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#
# Copyright 2007-2018 by the individuals mentioned in the source code history
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
require(OpenMx)
myDataCov<-matrix(
c(0.672, 0.315, 0.097, -0.037, 0.046,
0.315, 1.300, 0.428, 0.227, 0.146,
0.097, 0.428, 1.177, 0.568, 0.429,
-0.037, 0.227, 0.568, 1.069, 0.468,
0.046, 0.146, 0.429, 0.468, 1.031),
nrow=5,
dimnames=list(
c("x1","x2","x3","x4","x5"),
c("x1","x2","x3","x4","x5"))
)
myDataMeans <- c(3.054, 1.385, 0.680, 0.254, -0.027)
names(myDataMeans) <- c("x1","x2","x3","x4","x5")
model<-mxModel("Autoregressive Model, Matrix Specification, Covariance Data",
mxData(myDataCov,type="cov", means=myDataMeans, numObs=100),
mxMatrix("Full", nrow=5, ncol=5,
values=c(0,0,0,0,0,
1,0,0,0,0,
0,1,0,0,0,
0,0,1,0,0,
0,0,0,1,0),
free=c(F, F, F, F, F,
T, F, F, F, F,
F, T, F, F, F,
F, F, T, F, F,
F, F, F, T, F),
labels=c(NA, NA, NA, NA, NA,
"beta", NA, NA, NA, NA,
NA, "beta", NA, NA, NA,
NA, NA, "beta", NA, NA,
NA, NA, NA, "beta", NA),
byrow=TRUE,
name="A"),
mxMatrix("Symm", nrow=5, ncol=5,
values=c(1, 0, 0, 0, 0,
0, 1, 0, 0, 0,
0, 0, 1, 0, 0,
0, 0, 0, 1, 0,
0, 0, 0, 0, 1),
free=c(T, F, F, F, F,
F, T, F, F, F,
F, F, T, F, F,
F, F, F, T, F,
F, F, F, F, T),
labels=c("varx", NA, NA, NA, NA,
NA, "e2", NA, NA, NA,
NA, NA, "e3", NA, NA,
NA, NA, NA, "e4", NA,
NA, NA, NA, NA, "e5"),
byrow=TRUE,
name="S"),
mxMatrix("Iden", nrow=5, ncol=5,
dimnames=list(
c("x1","x2","x3","x4","x5"), c("x1","x2","x3","x4","x5")),
name="F"),
mxMatrix("Full", nrow=1, ncol=5,
values=c(1,1,1,1,1),
free=c(T, T, T, T, T),
labels=c("mean1","int2","int3","int4","int5"),
dimnames=list(
NULL, c("x1","x2","x3","x4","x5")),
name="M"),
mxFitFunctionML(),mxExpectationRAM("A","S","F","M")
)
autoregressiveMatrixCov<-mxRun(model)
autoregressiveMatrixCov$output
chisq = autoregressiveMatrixCov$output$Minus2LogLikelihood - autoregressiveMatrixCov$output$SaturatedLikelihood
# Slightly disagrees with old Mx Output, but matches Mplus exactly
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["beta"]], 0.4268, 0.001)
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["varx"]], 0.6652, 0.001)
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["e2"]], 1.1419, 0.001)
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["e3"]], 1.0379, 0.001)
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["e4"]], 0.7905, 0.001)
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["e5"]], 0.8179, 0.001)
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["mean1"]], 3.054, 0.001)
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["int2"]], 0.0816, 0.001)
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["int3"]], 0.0889, 0.001)
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["int4"]], -0.036, 0.001)
omxCheckCloseEnough(autoregressiveMatrixCov$output$estimate[["int5"]], -0.135, 0.001)
omxCheckCloseEnough(chisq, 8.4949, 0.001)
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