educ92_byear*byear
byear ~~ COV_byear_sexrsp*sexrsp
Simil92 ~~ COV_Simil92_educ92*educ92
! means
sexrsp~0*1;
byear~0*1;
educ92~0*1;
Simil92~0*1;
HUI3_MemProb_03~0*1;
TotRecall_2003~0*1;
TotRecall_2011~0*1;
";
result<-lavaan(model, data=modelData, fixed.x=FALSE, missing="FIML");## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan
## WARNING: some observed variances are (at least) a factor 1000 times larger than
## others; use varTable(fit) to investigatesummary(result, fit.measures=TRUE)## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68617e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68275e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68113e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68035e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68017e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68013e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68011e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=1.47447e+08, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=1.47447e+08, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=1.40074e+08, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=7.00372e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=6.82863e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=5.16525e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=4.33355e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.91771e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.70979e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.70459e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.70212e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.70095e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6898e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68423e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68145e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68138e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68072e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68039e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68022e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68014e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68014e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68012e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68011e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.68011e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=3.68617e+07, f=24,
## theta=3.6801e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61754e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61268e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61254e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61247e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61244e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=1.04702e+08, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=1.04702e+08, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=9.94666e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=4.97333e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=4.84899e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=3.66783e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=3.07725e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.78196e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.63431e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.63062e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.62887e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.62803e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.62012e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61616e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61419e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.6132e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.6127e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61246e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61245e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61245e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61245e+07, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.61754e+07, f=24,
## theta=2.61243e+07, ..): not converged in 1000000 iter.## lavaan 0.6.15 ended normally after 76 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 11
##
## Number of observations 9688
## Number of missing patterns 20
##
## Model Test User Model:
##
## Test statistic 36861707.750
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3662.727
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.000
## Tucker-Lewis Index (TLI) -8855.779
##
## Robust Comparative Fit Index (CFI) 0.000
## Robust Tucker-Lewis Index (TLI) -6102.297
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -18533927.220
## Loglikelihood unrestricted model (H1) -103073.345
##
## Akaike (AIC) 37067876.440
## Bayesian (BIC) 37067955.405
## Sample-size adjusted Bayesian (SABIC) 37067920.448
##
## Root Mean Square Error of Approximation:
##
## RMSEA 12.591
## 90 Percent confidence interval - lower 12.581
## 90 Percent confidence interval - upper 12.581
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Robust RMSEA 13.166
## 90 Percent confidence interval - lower 13.153
## 90 Percent confidence interval - upper 13.153
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 22.408
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## TotRecall_2011 ~
## TR_2003 (TR_2) 0.682 0.005 148.567 0.000
## HUI3_MP 1.000
## TotRecall_2003 ~
## sexrsp (s__T) 1.952 0.094 20.764 0.000
## HUI3_MemProb_03 ~
## TR_2003 1.000
## TotRecall_2003 ~
## byear (b__T) 0.075 0.009 8.762 0.000
## educ92 (e92_) 0.235 0.022 10.581 0.000
## Simil92 (S92_) 0.011 0.003 4.267 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## byear ~~
## educ92 (COV_9) 0.288 0.001 377.854 0.000
## sexrsp ~~
## byear (COV__) 0.039 0.000 150.060 0.000
## educ92 ~~
## Siml92 (COV_S) 0.050 0.001 86.730 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## sexrsp 0.000
## byear 0.000
## educ92 0.000
## Simil92 0.000
## .HUI3_MemPrb_03 0.000
## .TotRecall_2003 0.000
## .TotRecall_2011 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## s 1.000
## b 1.000
## e 1.000
## S 1.000
## .H (VAR_H) 81.004 1.376 58.890 0.000
## .T (VAR_TR_200) 12.080 0.231 52.405 0.000
## .T (VAR_TR_201) 10.437 0.226 46.140 0.000#remove direct path
model2 <-"
! regressions
TotRecall_2011 ~ 1.0*HUI3_MemProb_03
TotRecall_2003 ~ sexrsp__TotRecall_2003*sexrsp
TotRecall_2003 ~ byear__TotRecall_2003*byear
TotRecall_2003 ~ educ92__TotRecall_2003*educ92
TotRecall_2003 ~ Simil92__TotRecall_2003*Simil92
HUI3_MemProb_03 ~ 1.0*TotRecall_2003
! residuals, variances and covariances
sexrsp ~~ 1.0*sexrsp
byear ~~ 1.0*byear
educ92 ~~ 1.0*educ92
Simil92 ~~ 1.0*Simil92
HUI3_MemProb_03 ~~ VAR_HUI3_MemProb_03*HUI3_MemProb_03
TotRecall_2003 ~~ VAR_TotRecall_2003*TotRecall_2003
TotRecall_2011 ~~ VAR_TotRecall_2011*TotRecall_2011
educ92 ~~ COV_educ92_byear*byear
byear ~~ COV_byear_sexrsp*sexrsp
Simil92 ~~ COV_Simil92_educ92*educ92
! observed means
sexrsp~1;
byear~1;
educ92~1;
Simil92~1;
HUI3_MemProb_03~1;
TotRecall_2003~1;
TotRecall_2011~1;
";
result2<-lavaan(model2, data=modelData, fixed.x=FALSE, missing="FIML");## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan
## WARNING: some observed variances are (at least) a factor 1000 times larger than
## others; use varTable(fit) to investigatesummary(result2, fit.measures=TRUE);## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.49628e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48701e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48261e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48052e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=9.9851e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=9.9851e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=9.48585e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=4.74292e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=4.62435e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=3.49791e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.93468e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.65307e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.51227e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.50875e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.50708e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.49119e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48325e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48305e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48116e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48112e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48067e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48066e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48055e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48055e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48052e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.49628e+06, f=18,
## theta=2.48051e+06, ..): not converged in 1000000 iter.## lavaan 0.6.15 ended normally after 79 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 17
##
## Number of observations 9688
## Number of missing patterns 20
##
## Model Test User Model:
##
## Test statistic 2496275.856
## Degrees of freedom 18
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3662.727
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.000
## Tucker-Lewis Index (TLI) -798.703
##
## Robust Comparative Fit Index (CFI) 0.000
## Robust Tucker-Lewis Index (TLI) -583.151
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1351211.273
## Loglikelihood unrestricted model (H1) -103073.345
##
## Akaike (AIC) 2702456.546
## Bayesian (BIC) 2702578.583
## Sample-size adjusted Bayesian (SABIC) 2702524.559
##
## Root Mean Square Error of Approximation:
##
## RMSEA 3.783
## 90 Percent confidence interval - lower 3.772
## 90 Percent confidence interval - upper 3.772
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Robust RMSEA 4.073
## 90 Percent confidence interval - lower 4.068
## 90 Percent confidence interval - upper 4.078
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 5.100
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## TotRecall_2011 ~
## HUI3_MP 1.000
## TotRecall_2003 ~
## sexrsp (s__T) 1.744 0.084 20.754 0.000
## byear (b__T) 0.224 0.085 2.641 0.008
## educ92 (e92_) 0.206 0.020 10.384 0.000
## Simil92 (S92_) 0.012 0.003 4.581 0.000
## HUI3_MemProb_03 ~
## TR_2003 1.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## byear ~~
## educ92 (COV_9) 0.054 0.004 15.352 0.000
## sexrsp ~~
## byear (COV__) 0.748 0.004 179.316 0.000
## educ92 ~~
## Siml92 (COV_S) 0.049 0.001 77.610 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## sexrsp 1.521 0.010 149.744 0.000
## byear 38.845 0.010 3823.408 0.000
## educ92 13.610 0.011 1251.784 0.000
## Simil92 48.534 0.011 4441.630 0.000
## .HUI3_MemPrb_03 -8.565 0.045 -189.941 0.000
## .TotRecall_2003 -4.711 3.250 -1.449 0.147
## .TotRecall_2011 7.397 0.047 156.174 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## s 1.000
## b 1.000
## e 1.000
## S 1.000
## .H (VAR_H) 12.852 0.219 58.575 0.000
## .T (VAR_TR_200) 10.530 0.183 57.694 0.000
## .T (VAR_TR_201) 9.815 0.209 46.909 0.000library(lavaan);
modelNULL<-"
! regressions
! residuals, variances and covariances
sexrsp ~~ 1.0*sexrsp
byear ~~ 1.0*byear
educ92 ~~ 1.0*educ92
Simil92 ~~ 1.0*Simil92
HUI3_MemProb_03 ~~ VAR_HUI3_MemProb_03*HUI3_MemProb_03
TotRecall_2003 ~~ VAR_TotRecall_2003*TotRecall_2003
TotRecall_2011 ~~ VAR_TotRecall_2011*TotRecall_2011
! observed means
sexrsp~1;
byear~1;
educ92~1;
Simil92~1;
HUI3_MemProb_03~1;
TotRecall_2003~1;
TotRecall_2011~1;
";
result3<-lavaan(modelNULL, data=modelData, fixed.x=FALSE, missing="FIML");## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan
## WARNING: some observed variances are (at least) a factor 1000 times larger than
## others; use varTable(fit) to investigatesummary(result3, fit.measures=TRUE);## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.48574e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47651e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47213e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47005e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47002e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=9.94295e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=9.94295e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=9.4458e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=4.7229e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=4.60483e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=3.48314e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.9223e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.64187e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.50166e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.49816e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.49649e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.48067e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47277e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47257e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47069e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47064e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.4702e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47018e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47008e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47003e+06, ..): not converged in 1000000 iter.## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.
## Warning in pchisq(X2, df = df, ncp = lambda): pnchisq(x=2.48574e+06, f=25,
## theta=2.47e+06, ..): not converged in 1000000 iter.## lavaan 0.6.15 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 10
##
## Number of observations 9688
## Number of missing patterns 20
##
## Model Test User Model:
##
## Test statistic 2485737.406
## Degrees of freedom 25
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 3662.727
## Degrees of freedom 21
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.000
## Tucker-Lewis Index (TLI) -572.354
##
## Robust Comparative Fit Index (CFI) 0.000
## Robust Tucker-Lewis Index (TLI) -417.002
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1345942.048
## Loglikelihood unrestricted model (H1) -103073.345
##
## Akaike (AIC) 2691904.096
## Bayesian (BIC) 2691975.883
## Sample-size adjusted Bayesian (SABIC) 2691944.104
##
## Root Mean Square Error of Approximation:
##
## RMSEA 3.204
## 90 Percent confidence interval - lower 3.193
## 90 Percent confidence interval - upper 3.193
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Robust RMSEA 3.445
## 90 Percent confidence interval - lower 3.441
## 90 Percent confidence interval - upper 3.450
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.751
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## sexrsp 1.521 0.010 149.745 0.000
## byear 38.845 0.010 3823.408 0.000
## educ92 13.610 0.011 1251.202 0.000
## Simil92 48.534 0.011 4441.620 0.000
## HUI3_MemPrb_03 1.540 0.012 133.310 0.000
## TotRecall_2003 10.103 0.050 201.526 0.000
## TotRecall_2011 8.901 0.043 205.164 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## s 1.000
## b 1.000
## e 1.000
## S 1.000
## H (VAR_H) 0.964 0.016 60.112 0.000
## T (VAR_TR_200) 13.485 0.260 51.794 0.000
## T (VAR_TR_201) 8.579 0.180 47.738 0.000#not accounting for covar...
modelNoCov<-"
! regressions
TotRecall_2011 ~ TotRecall_2003__TotRecall_2011*TotRecall_2003
TotRecall_2011 ~ 1.0*HUI3_MemProb_03
HUI3_MemProb_03 ~ 1.0*TotRecall_2003
! residuals, variances and covariances
HUI3_MemProb_03 ~~ VAR_HUI3_MemProb_03*HUI3_MemProb_03
TotRecall_2003 ~~ VAR_TotRecall_2003*TotRecall_2003
TotRecall_2011 ~~ VAR_TotRecall_2011*TotRecall_2011
! observed means
HUI3_MemProb_03~1;
TotRecall_2003~1;
TotRecall_2011~1;
";
result4<-lavaan(modelNoCov, data=modelData, fixed.x=FALSE, missing="FIML");## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
## 1 8 16 17 19 20 22 23 24 26 28 31 32 35 40 43 45 51 55 63 64 65 66 67 72 76 94 96 101 112 114 117 120 124 126 128 130 131 132 133 147 157 164 165 173 174 178 180 191 196 199 202 203 205 206 207 210 216 225 226 228 230 231 243 245 252 253 254 256 259 264 267 269 277 280 284 288 292 293 294 295 300 308 319 320 323 330 331 339 345 352 354 357 358 361 363 372 373 379 386 392 398 407 412 413 422 425 428 431 437 440 462 466 473 475 476 484 491 493 495 503 506 507 514 515 516 527 528 530 539 541 546 547 548 554 562 568 574 588 601 603 604 608 611 612 617 619 620 625 626 628 632 634 635 639 641 648 652 655 658 660 670 679 682 689 692 698 700 701 703 707 712 714 717 720 728 732 738 742 746 752 756 760 767 770 772 775 776 778 782 784 786 802 803 804 823 825 837 838 839 842 848 853 854 858 860 869 871 872 875 878 886 887 888 889 891 895 897 899 905 906 918 946 949 958 964 968 969 970 972 976 984 986 988 989 999 1004 1011 1012 1015 1020 1025 1027 1030 1038 1039 1043 1053 1063 1065 1071 1074 1083 1089 1096 1099 1101 1106 1108 1109 1112 1113 1118 1121 1123 1137 1141 1142 1153 1157 1167 1176 1178 1180 1183 1190 1212 1217 1224 1225 1229 1231 1232 1240 1241 1243 1244 1246 1250 1254 1258 1264 1270 1272 1273 1277 1279 1285 1287 1296 1298 1300 1301 1303 1304 1312 1313 1315 1330 1348 1349 1353 1356 1364 1371 1379 1394 1404 1410 1416 1420 1427 1431 1432 1434 1437 1440 1443 1444 1447 1450 1452 1455 1459 1464 1465 1477 1479 1481 1489 1494 1495 1496 1497 1502 1506 1509 1510 1515 1521 1524 1527 1530 1532 1537 1546 1549 1556 1562 1573 1574 1575 1578 1579 1588 1589 1590 1592 1598 1601 1604 1609 1616 1618 1619 1620 1624 1629 1631 1632 1633 1634 1638 1641 1643 1646 1651 1655 1662 1664 1665 1667 1675 1679 1682 1684 1689 1694 1699 1700 1708 1710 1711 1715 1716 1720 1722 1723 1727 1730 1732 1733 1735 1737 1741 1742 1743 1745 1753 1763 1769 1776 1780 1782 1783 1805 1808 1813 1816 1820 1821 1824 1825 1827 1831 1840 1841 1842 1865 1866 1874 1881 1885 1887 1889 1891 1892 1893 1899 1903 1904 1911 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## Estimator ML
## Optimization method NLMINB
## Number of model parameters 7
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## Used Total
## Number of observations 7486 9688
## Number of missing patterns 7
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## Model Test User Model:
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## Test statistic 20131.279
## Degrees of freedom 2
## P-value (Chi-square) 0.000
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## Model Test Baseline Model:
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## Test statistic 679.748
## Degrees of freedom 3
## P-value 0.000
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## User Model versus Baseline Model:
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## Comparative Fit Index (CFI) 0.000
## Tucker-Lewis Index (TLI) -43.616
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## Robust Comparative Fit Index (CFI) 0.000
## Robust Tucker-Lewis Index (TLI) -25.869
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## Loglikelihood and Information Criteria:
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## Loglikelihood user model (H0) -45804.240
## Loglikelihood unrestricted model (H1) -35738.600
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## Akaike (AIC) 91622.479
## Bayesian (BIC) 91670.925
## Sample-size adjusted Bayesian (SABIC) 91648.680
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## Root Mean Square Error of Approximation:
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## RMSEA 1.160
## 90 Percent confidence interval - lower 1.146
## 90 Percent confidence interval - upper 1.173
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
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## Robust RMSEA 1.220
## 90 Percent confidence interval - lower 1.203
## 90 Percent confidence interval - upper 1.237
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
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## Standardized Root Mean Square Residual:
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## SRMR 8.880
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## Parameter Estimates:
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## Standard errors Standard
## Information Observed
## Observed information based on Hessian
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## Regressions:
## Estimate Std.Err z-value P(>|z|)
## TotRecall_2011 ~
## TR_2003 (TR_2) 0.308 0.013 24.556 0.000
## HUI3_MP 1.000
## HUI3_MemProb_03 ~
## TR_2003 1.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .HUI3_MemPrb_03 -8.568 0.045 -189.150 0.000
## TotRecall_2003 10.081 0.043 234.367 0.000
## .TotRecall_2011 4.270 0.135 31.678 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .H (VAR_H) 12.857 0.220 58.520 0.000
## T (VAR_TR_200) 11.482 0.197 58.295 0.000
## .T (VAR_TR_201) 8.559 0.184 46.629 0.000