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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)
myDataRaw<-read.table("data/myAutoregressiveData.txt",header=T)
model<-mxModel("Autoregressive Model, Matrix Specification, Raw Data",
mxData(myDataRaw,type="raw"),
mxMatrix("Full", nrow=5, ncol=5,
values=c(0,1,0,0,0,
0,0,1,0,0,
0,0,0,1,0,
0,0,0,0,1,
0,0,0,0,0),
free=c(F, T, F, F, F,
F, F, T, F, F,
F, F, F, T, F,
F, F, F, F, T,
F, F, F, F, F),
labels=c(NA, "beta", NA, NA, NA,
NA, NA, "beta", NA, NA,
NA, NA, NA, "beta", NA,
NA, NA, NA, NA, "beta",
NA, NA, NA, NA, 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")
)
autoregressiveMatrixRaw<-mxRun(model)
autoregressiveMatrixRaw$output
# Comparing to old Mx Output
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["beta"]], 0.372, 0.001)
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["varx"]], 0.6124, 0.001)
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["e2"]], 1.1325, 0.001)
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["e3"]], .8935, 0.001)
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["e4"]], 0.8546, 0.001)
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["e5"]], 1.014, 0.01)
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["mean1"]], 2.5369, 0.001)
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["int2"]], 1.1318, 0.001)
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["int3"]], 0.5854, 0.001)
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["int4"]], 0.2642, 0.001)
omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["int5"]], -0.0272, 0.001)
# Comparing to Mplus values
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["beta"]], 0.427, 0.001)
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["varx"]], 0.665, 0.001)
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["e2"]], 1.142, 0.001)
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["e3"]], 1.038, 0.001)
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["e4"]], 0.791, 0.001)
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["e5"]], 0.818, 0.001)
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["mean1"]], 3.054, 0.001)
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["int2"]], 0.082, 0.001)
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["int3"]], 0.089, 0.001)
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["int4"]], -0.036, 0.001)
# omxCheckCloseEnough(autoregressiveMatrixRaw$output$estimate[["int5"]], -0.135, 0.001)
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