||
编码如下:
library(devtools)
library(lavaan)
library(snow)
library(simsem)
library(matrixpls)
model<-"! regressions
A=~0.753*x1
A=~0.757*x2
A=~0.812*x3
A=~0.830*x4
B=~0.842*x5
B=~0.812*x6
B=~0.905*x7
C=~0.743*x8
C=~0.789*x9
C=~0.822*x10
D=~0.807*x11
D=~0.747*x12
D=~0.722*X13
E=~0.817*X14
E=~0.821*X15
E=~0.764*X16
E=~0.706*X17
F=~0.855*X18
F=~0.848*X19
F=~0.802*X20
G=~0.854*X21
G=~0.841*X22
G=~0.832*X23
H=~0.877*X24
H=~0.759*X25
H=~0.861*X26
G ~ 0.142*A
G ~ 0.059*B
G ~ 0.179*C
G ~ 0.264*D
G ~ 0.071*E
G ~ 0.193*F
H ~ 0.639*G
! residuals, variances and covariances
A ~~ 1.0*A
B ~~ 1.0*B
C ~~ 1.0*C
D ~~ 1.0*D
E ~~ 1.0*E
F ~~ 1.0*F
G ~~ 0.556*G
H ~~ 0.591*H
B ~~ 0.336*A
C ~~ 0.354*A
D ~~ 0.361*A
E ~~ 0.386*A
F ~~ 0.309*A
C ~~ 0.228*B
D ~~ 0.282*B
E ~~ 0.355*B
F ~~ 0.266*B
D ~~ 0.523*C
E ~~ 0.531*C
F ~~ 0.393*C
E ~~ 0.567*D
F ~~ 0.482*D
F ~~ 0.630*E
x1 ~~ 0.433*x1
x2 ~~ 0.427*x2
x3 ~~ 0.341*x3
x4 ~~ 0.311*x4
x5 ~~ 0.291*x5
x6 ~~ 0.341*x6
x7 ~~ 0.181*x7
x8 ~~ 0.448*x8
x9 ~~ 0.377*x9
x10 ~~ 0.324*x10
x11 ~~ 0.349*x11
x12 ~~ 0.442*x12
x13 ~~ 0.479*x13
x14 ~~ 0.333*x14
x15 ~~ 0.326*x15
x16 ~~ 0.416*x16
x17 ~~ 0.502*x17
x18 ~~ 0.269*x18
x19 ~~ 0.281*x19
x20 ~~ 0.357*x20
x21 ~~ 0.271*x21
x22 ~~ 0.293*x22
x23 ~~ 0.308*x23
x24 ~~ 0.231*x24
x25 ~~ 0.424*x25
x26 ~~ 0.259*x26"
# Normal data
output <- matrixpls.sim(1000, model, n=376, multicore=FALSE, completeRep = TRUE)
summary(output)
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