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################################################
# Copyright 2021 NIEHS <matt.wheeler@nih.gov>
#Permission is hereby granted, free of charge, to any person obtaining a copy of this software
#and associated documentation files (the "Software"), to deal in the Software without restriction,
#including without limitation the rights to use, copy, modify, merge, publish, distribute,
#sublicense, and/or sell copies of the Software, and to permit persons to whom the Software
#is furnished to do so, subject to the following conditions:
#
#The above copyright notice and this permission notice shall be included in all copies
#or substantial portions of the Software.
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
#CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
#OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
#
#################################################
.crutial_stat_constant <- function(param,y,doses,var,mean_function,decreasing,alpha=0){
expected <- mean_function(param,doses,decreasing)
sq_resid <- (y-expected)^2/(var*expected^alpha)
return(sum(sq_resid))
}
#@ .pvalue_cont_mcmc
#@ Function that computes a p-value from an MCMC object based upon
#@ the method of Johnson(2007) on Bayesian pivitol quantities
.pvalue_cont_mcmc <- function(mcmc_fit,model,y,doses,distribution,decreasing){
pValue_return = NA;
if (model == "FUNL"){
func = .cont_FUNL_f
}
if (model == "exp-5"){
func = .cont_exp_5_f
}
if (model == "exp-3"){
func = .cont_exp_3_f
}
if (model == "hill"){
func = .cont_hill_f
}
if (model == "power"){
func = .cont_power_f
}
if (model == "polynomial"){
}
if (distribution == "normal"){
q<- apply(mcmc_fit$mcmc_result$PARM_samples[1000:nrow(mcmc_fit$mcmc_result$PARM_samples),], 1,.crutial_stat_constant,y=y,
doses=doses,var =exp(mcmc_fit$varOpt[1]),mean_function=func,decreasing=decreasing,alpha=0)
temp <- pchisq(quantile(q,0.90),length(y)-1)
pValue_return = 1 - max(0,(temp*length(q)-0.90*length(q)+1)/(length(q)-0.90*length(q)+1))
}
if (distribution == "normal-ncv"){
q<- apply(mcmc_fit$mcmc_result$PARM_samples[1000:nrow(mcmc_fit$mcmc_result$PARM_samples),], 1,.crutial_stat_constant,y=y,
doses=doses,var =exp(mcmc_fit$varOpt[2]),mean_function=func,decreasing=decreasing,alpha=mcmc_fit$varOpt[3])
temp <- pchisq(quantile(q,0.90),length(y)-1)
pValue_return = 1 - max(0,(temp*length(q)-0.90*length(q)+1)/(length(q)-0.90*length(q)+1))
}
return(pValue_return)
}
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