#
# 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.
#options(error = browser)
library(OpenMx)
library(testthat)
context("DataErrorDetection")
data <- mxData(type = 'raw', matrix(".", 3, 3, dimnames = list(NULL,c('a','b','c'))))
covariance <- mxMatrix('Symm', 3, 3, values = c(1:6), name = 'cov')
means <- mxMatrix('Full', 1, 3, values = c(1:3), name = 'means')
objective <- mxExpectationNormal('cov', 'means')
model <- mxModel('model', objective, covariance, means, data, mxFitFunctionML())
omxCheckError(mxRun(model), paste("The data object", omxQuotes("model.data"),
"contains an observed matrix that is not of type 'double'"))
# Define a model
model <- mxModel()
model <- mxModel(model, mxMatrix("Full", values = c(0,0.2,0,0), name="A", nrow=2, ncol=2))
model <- mxModel(model, mxMatrix("Symm", values = c(0.8,0,0,0.8), name="S", nrow=2, ncol=2, free=TRUE))
model <- mxModel(model, mxMatrix("Iden", name="F", nrow=2, ncol=2, dimnames = list(c('a','b'), c('a','b'))))
model[["A"]]$free[2,1] <- TRUE
model[["S"]]$free[2,1] <- FALSE
model[["S"]]$free[1,2] <- FALSE
model[["S"]]$labels[1,1] <- "apple"
model[["S"]]$labels[2,2] <- "banana"
# Bounds must be added after all the free parameters are specified
model <- mxModel(model, mxBounds(c("apple", "banana"), 0.001, NA))
# Define the objective function
objective <- mxExpectationRAM("A", "S", "F")
# Define the observed covariance matrix
covMatrix <- matrix( c(0.77642931, 0.39590663, 0.39590663, 0.49115615),
nrow = 2, ncol = 2, byrow = TRUE, dimnames = list(c('a','b'), c('a','b')))
data <- mxData(covMatrix, 'cov', numObs = 100)
data$numObs <- 100L
# Add the objective function and the data to the model
model <- mxModel(model, objective, data, mxFitFunctionML())
fit <- mxRun(model)
primaryKey <- c(1:4, 3L)
m1 <- mxModel("uniqueModel", type="RAM",
latentVars = "ign",
mxData(type="raw", observed=data.frame(key=primaryKey), primaryKey = "key"),
mxPath("one", "ign"),
mxPath("ign", arrows=2))
omxCheckError(mxRun(m1), "uniqueModel.data: primary keys are not unique (examine rows with key=3)")
bad <- mxModel("bad", type="RAM",
latentVars = "ign",
mxPath("one", "ign"),
mxPath("ign", arrows=2),
mxData(data.frame(key=1), 'raw', primaryKey="key"))
omxCheckError(mxRun(bad), "bad.data: primary key must be an integer or factor column in raw observed data")
omxCheckError(mxData(mtcars[1:2,1:2], type="cov", numObs= 77),
"The observed matrix is not symmetric. Check what you are providing to mxData and perhaps try round(yourData, x) for x digits of precision.")
m <- diag(2)
m[1,2] <- .001
expect_error(mxData(m, type="cov", numObs=10),
"The observed matrix is not a symmetric matrix, possibly due to rounding errors.")
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