library(lavaan)

popModel <- "

A=~0.641*x1+0.653*x2+0.760*x3+0.760*x4

B=~0.727*x5+0.756*x6+0.899*x7

C=~0.574*x8+0.689*x9+0.697*x10

D=~0.685*x11+0.575*x12+0.549*x13

E=~0.750*x14+0.743*x15+0.648*x16+0.634*x17

F=~0.796*x18+0.741*x19+0.691*x20

G=~0.769*x21+0.739*x22+0.747*x23

H=~0.803*x24+0.636*x25+0.779*x26

A~~ 1*A

B~~ 1*B

C~~ 1*C

D~~ 1*D

E~~ 1*E

F~~ 1*F

G~~ 1*G

H~~ 1*H

A~~ 0.459*B

A~~ 0.282*C

A~~ 0.256*D

A~~ 0.260*E

A~~ 0.288*F

A~~ 0.336*G

A~~ 0.526*H

B~~ 0.204*C

B~~ 0.238*D

B~~ 0.254*E

B~~ 0.243*F

B~~ 0.280*G

B~~ 0.434*H

C~~ 0.293*D

C~~ 0.279*E

C~~ 0.293*F

C~~ 0.326*G

C~~ 0.265*H

D~~ 0.255*E

D~~ 0.370*F

D~~ 0.269*G

D~~ 0.254*H

E~~ 0.299*F

E~~ 0.320*G

E~~ 0.302*H

F~~ 0.364*G

F~~ 0.224*H

G~~ 0.553*H

x1 ~~ 0.589*x1

x2 ~~ 0.574*x2

x3 ~~ 0.422*x3

x4 ~~ 0.422*x4

x5 ~~ 0.471*x5

x6 ~~ 0.428*x6

x7 ~~ 0.192*x7

x8 ~~ 0.671*x8

x9 ~~ 0.525*x9

x10 ~~ 0.514*x10

x11 ~~ 0.531*x11

x12 ~~ 0.699*x12

x13 ~~ 0.699*x13

x14 ~~ 0.438*x14

x15 ~~ 0.448*x15

x16 ~~ 0.580*x16

x17 ~~ 0.598*x17

x18 ~~ 0.366*x18

x19 ~~ 0.451*x19

x20 ~~ 0.523*x20

x21 ~~ 0.409*x21

x22 ~~ 0.454*x22

x23 ~~ 0.442*x23

x24 ~~ 0.355*x24

x25 ~~ 0.596*x25

x26 ~~ 0.393*x26

"

data <- simulateData(popModel, sample.nobs = 200)

analyzeModel <- "

A=~x1+x2+x3+x4

B=~x5+x6+x7

C=~x8+x9+x10

D=~x11+x12+x13

E=~x14+x15+x16+x17

F=~x18+x19+x20

G=~x21+x22+x23

H=~x24+x25+x26

"

data <- cfa(analyzeModel, data = data, std.lv = TRUE)

# Use simsem to simulate and analyze multiple data sets

library(simsem)

Output1 <- sim(1000, analyzeModel, n=376, generate=popModel,

               lavaanfun = "cfa", std.lv=TRUE)

summary(Output1)

Output2 <- sim(NULL, analyzeModel, n=100:1000, generate=popModel,

               lavaanfun = "cfa", std.lv=TRUE)

summary(Output2) 

powTable2 <- getPower(Output2)

findPower(powTable2, "N", 0.89)