semnova() functionThis is a simple example how to use semnova() function.
library(semnova)
set.seed(323412431)
data("semnova_test_data", package = "semnova")
idata <- expand.grid(A = c("A1", "A2", "A3"), B = c("B1", "B2"))
mmodel <- create_mmodel(
A1B1 = "var1",
A2B1 = "var2",
A3B1 = "var3",
A1B2 = "var4",
A2B2 = "var5",
A3B2 = "var6",
lv_scaling = "referent"
)
fit_semnova <-
semnova(
formula = cbind(A1B1, A2B1, A3B1, A1B2, A2B2, A3B2) ~ 1,
data = semnova_test_data,
idata = idata,
idesign = ~ A * B,
mmodel = mmodel
)
summary(fit_semnova)
#> ------------------------------------------
#>
#> term: (Intercept)
#>
#> Response transformation matrix:
#> (Intercept)
#> A1.B1 0.40825
#> A2.B1 0.40825
#> A3.B1 0.40825
#> A1.B2 0.40825
#> A2.B2 0.40825
#> A3.B2 0.40825
#>
#> multiv. tests:
#> Df test stat approx F num Df den Df Pr(>F)
#> Wald 1 0.09447 0.094467 1 99 0.7592
#> Wilks 1 0.99906 0.093522 1 99 0.7604
#>
#> univ. test:
#> Sum Sq num Df Error SS den Df F value Pr(>F)
#> F-test 0.30923 1 327.35 99 0.0935 0.7604
#>
#> ------------------------------------------
#>
#> term: A
#>
#> Response transformation matrix:
#> A1 A2
#> A1.B1 -0.5 0.28868
#> A2.B1 0.0 -0.57735
#> A3.B1 0.5 0.28868
#> A1.B2 -0.5 0.28868
#> A2.B2 0.0 -0.57735
#> A3.B2 0.5 0.28868
#>
#> multiv. tests:
#> Df test stat approx F num Df den Df Pr(>F)
#> Wald 2 3.4527 1.7264 2 198 0.1806
#> Wilks 1 0.9666 1.6919 2 98 0.1895
#>
#> univ. test:
#> Sum Sq num Df Error SS den Df F value Pr(>F)
#> F-test 1.628 2 94.479 198 1.7059 0.1843
#>
#> ------------------------------------------
#>
#> term: B
#>
#> Response transformation matrix:
#> B1
#> A1.B1 -0.40825
#> A2.B1 -0.40825
#> A3.B1 -0.40825
#> A1.B2 0.40825
#> A2.B2 0.40825
#> A3.B2 0.40825
#>
#> multiv. tests:
#> Df test stat approx F num Df den Df Pr(>F)
#> Wald 1 0.26776 0.26776 1 99 0.6060
#> Wilks 1 0.99733 0.26509 1 99 0.6078
#>
#> univ. test:
#> Sum Sq num Df Error SS den Df F value Pr(>F)
#> F-test 0.15035 1 56.151 99 0.2651 0.6078
#>
#> ------------------------------------------
#>
#> term: A:B
#>
#> Response transformation matrix:
#> A1:B1 A2:B1
#> A1.B1 0.5 -0.28868
#> A2.B1 0.0 0.57735
#> A3.B1 -0.5 -0.28868
#> A1.B2 -0.5 0.28868
#> A2.B2 0.0 -0.57735
#> A3.B2 0.5 0.28868
#>
#> multiv. tests:
#> Df test stat approx F num Df den Df Pr(>F)
#> Wald 2 1.05674 0.52837 2 198 0.5904
#> Wilks 1 0.98954 0.51780 2 98 0.5974
#>
#> univ. test:
#> Sum Sq num Df Error SS den Df F value Pr(>F)
#> F-test 0.5627 2 104.34 198 0.5339 0.5872
#>
#> ------------------------------------------lgc() functionThis is a simple example how to use lgc() function.
library(semnova)
set.seed(323412431)
data("semnova_test_data", package = "semnova")
mmodel <- create_mmodel(
A1B1 = "var1",
A2B1 = "var2",
A3B1 = "var3",
A1B2 = "var4",
A2B2 = "var5",
A3B2 = "var6",
lv_scaling = "referent"
)
hypotheses <- list(
Intercept = c(1),
A = c(2, 3),
B = c(4),
AB = c(5, 6)
)
C_matrix <- matrix(
c(
1, 1, 0, 1, 1, 0,
1, 0, 1, 1, 0, 1,
1,-1,-1, 1,-1,-1,
1, 1, 0,-1,-1, 0,
1, 0, 1,-1, 0,-1,
1,-1,-1,-1, 1, 1
),
nrow = 6
)
fit_lgc <-
lgc(
data = semnova_test_data,
mmodel = mmodel,
C_matrix = C_matrix,
hypotheses = hypotheses
)
summary(fit_lgc)
#> ------------------------------------------
#>
#> term: Intercept
#>
#> Response transformation matrix:
#> V1
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1
#> 6 1
#>
#> multiv. tests:
#> Df test stat approx F num Df den Df Pr(>F)
#> Wald 1 0.09447 0.094467 1 99 0.7592
#> Wilks 1 0.99906 0.093522 1 99 0.7604
#>
#> ------------------------------------------
#>
#> term: A
#>
#> Response transformation matrix:
#> V1 V2
#> 1 1 0
#> 2 0 1
#> 3 -1 -1
#> 4 1 0
#> 5 0 1
#> 6 -1 -1
#>
#> multiv. tests:
#> Df test stat approx F num Df den Df Pr(>F)
#> Wald 2 3.4527 1.7264 2 198 0.1806
#> Wilks 1 0.9666 1.6919 2 98 0.1895
#>
#> ------------------------------------------
#>
#> term: B
#>
#> Response transformation matrix:
#> V1
#> 1 1
#> 2 1
#> 3 1
#> 4 -1
#> 5 -1
#> 6 -1
#>
#> multiv. tests:
#> Df test stat approx F num Df den Df Pr(>F)
#> Wald 1 0.26776 0.26776 1 99 0.6060
#> Wilks 1 0.99733 0.26509 1 99 0.6078
#>
#> ------------------------------------------
#>
#> term: AB
#>
#> Response transformation matrix:
#> V1 V2
#> 1 1 0
#> 2 0 1
#> 3 -1 -1
#> 4 -1 0
#> 5 0 -1
#> 6 1 1
#>
#> multiv. tests:
#> Df test stat approx F num Df den Df Pr(>F)
#> Wald 2 1.05674 0.52837 2 198 0.5904
#> Wilks 1 0.98954 0.51780 2 98 0.5975
#>
#> ------------------------------------------