B Filled Power Script Example

data_syntax <- "
.eta1 =~
    c(1, 1, 1, 1)*.Y1_W1_1_W2_1 +
    c(1, 1, 1, 1)*.Y2_W1_1_W2_1 +
    c(1, 1, 1, 1)*.Y3_W1_1_W2_1
.eta2 =~
    c(1, 1, 1, 1)*.Y1_W1_2_W2_1 +
    c(1, 1, 1, 1)*.Y2_W1_2_W2_1 +
    c(1, 1, 1, 1)*.Y3_W1_2_W2_1
.eta3 =~
    c(1, 1, 1, 1)*.Y1_W1_1_W2_2 +
    c(1, 1, 1, 1)*.Y2_W1_1_W2_2 +
    c(1, 1, 1, 1)*.Y3_W1_1_W2_2
.eta4 =~
    c(1, 1, 1, 1)*.Y1_W1_2_W2_2 +
    c(1, 1, 1, 1)*.Y2_W1_2_W2_2 +
    c(1, 1, 1, 1)*.Y3_W1_2_W2_2
.eta5 =~
    c(1, 1, 1, 1)*.Y1_W1_1_W2_3 +
    c(1, 1, 1, 1)*.Y2_W1_1_W2_3 +
    c(1, 1, 1, 1)*.Y3_W1_1_W2_3
.eta6 =~
    c(1, 1, 1, 1)*.Y1_W1_2_W2_3 +
    c(1, 1, 1, 1)*.Y2_W1_2_W2_3 +
    c(1, 1, 1, 1)*.Y3_W1_2_W2_3
.pi1 =~
    c(0.408248290463863, 0.408248290463863, 0.408248290463863, 0.408248290463863)*.eta1 +
    c(0.408248290463863, 0.408248290463863, 0.408248290463863, 0.408248290463863)*.eta2 +
    c(0.408248290463863, 0.408248290463863, 0.408248290463863, 0.408248290463863)*.eta3 +
    c(0.408248290463863, 0.408248290463863, 0.408248290463863, 0.408248290463863)*.eta4 +
    c(0.408248290463863, 0.408248290463863, 0.408248290463863, 0.408248290463863)*.eta5 +
    c(0.408248290463863, 0.408248290463863, 0.408248290463863, 0.408248290463863)*.eta6
.pi2 =~
    c(-0.408248290463863, -0.408248290463863, -0.408248290463863, -0.408248290463863)*.eta1 +
    c(0.408248290463863, 0.408248290463863, 0.408248290463863, 0.408248290463863)*.eta2 +
    c(-0.408248290463863, -0.408248290463863, -0.408248290463863, -0.408248290463863)*.eta3 +
    c(0.408248290463863, 0.408248290463863, 0.408248290463863, 0.408248290463863)*.eta4 +
    c(-0.408248290463863, -0.408248290463863, -0.408248290463863, -0.408248290463863)*.eta5 +
    c(0.408248290463863, 0.408248290463863, 0.408248290463863, 0.408248290463863)*.eta6
.pi3 =~
    c(-0.5, -0.5, -0.5, -0.5)*.eta1 +
    c(-0.5, -0.5, -0.5, -0.5)*.eta2 +
    c(0, 0, 0, 0)*.eta3 +
    c(0, 0, 0, 0)*.eta4 +
    c(0.5, 0.5, 0.5, 0.5)*.eta5 +
    c(0.5, 0.5, 0.5, 0.5)*.eta6
.pi4 =~
    c(0.288675134594813, 0.288675134594813, 0.288675134594813, 0.288675134594813)*.eta1 +
    c(0.288675134594813, 0.288675134594813, 0.288675134594813, 0.288675134594813)*.eta2 +
    c(-0.577350269189626, -0.577350269189626, -0.577350269189626, -0.577350269189626)*.eta3 +
    c(-0.577350269189626, -0.577350269189626, -0.577350269189626, -0.577350269189626)*.eta4 +
    c(0.288675134594813, 0.288675134594813, 0.288675134594813, 0.288675134594813)*.eta5 +
    c(0.288675134594813, 0.288675134594813, 0.288675134594813, 0.288675134594813)*.eta6
.pi5 =~
    c(0.5, 0.5, 0.5, 0.5)*.eta1 +
    c(-0.5, -0.5, -0.5, -0.5)*.eta2 +
    c(0, 0, 0, 0)*.eta3 +
    c(0, 0, 0, 0)*.eta4 +
    c(-0.5, -0.5, -0.5, -0.5)*.eta5 +
    c(0.5, 0.5, 0.5, 0.5)*.eta6
.pi6 =~
    c(-0.288675134594813, -0.288675134594813, -0.288675134594813, -0.288675134594813)*.eta1 +
    c(0.288675134594813, 0.288675134594813, 0.288675134594813, 0.288675134594813)*.eta2 +
    c(0.577350269189626, 0.577350269189626, 0.577350269189626, 0.577350269189626)*.eta3 +
    c(-0.577350269189626, -0.577350269189626, -0.577350269189626, -0.577350269189626)*.eta4 +
    c(-0.288675134594813, -0.288675134594813, -0.288675134594813, -0.288675134594813)*.eta5 +
    c(0.288675134594813, 0.288675134594813, 0.288675134594813, 0.288675134594813)*.eta6
covariate2 =~
    c(1, 1, 1, 1)*covariate2_1 +
    c(1, 1, 1, 1)*covariate2_2
.pi1 ~
    c(0.1, 0.1, 0.1, 0.1)*covariate1 +
    c(0.1, 0.1, 0.1, 0.1)*covariate2
.pi2 ~
    c(0.1, 0.1, 0.1, 0.1)*covariate1 +
    c(0.1, 0.1, 0.1, 0.1)*covariate2
.pi3 ~
    c(0.1, 0.1, 0.1, 0.1)*covariate1 +
    c(0.1, 0.1, 0.1, 0.1)*covariate2
.pi4 ~
    c(0.1, 0.1, 0.1, 0.1)*covariate1 +
    c(0.1, 0.1, 0.1, 0.1)*covariate2
.pi5 ~
    c(0.1, 0.1, 0.1, 0.1)*covariate1 +
    c(0.1, 0.1, 0.1, 0.1)*covariate2
.pi6 ~
    c(0.1, 0.1, 0.1, 0.1)*covariate1 +
    c(0.1, 0.1, 0.1, 0.1)*covariate2
.eta1 ~~
    c(0, 0, 0, 0)*.eta1 +
    c(0, 0, 0, 0)*.eta2 +
    c(0, 0, 0, 0)*.eta3 +
    c(0, 0, 0, 0)*.eta4 +
    c(0, 0, 0, 0)*.eta5 +
    c(0, 0, 0, 0)*.eta6
.eta2 ~~
    c(0, 0, 0, 0)*.eta2 +
    c(0, 0, 0, 0)*.eta3 +
    c(0, 0, 0, 0)*.eta4 +
    c(0, 0, 0, 0)*.eta5 +
    c(0, 0, 0, 0)*.eta6
.eta3 ~~
    c(0, 0, 0, 0)*.eta3 +
    c(0, 0, 0, 0)*.eta4 +
    c(0, 0, 0, 0)*.eta5 +
    c(0, 0, 0, 0)*.eta6
.eta4 ~~
    c(0, 0, 0, 0)*.eta4 +
    c(0, 0, 0, 0)*.eta5 +
    c(0, 0, 0, 0)*.eta6
.eta5 ~~
    c(0, 0, 0, 0)*.eta5 +
    c(0, 0, 0, 0)*.eta6
.eta6 ~~
    c(0, 0, 0, 0)*.eta6
.pi1 ~~
    c(1, 1, 1, 1)*.pi1 +
    c(0.5, 0.5, 0.5, 0.5)*.pi2 +
    c(0.5, 0.5, 0.5, 0.5)*.pi3 +
    c(0.5, 0.5, 0.5, 0.5)*.pi4 +
    c(0.5, 0.5, 0.5, 0.5)*.pi5 +
    c(0.5, 0.5, 0.5, 0.5)*.pi6
.pi2 ~~
    c(1, 1, 1, 1)*.pi2 +
    c(0.5, 0.5, 0.5, 0.5)*.pi3 +
    c(0.5, 0.5, 0.5, 0.5)*.pi4 +
    c(0.5, 0.5, 0.5, 0.5)*.pi5 +
    c(0.5, 0.5, 0.5, 0.5)*.pi6
.pi3 ~~
    c(1, 1, 1, 1)*.pi3 +
    c(0, 0, 0, 0)*.pi4 +
    c(0.5, 0.5, 0.5, 0.5)*.pi5 +
    c(0.5, 0.5, 0.5, 0.5)*.pi6
.pi4 ~~
    c(1, 1, 1, 1)*.pi4 +
    c(0.5, 0.5, 0.5, 0.5)*.pi5 +
    c(0.5, 0.5, 0.5, 0.5)*.pi6
.pi5 ~~
    c(1, 1, 1, 1)*.pi5 +
    c(0.5, 0.5, 0.5, 0.5)*.pi6
.pi6 ~~
    c(1, 1, 1, 1)*.pi6
.Y1_W1_1_W2_1 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y1_W1_1_W2_1
.Y1_W1_1_W2_2 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y1_W1_1_W2_2
.Y1_W1_1_W2_3 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y1_W1_1_W2_3
.Y1_W1_2_W2_1 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y1_W1_2_W2_1
.Y1_W1_2_W2_2 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y1_W1_2_W2_2
.Y1_W1_2_W2_3 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y1_W1_2_W2_3
.Y2_W1_1_W2_1 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y2_W1_1_W2_1
.Y2_W1_1_W2_2 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y2_W1_1_W2_2
.Y2_W1_1_W2_3 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y2_W1_1_W2_3
.Y2_W1_2_W2_1 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y2_W1_2_W2_1
.Y2_W1_2_W2_2 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y2_W1_2_W2_2
.Y2_W1_2_W2_3 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y2_W1_2_W2_3
.Y3_W1_1_W2_1 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y3_W1_1_W2_1
.Y3_W1_1_W2_2 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y3_W1_1_W2_2
.Y3_W1_1_W2_3 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y3_W1_1_W2_3
.Y3_W1_2_W2_1 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y3_W1_2_W2_1
.Y3_W1_2_W2_2 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y3_W1_2_W2_2
.Y3_W1_2_W2_3 ~~
    c(0.4, 0.4, 0.4, 0.4)*.Y3_W1_2_W2_3
covariate1 ~~
    c(0.4, 0.4, 0.4, 0.4)*covariate1
covariate2 ~~
    c(1, 1, 1, 1)*covariate2
covariate2_1 ~~
    c(0.4, 0.4, 0.4, 0.4)*covariate2_1
covariate2_2 ~~
    c(0.4, 0.4, 0.4, 0.4)*covariate2_2
.eta1 ~
    c(0, 0, 0, 0)*1
.eta2 ~
    c(0, 0, 0, 0)*1
.eta3 ~
    c(0, 0, 0, 0)*1
.eta4 ~
    c(0, 0, 0, 0)*1
.eta5 ~
    c(0, 0, 0, 0)*1
.eta6 ~
    c(0, 0, 0, 0)*1
.pi1 ~
    c(0.1, 0.1, 0.1, 0.1)*1
.pi2 ~
    c(0.1, 0.1, 0.1, 0.1)*1
.pi3 ~
    c(0.1, 0.1, 0.1, 0.1)*1
.pi4 ~
    c(0.1, 0.1, 0.1, 0.1)*1
.pi5 ~
    c(0.1, 0.1, 0.1, 0.1)*1
.pi6 ~
    c(0.1, 0.1, 0.1, 0.1)*1
.Y1_W1_1_W2_1 ~
    c(0, 0, 0, 0)*1
.Y1_W1_1_W2_2 ~
    c(0, 0, 0, 0)*1
.Y1_W1_1_W2_3 ~
    c(0, 0, 0, 0)*1
.Y1_W1_2_W2_1 ~
    c(0, 0, 0, 0)*1
.Y1_W1_2_W2_2 ~
    c(0, 0, 0, 0)*1
.Y1_W1_2_W2_3 ~
    c(0, 0, 0, 0)*1
.Y2_W1_1_W2_1 ~
    c(0, 0, 0, 0)*1
.Y2_W1_1_W2_2 ~
    c(0, 0, 0, 0)*1
.Y2_W1_1_W2_3 ~
    c(0, 0, 0, 0)*1
.Y2_W1_2_W2_1 ~
    c(0, 0, 0, 0)*1
.Y2_W1_2_W2_2 ~
    c(0, 0, 0, 0)*1
.Y2_W1_2_W2_3 ~
    c(0, 0, 0, 0)*1
.Y3_W1_1_W2_1 ~
    c(0, 0, 0, 0)*1
.Y3_W1_1_W2_2 ~
    c(0, 0, 0, 0)*1
.Y3_W1_1_W2_3 ~
    c(0, 0, 0, 0)*1
.Y3_W1_2_W2_1 ~
    c(0, 0, 0, 0)*1
.Y3_W1_2_W2_2 ~
    c(0, 0, 0, 0)*1
.Y3_W1_2_W2_3 ~
    c(0, 0, 0, 0)*1
covariate1 ~
    c(0, 0, 0, 0)*1
covariate2 ~
    c(0.1, 0.1, 0.1, 0.1)*1
covariate2_1 ~
    c(0, 0, 0, 0)*1
covariate2_2 ~
    c(0, 0, 0, 0)*1
"

power <- power_analysis_semnova(
    n_within = c(2, 3),
    n_between = c(2, 2),
    n_indicator = 3,
    n_manifest_covariate = 1,
    n_latent_covariate = 2,
    sphericity = list(2),
    normalize_contrasts = TRUE,
    data_syntax = data_syntax,
    sample_size = c(100, 100, 100, 100),
    replications = 500
)

# hypothesis tests
summary(power)

# parameters
print(power)

# path diagram
plot(power)