library(metaRmat)
Br <- matrix(c(1.00000000, -0.09773331, -0.1755029, 0.3186775,
-0.09773331, 1.00000000, 0.5271873, -0.4175596,
-0.17550292, 0.5271872, 1.0000000, -0.4006848,
0.31867753, -0.41755963, -0.4006848, 1.0000000),
nrow = 4, byrow = TRUE)
colnames(Br) <- c("Performance", "Self_confidence", "Cognitive", "Somatic" )
rownames(Br) <- colnames(Br)
## Proposed path model
model <- "## Regression paths
Performance ~ Self_confidence + Cognitive + Somatic
Self_confidence ~ Cognitive + Somatic "
find_B(model, Br)
## [[1]]
## Self_confidence -> Performance Cognitive -> Performance
## 0.08316153 -0.09261001
## Somatic -> Performance
## 0.31629500
##
## [[2]]
## Cognitive -> Self_confidence Somatic -> Self_confidence
## 0.4287053 -0.2457839
c_mat_ft(model, Br) # the same results with lavaan ML
## $model_input
## [1] "## Regression paths\n Performance ~ Self_confidence + Cognitive + Somatic\n Self_confidence ~ Cognitive + Somatic "
##
## $A_mat
## Performance Self_confidence Cognitive Somatic
## Performance 0 0.08316153 -0.09261001 0.3162950
## Self_confidence 0 0.00000000 0.42870532 -0.2457839
## Cognitive 0 0.00000000 0.00000000 0.0000000
## Somatic 0 0.00000000 0.00000000 0.0000000
##
## $S_mat
## Performance Self_confidence Cognitive Somatic
## Performance 0.8910782 0.0000000 0.0000000 0.0000000
## Self_confidence 0.0000000 0.6713626 0.0000000 0.0000000
## Cognitive 0.0000000 0.0000000 1.0000000 -0.4006848
## Somatic 0.0000000 0.0000000 -0.4006848 1.0000000
##
## $F_mat
## Performance Self_confidence Cognitive Somatic
## Performance 1 0 0 0
## Self_confidence 0 1 0 0
## Cognitive 0 0 1 0
## Somatic 0 0 0 1
##
## $C_mat
## Performance Self_confidence Cognitive Somatic
## Performance 1.00000000 -0.09773331 -0.1755029 0.3186775
## Self_confidence -0.09773331 1.00000000 0.5271872 -0.4175596
## Cognitive -0.17550292 0.52718720 1.0000000 -0.4006848
## Somatic 0.31867753 -0.41755963 -0.4006848 1.0000000
##
## $Mul_R2
## Performance Self_confidence
## 0.1089218 0.3286374
##
## $num_freePar
## [1] 8
##
## $df
## [1] 2
N <- 573
model_fit(model, R = Br, method_mat = "lavaan",
method_null = "sem", N)
## $path_coefficients
## $path_coefficients[[1]]
## Self_confidence -> Performance Cognitive -> Performance
## 0.08316153 -0.09261001
## Somatic -> Performance
## 0.31629500
##
## $path_coefficients[[2]]
## Cognitive -> Self_confidence Somatic -> Self_confidence
## 0.4287053 -0.2457839
##
##
## $Model
## Chi2 df pvalue
## 59.1102 2.0000 0.0000
##
## $NullModel
## Chi2Null dfNull
## 643.3372 6.0000
##
## $CFI
## [1] 0.9103925
##
## $TLI
## [1] 0.7311776
##
## $RMSEA
## [1] 0.2234311
##
## $RMSEA_CI
## [1] 0.1671203 0.2835352
##
## $SRMR
## [1] 0.2672185
model_fit(model, R = Br, method_mat = "loehlin",
method_null = "sem", N )
## $path_coefficients
## $path_coefficients[[1]]
## Self_confidence -> Performance Cognitive -> Performance
## 0.08316153 -0.09261001
## Somatic -> Performance
## 0.31629500
##
## $path_coefficients[[2]]
## Cognitive -> Self_confidence Somatic -> Self_confidence
## 0.4287053 -0.2457839
##
##
## $Model
## Chi2 df pvalue
## 0 2 1
##
## $NullModel
## Chi2Null dfNull
## 643.3372 6.0000
##
## $CFI
## [1] 1.003138
##
## $TLI
## [1] 1.009414
##
## $RMSEA
## [1] "RMSEA is negative"
##
## $RMSEA_CI
## NULL
##
## $SRMR
## [1] 3.492857e-08