rm(list = ls())
library(knitr)
library(foreign)
library(lavaan)
## This is lavaan 0.5-22
## lavaan is BETA software! Please report any bugs.
setwd(dir = "/Users/ivanropovik/OneDrive/Projects/APVV IK/DATA")
data <- read.spss("APVV_ANALOGIES_ARTICLE_imputed.sav", to.data.frame=TRUE)
## Warning in read.spss("APVV_ANALOGIES_ARTICLE_imputed.sav", to.data.frame
## = TRUE): APVV_ANALOGIES_ARTICLE_imputed.sav: Unrecognized record type 7,
## subtype 18 encountered in system file
## Warning in read.spss("APVV_ANALOGIES_ARTICLE_imputed.sav", to.data.frame
## = TRUE): APVV_ANALOGIES_ARTICLE_imputed.sav: Unrecognized record type 7,
## subtype 24 encountered in system file
#attach(data)
data$P_STROOP_INH_CAS <- with(data, ((max(P_STROOP_INH_CAS) + 1) - P_STROOP_INH_CAS))
data$P_TMT_stried <- with(data, ((max(P_TMT_stried) + 1) - P_TMT_stried))
data$AL_HINT_SUM <- with(data, ((max(AL_HINT_SUM) + 1) - AL_HINT_SUM))
model.ini <- '
Att =~ a*P_STROOP_INH_CAS + b*WJ_VIZ_POROV
Fluency =~ c*P_FLUE_PISM_SUM + d*P_DF_spravne_spolu
Shifting =~ e*P_TMT_stried + f*P_FLUE_STR_POC
WM =~ g*WJ_CIS_RADY + h*TOH_SCORE
gF =~ i*WJ_PRIEST_VZTAH + j*WJ_KV_VYVODZ
AtL =~ k*Learn + l*AL_HINT_SUM
Fluency ~ r*Att
Shifting ~ s*Att
WM ~ t*Att
gF ~ u*WM
AtL ~ v*WM
'
fitted.model <- sem(model = model.ini, data = data, meanstructure = FALSE,
std.lv = FALSE, estimator = "ML", test = "standard",
orthogonal = TRUE, std.ov = TRUE, likelihood = "wishart", bootstrap = 2000)
summary(fitted.model, standardized = TRUE, rsquare = TRUE)
## lavaan (0.5-22) converged normally after 39 iterations
##
## Number of observations 210
##
## Estimator ML
## Minimum Function Test Statistic 76.130
## Degrees of freedom 49
## P-value (Chi-square) 0.008
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Att =~
## P_STROOP_I (a) 1.000 0.648 0.648
## WJ_VIZ_POR (b) 0.979 0.145 6.769 0.000 0.634 0.634
## Fluency =~
## P_FLUE_PIS (c) 1.000 0.476 0.476
## P_DF_sprv_ (d) 1.057 0.245 4.320 0.000 0.503 0.503
## Shifting =~
## P_TMT_strd (e) 1.000 0.750 0.750
## P_FLUE_STR (f) 0.624 0.122 5.124 0.000 0.468 0.468
## WM =~
## WJ_CIS_RAD (g) 1.000 0.622 0.622
## TOH_SCORE (h) 0.411 0.134 3.062 0.002 0.256 0.256
## gF =~
## WJ_PRIEST_ (i) 1.000 0.576 0.576
## WJ_KV_VYVO (j) 1.360 0.216 6.307 0.000 0.784 0.784
## AtL =~
## Learn (k) 1.000 0.762 0.762
## AL_HINT_SU (l) 0.957 0.162 5.897 0.000 0.729 0.729
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Fluency ~
## Att (r) 0.650 0.133 4.881 0.000 0.885 0.885
## Shifting ~
## Att (s) 1.036 0.150 6.896 0.000 0.895 0.895
## WM ~
## Att (t) 0.545 0.115 4.735 0.000 0.568 0.568
## gF ~
## WM (u) 0.862 0.171 5.029 0.000 0.930 0.930
## AtL ~
## WM (v) 0.752 0.146 5.158 0.000 0.614 0.614
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Fluency ~~
## .Shifting 0.000 0.000 0.000
## .gF 0.000 0.000 0.000
## .AtL 0.000 0.000 0.000
## .Shifting ~~
## .gF 0.000 0.000 0.000
## .AtL 0.000 0.000 0.000
## .gF ~~
## .AtL 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .P_STROOP_INH_C 0.580 0.075 7.723 0.000 0.580 0.580
## .WJ_VIZ_POROV 0.598 0.076 7.905 0.000 0.598 0.598
## .P_FLUE_PISM_SU 0.774 0.097 7.987 0.000 0.774 0.774
## .P_DF_sprvn_spl 0.747 0.100 7.506 0.000 0.747 0.747
## .P_TMT_stried 0.437 0.107 4.076 0.000 0.437 0.437
## .P_FLUE_STR_POC 0.781 0.085 9.136 0.000 0.781 0.781
## .WJ_CIS_RADY 0.613 0.081 7.554 0.000 0.613 0.613
## .TOH_SCORE 0.935 0.094 9.963 0.000 0.935 0.935
## .WJ_PRIEST_VZTA 0.668 0.079 8.404 0.000 0.668 0.668
## .WJ_KV_VYVODZ 0.386 0.092 4.203 0.000 0.386 0.386
## .Learn 0.419 0.099 4.233 0.000 0.419 0.419
## .AL_HINT_SUM 0.469 0.094 4.966 0.000 0.469 0.469
## Att 0.420 0.094 4.485 0.000 1.000 1.000
## .Fluency 0.049 0.059 0.823 0.410 0.216 0.216
## .Shifting 0.112 0.102 1.100 0.271 0.199 0.199
## .WM 0.262 0.069 3.784 0.000 0.677 0.677
## .gF 0.045 0.050 0.899 0.368 0.135 0.135
## .AtL 0.362 0.095 3.812 0.000 0.623 0.623
##
## R-Square:
## Estimate
## P_STROOP_INH_C 0.420
## WJ_VIZ_POROV 0.402
## P_FLUE_PISM_SU 0.226
## P_DF_sprvn_spl 0.253
## P_TMT_stried 0.563
## P_FLUE_STR_POC 0.219
## WJ_CIS_RADY 0.387
## TOH_SCORE 0.065
## WJ_PRIEST_VZTA 0.332
## WJ_KV_VYVODZ 0.614
## Learn 0.581
## AL_HINT_SUM 0.531
## Fluency 0.784
## Shifting 0.801
## WM 0.323
## gF 0.865
## AtL 0.377
fitMeasures(fitted.model)
## npar fmin chisq
## 29.000 0.182 76.130
## df pvalue baseline.chisq
## 49.000 0.008 543.661
## baseline.df baseline.pvalue cfi
## 66.000 0.000 0.943
## tli nnfi rfi
## 0.923 0.923 0.811
## nfi pnfi ifi
## 0.860 0.638 0.945
## rni logl unrestricted.logl
## 0.943 -3340.841 -3302.594
## aic bic ntotal
## 6739.681 6836.609 210.000
## bic2 rmsea rmsea.ci.lower
## 6744.722 0.051 0.027
## rmsea.ci.upper rmsea.pvalue rmr
## 0.073 0.434 0.055
## rmr_nomean srmr srmr_bentler
## 0.055 0.055 0.055
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.055 0.055 0.055
## srmr_mplus srmr_mplus_nomean cn_05
## 0.055 0.055 183.121
## cn_01 gfi agfi
## 206.678 0.943 0.909
## pgfi mfi ecvi
## 0.592 0.937 0.642
residuals(fitted.model, type = "cor")$cor
## P_STRO WJ_VIZ P_FLUE_P P_DF__ P_TMT_ P_FLUE_S WJ_CIS
## P_STROOP_INH_CAS 0.000
## WJ_VIZ_POROV 0.089 0.000
## P_FLUE_PISM_SUM -0.052 -0.026 0.000
## P_DF_spravne_spolu 0.000 -0.053 0.000 0.000
## P_TMT_stried -0.020 -0.028 -0.002 0.036 0.000
## P_FLUE_STR_POC 0.044 -0.063 0.053 0.003 0.000 0.000
## WJ_CIS_RADY 0.013 0.076 0.114 -0.034 0.031 0.016 0.000
## TOH_SCORE 0.100 0.046 -0.092 0.002 0.184 0.116 -0.013
## WJ_PRIEST_VZTAH -0.088 -0.019 0.043 0.047 -0.024 -0.064 0.025
## WJ_KV_VYVODZ -0.180 -0.035 0.100 0.031 0.047 -0.035 -0.005
## Learn -0.029 0.017 0.076 0.035 0.042 -0.031 -0.019
## AL_HINT_SUM -0.035 -0.108 0.040 0.007 0.010 0.040 -0.084
## TOH_SC WJ_PRI WJ_KV_ Learn AL_HIN
## P_STROOP_INH_CAS
## WJ_VIZ_POROV
## P_FLUE_PISM_SUM
## P_DF_spravne_spolu
## P_TMT_stried
## P_FLUE_STR_POC
## WJ_CIS_RADY
## TOH_SCORE 0.000
## WJ_PRIEST_VZTAH -0.087 0.000
## WJ_KV_VYVODZ 0.022 0.000 0.000
## Learn -0.049 -0.002 0.012 0.000
## AL_HINT_SUM -0.050 0.065 0.053 0.000 0.000