#
# Copyright 2007-2019 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.
# -----------------------------------------------------------------------------
# Program: BivariateSaturated_PathCov.R
# Author: Hermine Maes
# Date: 2009.08.01
#
# ModelType: Saturated
# DataType: Continuous
# Field: None
#
# Purpose:
# Bivariate Saturated model to estimate means and (co)variances
# Path style model input - Covariance matrix data input
#
# RevisionHistory:
# Hermine Maes -- 2009.10.08 updated & reformatted
# Ross Gore -- 2011.06.15 added Model, Data & Field metadata
# -----------------------------------------------------------------------------
require(OpenMx)
require(MASS)
# Load Libraries
# -----------------------------------------------------------------------------
set.seed(200)
rs=.5
xy <- mvtnorm::rmvnorm (1000, c(0,0), matrix(c(1,rs,rs,1),2,2))
testData <- xy
testData <- testData[, order(apply(testData, 2, var))[2:1]] #put the data columns in order from largest to smallest variance
# Note: Users do NOT have to re-order their data columns. This is only to make data generation the same on different operating systems: to fix an inconsistency with the mvtnorm::rmvnorm function in the MASS package.
selVars <- c("X","Y")
dimnames(testData) <- list(NULL, selVars)
summary(testData)
cov(testData)
# Simulate Data
# -----------------------------------------------------------------------------
bivSatModel1 <- mxModel("bivSat1",
manifestVars= selVars,
mxPath(
from=c("X", "Y"),
arrows=2,
free = TRUE,
values=1,
lbound=.01,
labels=c("varX","varY")
),
mxPath(
from="X",
to="Y",
arrows=2,
free = TRUE,
values=.2,
lbound=.01,
labels="covXY"
),
mxData(
observed=cov(testData),
type="cov",
numObs=1000
),
type="RAM"
)
bivSatFit1 <- mxRun(bivSatModel1)
EC1 <- mxEval(S, bivSatFit1)
LL1 <- mxEval(objective, bivSatFit1)
SL1 <- summary(bivSatFit1)$SaturatedLikelihood
Chi1 <- LL1-SL1
# example 1: Saturated Model with Cov Matrices and Path-Style Input
# -----------------------------------------------------------------------------
bivSatModel1m <- mxModel("bivSat1m",
manifestVars= selVars,
mxPath(
from=c("X", "Y"),
arrows=2,
free = TRUE,
values=1,
lbound=.01,
labels=c("varX","varY")
),
mxPath(
from="X",
to="Y",
arrows=2,
free = TRUE,
values=.2,
lbound=.01,
labels="covXY"
),
mxPath(
from="one",
to=c("X", "Y"),
arrows=1,
free = TRUE,
values=.01,
labels=c("meanX","meanY")
),
mxData(
observed=cov(testData),
type="cov",
numObs=1000,
means=colMeans(testData)
),
type="RAM"
)
bivSatFit1m <- mxRun(bivSatModel1m)
EM1m <- mxEval(M, bivSatFit1m)
EC1m <- mxEval(S, bivSatFit1m)
LL1m <- mxEval(objective, bivSatFit1m)
SL1m <- summary(bivSatFit1m)$SaturatedLikelihood
Chi1m <- LL1m-SL1m
# example 1m: Saturated Model with Cov Matrices & Means and Path-Style Input
# -----------------------------------------------------------------------------
omxCheckCloseEnough(Chi1, -0.001, .001)
omxCheckCloseEnough(c(EC1),c(1.065, 0.475, 0.475, 0.929),.001)
# 1:CovPat
# -------------------------------------
omxCheckCloseEnough(Chi1m, -0.001,.001)
omxCheckCloseEnough(c(EC1m),c(1.065, 0.475, 0.475, 0.929),.001)
omxCheckCloseEnough(c(EM1m),c(0.058, 0.006),.001)
# 1m:CovMPat
# -------------------------------------
# Compare OpenMx results to Mx results
# (LL: likelihood; EC: expected covariance, EM: expected means)
# -----------------------------------------------------------------------------
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.