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backupfiles/OpenBUGScodes from the Bayesian Statistics Chapter
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OpenBUGSPractical0400: | ||
# OpenBUGS codes: | ||
# # Step 1 check model | ||
# modelCheck(paste(bugpath, "/backupfiles/MCdrugPractical04.txt", sep = "")) | ||
# | ||
# # compile the model | ||
# modelCompile(numChains = 1) | ||
# # There is no need to provide initial values as | ||
# # they are aleady provided in the model specification | ||
# modelGenInits() # | ||
# # Set monitors on nodes of interest#### SPECIFY, WHICH PARAMETERS TO TRACE: | ||
# parameters <- c("theta") | ||
# samplesSet(parameters) | ||
# | ||
# # Generate 1000 iterations | ||
# modelUpdate(1000) | ||
# #### PUT THE SAMPLED VALUES IN ONE R DATA FRAME: | ||
# chain <- data.frame(theta = samplesSample("theta")) | ||
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# #### PLOT THE MCMC CHAINS: | ||
# plot(chain$theta, main=~theta, type="l", ylim = c(0.2, 1.2), | ||
# ylab="theta", xlab="iteration", col="red") | ||
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OpenBUGSPractical0401: | ||
# OpenBUGS codes: | ||
# # Step 1 check model | ||
# modelCheck(paste(bugpath, "/backupfiles/logistic-reg-model.txt", sep = "")) | ||
# # Load the data | ||
# modelData(paste(bugpath, "/backupfiles/logistic-reg-data.txt", sep = "")) | ||
# # compile the model | ||
# modelCompile(numChains = 1) | ||
# # generate initial values | ||
# modelGenInits() | ||
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OpenBUGSPractical0402: | ||
# Set monitors on nodes of interest#### SPECIFY, WHICH PARAMETERS TO TRACE: | ||
# parameters <- c("beta0", "beta1", "theta[6]") | ||
# samplesSet(parameters) | ||
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# Generate 1000 iterations | ||
# modelUpdate(1000) | ||
#### PUT THE SAMPLED VALUES IN ONE R DATA FRAME: | ||
# chain <- data.frame(beta0 = samplesSample("beta0"), | ||
# beta1 = samplesSample("beta1"), | ||
# theta6 = samplesSample("theta[6]")) | ||
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OpenBUGSPractical0405 | ||
# OpenBUGS codes: | ||
# # Step 1 check model | ||
# modelCheck(paste(bugpath, "/backupfiles/logistic-reg-model.txt", sep = "")) | ||
# # Load the data | ||
# modelData(paste(bugpath, "/backupfiles/logistic-reg-data.txt", sep = "")) | ||
# # compile the model with two separate chains | ||
# modelCompile(numChains = 2) | ||
# # generate initial values | ||
# modelGenInits() | ||
# # Set monitors on nodes of interest#### SPECIFY, WHICH PARAMETERS TO TRACE: | ||
# parameters <- c("beta0", "beta1", "theta[6]") | ||
# samplesSet(parameters) | ||
# | ||
# # Generate 1000 iterations | ||
# modelUpdate(1000) | ||
# #### PUT THE SAMPLED VALUES IN ONE R DATA FRAME: | ||
# chain <- data.frame(beta0 = samplesSample("beta0"), | ||
# beta1 = samplesSample("beta1"), | ||
# theta6 = samplesSample("theta[6]")) | ||
# samplesHistory("*", mfrow = c(3,1), ask = FALSE) | ||
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OpenBUGSPractical040808 | ||
# samplesAutoC("beta0", mfrow = c(1, 1), 1, beg = 501, | ||
# ask = FALSE, main = "", lag.max = 100) | ||
# samplesAutoC("beta0", mfrow = c(1, 1), 2, beg = 501, | ||
# ask = FALSE, main = "", lag.max = 100) | ||
# acf(chain$beta0, main="beta0",lwd=4,col="red", lag.max = 50) | ||
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OpenBUGSPractical0411 | ||
# # step 1 check model | ||
# jagsModel <- jags.model( | ||
# file = paste(bugpath, | ||
# "/backupfiles/logistic-reg-model.txt", | ||
# sep = ""), | ||
# data = Dat, | ||
# n.chains = 2, | ||
# inits = inits, | ||
# quiet = TRUE) | ||
# # Step 2 update 10000 iterations | ||
# | ||
# update(jagsModel, n.iter = 1, progress.bar = "none") | ||
# | ||
# # Step 3 set monitor variables | ||
# | ||
# codaSamples <- coda.samples( | ||
# jagsModel, variable.names = c("beta0", "beta1", "theta[6]"), | ||
# n.iter = 10000, progress.bar = "none" | ||
# ) | ||
# summary(codaSamples) | ||
# OpenBUGS codes: | ||
# # Step 1 check model | ||
# modelCheck(paste(bugpath, "/backupfiles/logistic-reg-model.txt", sep = "")) | ||
# # Load the data | ||
# modelData(paste(bugpath, "/backupfiles/logistic-reg-data.txt", sep = "")) | ||
# # compile the model with two separate chains | ||
# modelCompile(numChains = 2) | ||
# # generate initial values | ||
# # the choice is arbitrary | ||
# initlist <- list(beta0=-45, beta1=38) | ||
# modelInits(bugsData(initlist)) | ||
# initlist1 <- list(beta0=60, beta1=-40) | ||
# modelInits(bugsData(initlist1)) | ||
# | ||
# # Set monitors on nodes of interest#### SPECIFY, WHICH PARAMETERS TO TRACE: | ||
# parameters <- c("beta0", "beta1", "theta[6]") | ||
# samplesSet(parameters) | ||
# | ||
# # Generate 10000 iterations | ||
# modelUpdate(10000) | ||
# #### PUT THE SAMPLED VALUES IN ONE R DATA FRAME: | ||
# chain <- data.frame(beta0 = samplesSample("beta0"), | ||
# beta1 = samplesSample("beta1"), | ||
# theta6 = samplesSample("theta[6]")) | ||
# | ||
# #### PLOT the chain history of beta0, beta1 | ||
# samplesHistory("beta0", mfrow = c(1,1), ask = FALSE) | ||
# samplesHistory("beta1", mfrow = c(1,1), ask = FALSE) | ||
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OpenBUGSPractical04110000: | ||
# # step 1 check model | ||
# jagsModel <- jags.model( | ||
# file = paste(bugpath, | ||
# "/backupfiles/logistic-reg-model.txt", | ||
# sep = ""), | ||
# data = Dat, | ||
# n.chains = 2, | ||
# inits = inits, | ||
# quiet = TRUE) | ||
# # Step 2 update 10000 iterations | ||
# | ||
# update(jagsModel, n.iter = 2000, progress.bar = "none") | ||
# | ||
# # Step 3 set monitor variables | ||
# | ||
# codaSamples <- coda.samples( | ||
# jagsModel, variable.names = c("beta0", "beta1", "theta[6]"), | ||
# n.iter = 5000, progress.bar = "none" | ||
# ) | ||
# summary(codaSamples) | ||
# OpenBUGS codes: | ||
# # Step 1 check model | ||
# modelCheck(paste(bugpath, "/backupfiles/logistic-reg-model.txt", sep = "")) | ||
# # Load the data | ||
# modelData(paste(bugpath, "/backupfiles/logistic-reg-data.txt", sep = "")) | ||
# # compile the model with two separate chains | ||
# modelCompile(numChains = 2) | ||
# # generate initial values | ||
# # the choice is arbitrary | ||
# initlist <- list(beta0=-45, beta1=38) | ||
# modelInits(bugsData(initlist)) | ||
# initlist1 <- list(beta0=60, beta1=-40) | ||
# modelInits(bugsData(initlist1)) | ||
# | ||
# # Set monitors on nodes of interest#### SPECIFY, WHICH PARAMETERS TO TRACE: | ||
# parameters <- c("beta0", "beta1", "theta[6]") | ||
# samplesSet(parameters) | ||
# | ||
# # Generate 10000 iterations | ||
# modelUpdate(10000) | ||
# #### PUT THE SAMPLED VALUES IN ONE R DATA FRAME: | ||
# chain <- data.frame(beta0 = samplesSample("beta0"), | ||
# beta1 = samplesSample("beta1"), | ||
# theta6 = samplesSample("theta[6]")) | ||
# # Generate another 1000000 iterations | ||
# modelUpdate(42000) | ||
# sample.statistics <- samplesStats("*", beg = 2001) | ||
# print(sample.statistics) | ||
# #### PLOT the chain history of beta0, beta1 | ||
# samplesHistory("beta0", mfrow = c(1,1), ask = FALSE) | ||
# samplesHistory("beta1", mfrow = c(1,1), ask = FALSE) | ||
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OpenBUGSPractical0420 | ||
# # OpenBUGS codes | ||
# # Step 1 check model | ||
# modelCheck(paste(bugpath, "/backupfiles/logistic-reg-model-centred.txt", sep = "")) | ||
# # Load the data | ||
# modelData(paste(bugpath, "/backupfiles/logistic-reg-data.txt", sep = "")) | ||
# # compile the model with two separate chains | ||
# modelCompile(numChains = 2) | ||
# # generate initial values | ||
# # the choice is arbitrary | ||
# initlist <- list(beta0=-45, beta1=38) | ||
# modelInits(bugsData(initlist)) | ||
# initlist1 <- list(beta0=60, beta1=-40) | ||
# modelInits(bugsData(initlist1)) | ||
# | ||
# # Set monitors on nodes of interest#### SPECIFY, WHICH PARAMETERS TO TRACE: | ||
# parameters <- c("beta0", "beta1", "theta[6]") | ||
# samplesSet(parameters) | ||
# | ||
# # Generate 10000 iterations | ||
# modelUpdate(20000) | ||
# #### PUT THE SAMPLED VALUES IN ONE R DATA FRAME: | ||
# chain <- data.frame(beta0 = samplesSample("beta0"), | ||
# beta1 = samplesSample("beta1"), | ||
# theta6 = samplesSample("theta[6]")) | ||
# sample.statistics <- samplesStats("*", beg = 7501) | ||
# print(sample.statistics) | ||
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OpenBUGSPractical0424 | ||
# OpenBUGS codes: | ||
# # Step 1 check model | ||
# modelCheck(paste(bugpath, "/backupfiles/logistic-reg-model-centred-stat.txt", sep = "")) | ||
# # Load the data | ||
# modelData(paste(bugpath, "/backupfiles/logistic-reg-data.txt", sep = "")) | ||
# # compile the model with two separate chains | ||
# modelCompile(numChains = 2) | ||
# # generate initial values | ||
# # the choice is arbitrary | ||
# initlist <- list(beta0=-45, beta1=38) | ||
# modelInits(bugsData(initlist)) | ||
# initlist1 <- list(beta0=60, beta1=-40) | ||
# modelInits(bugsData(initlist1)) | ||
# | ||
# # Set monitors on nodes of interest#### SPECIFY, WHICH PARAMETERS TO TRACE: | ||
# parameters <- c("ED95", "OR", "P35", "beta0", "beta1") | ||
# samplesSet(parameters) | ||
# | ||
# # Generate 10000 iterations | ||
# modelUpdate(20000) | ||
#### PUT THE SAMPLED VALUES IN ONE R DATA FRAME: | ||
# chain <- data.frame(ED95 = samplesSample("ED95"), | ||
# OR = samplesSample("OR"), | ||
# P35 = samplesSample("P35"), | ||
# beta0 = samplesSample("beta0"), | ||
# beta1 = samplesSample("beta1")) | ||
# sample.statistics <- samplesStats("*", beg = 7501) | ||
# print(sample.statistics) | ||
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@@ -8,4 +8,3 @@ model{ | |
beta0 ~ dunif(-100, 100) | ||
beta1 ~ dunif(-100, 100) | ||
} | ||
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model { | ||
for( i in 1 : N ) { # loop thorugh experiments | ||
y[i] ~ dbin(theta[i],n[i]) | ||
logit(theta[i]) <- beta0 + beta1 * x[i] | ||
} | ||
# priors | ||
beta0 ~ dunif(-100, 100) | ||
beta1 ~ dunif(-100, 100) | ||
} | ||
model { | ||
for( i in 1 : N ) { # loop thorugh experiments | ||
y[i] ~ dbin(theta[i],n[i]) | ||
# y[i] ~ dbern(theta[i]) | ||
logit(theta[i]) <- beta0 + beta1 * x[i] | ||
} | ||
# priors | ||
beta0 ~ dunif(-100, 100) | ||
beta1 ~ dunif(-100, 100) | ||
} |
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