View source: R/confRegUCRandUCCR.R
confRegUCRandUCCR | R Documentation |
Determines whether a parameter vector is in the confidence region UCR and/or UCCR, according to the definitions in Espinosa and Hennig (2023) <https://doi.org/10.48550/arXiv.2107.04946>.
confRegUCRandUCCR(
UMLE = NULL,
paramVals = NULL,
paramIDs = NULL,
SignifLevel = 0.05,
df,
matY,
matX
)
UMLE |
A vector with the unconstrained maximum likelihood estimates. |
paramVals |
A vector with the parameter values for which it is needed to
assess whether it is part of one of the confidence regions or not.
The order of the parameters must be the same as the one of |
paramIDs |
A vector indicating the positions of the parameter values of
beta_0r in |
SignifLevel |
A decimal number indicating the significant level. Usually, 0.05. |
df |
Degrees of freedom to be used. |
matY |
matY resulting from mdcp(). |
matX |
matX resulting from mdcp(). |
confRegions
: Data frame with columns:
UMLE_logLik
=log-likelihood of the unconstrained model,
param_logLik
=log-likelihood of the model using paramVals
,
monotonicBeta0
=logical value, TRUE
if the set of parameters
of paramVals
indicated by paramIDs
are monotonic,
df
=degrees of freedom used to calculate the critical value,
StatUCR
=value of the statistic used for UCR
,
StatUCCR
=value of the statistic used for UCCR
,
CritValue
=critical value, chi-squared with df
and 1-SignifLevel
,
SignifLevel
=significance level used to calculate the critical value,
inUCR
=logical value, TRUE
if paramVals
belongs to the confidence region UCR
,
inUCCR
=logical value, TRUE
if paramVals
belongs to the confidence region UCCR
,
Espinosa, J., and Hennig, C. "Inference for the proportional odds cumulative logit model with monotonicity constraints for ordinal predictors and ordinal response." Arxiv (2023). <https://doi.org/10.48550/arXiv.2107.04946>.
confRegCCR
,
mdcp
,
monoTestBonf
,
monoTestConfReg
,
plotCMLE
,
vlgm
.
resAux <- mdcp(QoL ~ EduLevel + Age + IncQuint + Gender + Health, data = crovData)
plotCMLE(resAux)
myVector <- resAux$estimates
myVectorID <- 10:12
myVector[myVectorID]
# non-monotonic beta_{0r}, paramVals in UCR but not in UCCR
myVector[myVectorID] <- seq(0.195,0.185,length.out=3)
confRegUCRandUCCR(UMLE=resAux$UMLE, paramVals=myVector, paramIDs=myVectorID,SignifLevel=0.05, df=3,
matY= resAux$matY, matX= resAux$matX)
# monotonic beta_{0r}, paramVals in UCR and UCCR
myVector[myVectorID] <- seq(0.073,0.074,length.out=3)
confRegUCRandUCCR(UMLE=resAux$UMLE, paramVals=myVector, paramIDs=myVectorID,SignifLevel=0.05, df=3,
matY= resAux$matY, matX= resAux$matX)
# monotonic beta_{0r}, paramVals out of UCR and UCCR
myVector[myVectorID] <- seq(0.072,0.073,length.out=3)
confRegUCRandUCCR(UMLE=resAux$UMLE, paramVals=myVector, paramIDs=myVectorID,SignifLevel=0.05, df=3,
matY= resAux$matY, matX= resAux$matX)
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