principalstratmod_monoind: Fit principal stratification model
In czigler/HEIfunctions: Functions for HEI Air pollution accountability grant
Usage
1
Arguments
formula
data
trt
formula_h
denom
nsamp
nburn
thin
tuning
prior
starting
Examples
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##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (formula, data, trt, formula_h, denom, nsamp, nburn,
thin, tuning = list(psi = 0.2, alpha0 = alpha0prop, alpha1 = alpha1prop,
B0 = B0prop, B1 = B1prop, Y = ypropsd), prior = list(psi = rep(0.01,
2)), starting = list(B = NULL, psi = NULL, alpha0 = NULL,
alpha1 = NULL))
{
require(MCMCpack)
require(corpcor)
require(MASS)
require(msm)
ncov <- dim(model.matrix(formula, data))[2]
print("Note: function does not accept missing data in covariates.")
logit = function(theta, a, b) {
return(log((theta - a)/(b - theta)))
}
logitInv = function(z, a, b) {
return(b - (b - a)/(1 + exp(z)))
}
index <- seq(from = (nburn + 1), to = nsamp, by = thin)
n <- dim(data)[1]
q <- 2
names <- colnames(model.matrix(formula, data))
data$a <- data[, trt]
data$y <- data[, all.vars(formula)[1]]
data$ytemp <- 1
nterm <- length(labels(terms(formula)))
terms <- NULL
for (ii in 1:nterm) {
if (ii == 1)
terms <- labels(terms(formula))[ii]
if (ii > 1)
terms <- paste(terms, "+", labels(terms(formula))[ii],
sep = " ")
}
formulatemp <- as.formula(paste("ytemp ~ ", terms))
covs <- model.matrix(formulatemp, data)
names_h <- colnames(model.matrix(formula_h, data))
data$h <- data[, all.vars(formula_h)[1]]
data$ytemp <- 1
nterm <- length(labels(terms(formula_h)))
terms <- NULL
for (ii in 1:nterm) {
if (ii == 1)
terms <- labels(terms(formula_h))[ii]
if (ii > 1)
terms <- paste(terms, "+", labels(terms(formula_h))[ii],
sep = " ")
}
formulatemp <- as.formula(paste("ytemp ~ ", terms))
covs_h <- model.matrix(formulatemp, data)
makeXmat = function(Xvals) {
p = dim(Xvals)[[2]]
Xout = matrix(0, n * q, p * q)
Xout[seq(1, n * q, q), 1:p] = Xvals
Xout[seq(2, n * q, q), (p + 1):(2 * p)] = Xvals
return(Xout)
}
X = makeXmat(covs)
p = ncol(X)
X_h = covs_h
p_h = ncol(X_h) + 1
y0 = rep(NA, n)
y1 = rep(NA, n)
y0[data$a == 0] = data$y[data$a == 0]
y1[data$a == 1] = data$y[data$a == 1]
Y = rep(-999, n * q)
Y[seq(1, n * q, q)] = y0
Y[seq(2, n * q, q)] = y1
ismissy = (is.na(Y))
nwithmissing = sum(ismissy)
H = rep(-999, n * 2)
H[seq(1, n * 2, 2)][data$a == 0] = data$h[data$a == 0]
H[seq(2, n * 2, 2)][data$a == 1] = data$h[data$a == 1]
denom = data[, denom]
a = rep(NA, n * q)
a[seq(1, n * q, q)] = data$a
a[seq(2, n * q, q)] = data$a
psiig_a = prior$psi
psiig_b = prior$psi
my0 = summary(lm(y0 ~ data$ps))
my1 = summary(lm(y1 ~ data$ps))
missy1 = ismissy[seq(1, n * q, q)]
missy2 = ismissy[seq(2, n * q, q)]
missboth = rep(FALSE, n)
missboth[is.na(Y[seq(1, n * q, q)]) & is.na(Y[seq(2, n *
q, q)])] = TRUE
Y[seq(1, n * q, q)][missboth == TRUE] = rnorm(sum(missboth ==
TRUE), data$base[missboth == TRUE], 0.2 * sd(dat$y, na.rm = TRUE))
Y[seq(2, n * q, q)][missy2 == 1] = rtnorm(sum(missy2 == 1),
mean(Y[seq(2, n * q, q)], na.rm = T), 0.2 * sd(Y[seq(2,
n * 1, q)], na.rm = T), lower = -Inf, upper = Y[seq(1,
n * q, q)][missy2 == 1])
Y[seq(1, n * q, q)][missy1 == 1] = rtnorm(sum(missy1 == 1),
mean(Y[seq(1, n * q, q)], na.rm = T), 0.2 * sd(Y[seq(1,
n * q, q)], na.rm = T), lower = Y[seq(2, n * q, q)][missy1 ==
1], upper = Inf)
H[seq(1, n * 2, 2)][data$a == 1] = rpois(sum(data$a == 1),
10)
H[seq(2, n * 2, 2)][data$a == 0] = rpois(sum(data$a == 0),
10)
if (is.null(starting$B) == F) {
B = starting$B
}
if (is.null(starting$B) == T) {
B = rep(0, ncov * 2)
}
if (is.null(starting$psi) == T) {
initpsis = c(my0$sigma^2, my1$sigma^2)
}
else {
initpsis = starting$psi
}
if (is.null(starting$alpha0) == T) {
initalpha = rep(0, 2 * p_h)
}
else {
initalpha = c(starting$alpha0, starting$alpha1)
}
psipropsds = tuning$psi
alpha0propcov = tuning$alpha0
alpha1propcov = tuning$alpha1
B0propcov = tuning$B0
B1propcov = tuning$B1
propysds = tuning$Y
loglike_norm_monoind = function(Yvals, Bvals, psivals) {
lowertrunc = rep(NA, n * q)
uppertrunc = lowertrunc
lowertrunc[seq(1, n * q, q)] = Yvals[seq(2, n * q, q)]
uppertrunc[seq(1, n * q, q)] = Inf
uppertrunc[seq(2, n * q, q)] = Yvals[seq(1, n * q, q)]
lowertrunc[seq(2, n * q, q)] = -Inf
m = as.vector(X %*% Bvals)
index = seq(1, n * q, q)
llike = sum(dtnorm(Yvals[index], mean = m[index], sd = sqrt(psivals[1]),
lower = lowertrunc[index], upper = uppertrunc[index],
log = TRUE))
index = seq(2, n * q, q)
llike = llike + sum(dtnorm(Yvals[index], mean = m[index],
sd = sqrt(psivals[2]), lower = lowertrunc[index],
upper = uppertrunc[index], log = TRUE))
llike = llike + log(dinvgamma(psivals[1], psiig_a[1],
psiig_b[1])) + log(dinvgamma(psivals[2], psiig_a[2],
psiig_b[2]))
return(llike)
}
loglike_pois = function(Yvals, alphavals, h0vals, h1vals) {
ya0 = Yvals[seq(1, n * q, q)]
ya1 = Yvals[seq(2, n * q, q)]
Xmat0 = as.matrix(cbind(X_h, ya0))
alpha0 = alphavals[1:p_h]
lambda0 = exp(Xmat0 %*% alpha0 + log(denom))
Xmat1 = as.matrix(cbind(X_h, ya1))
alpha1 = alphavals[(p_h + 1):length(alphavals)]
lambda1 = exp(Xmat1 %*% alpha1 + log(denom))
llike = sum(h0vals * log(lambda0) - lambda0 - lfactorial(h0vals))
llike = llike + sum(h1vals * log(lambda1) - lambda1 -
lfactorial(h1vals))
return(llike)
}
MHstep_pois = function(whichalphas, Yvals, alphavals, h0vals,
h1vals, currentll) {
accept = 0
llret = currentll
alpharet = alphavals
alphaprop = alphavals
index = 1:p_h
if (whichalphas == 1) {
index = (p_h + 1):length(alphavals)
}
alphaprop[index] = mvrnorm(1, alpharet[index], alphapropcovs[[whichalphas +
1]])
llprop = loglike_pois(Yvals, alphaprop, h0vals, h1vals)
ratio = exp(llprop - currentll)
ratio[ratio > 1] = 1
if (runif(1) <= ratio) {
alpharet = alphaprop
llret = llprop
accept = 1
}
outlist = list(alpharet, llret, accept)
names(outlist) = c("alpha", "ll", "accepted")
return(outlist)
}
MHstep_B = function(Yvals, Bvals, psivals, currentll) {
accept = 0
llret = currentll
Bret = Bvals
Bprop = Bvals
Bprop = mvrnorm(1, Bret, Bpropcov)
llprop = loglike_norm_monoind(Yvals, Bprop, psivals)
ratio = exp(llprop - currentll)
if (runif(1) <= ratio) {
Bret = Bprop
llret = llprop
accept = 1
}
outlist = list(Bret, llret, accept)
names(outlist) = c("B", "ll", "accepted")
return(outlist)
}
MHstep_psi = function(whichpsi, Yvals, Bvals, psivals, currentll) {
accept = 0
llret = currentll
psiret = psivals
psiprop = psivals
psiprop[whichpsi + 1] = rnorm(1, psiret[whichpsi + 1],
psipropsds[whichpsi + 1])
llprop = loglike_norm_monoind(Yvals, Bvals, psiprop)
ratio = exp(llprop - currentll)
if (runif(1) <= ratio) {
psiret = psiprop
llret = llprop
accept = 1
}
outlist = list(psiret, llret, accept)
names(outlist) = c("psi", "ll", "accepted")
return(outlist)
}
sampleH = function(whicha, alphavals, Yvals) {
ya0 = Yvals[seq(1, n * q, q)]
ya1 = Yvals[seq(2, n * q, q)]
index = 1:p_h
Xmat = cbind(X_h, ya0)
if (whicha == 1) {
index = (p_h + 1):length(alphavals)
Xmat = cbind(X_h, ya1)
}
alphs = alphavals[index]
lamda = exp(Xmat %*% alphs + log(denom))
h = rpois(sum(data$a == (1 - whicha)), lamda[data$a ==
(1 - whicha)])
return(h)
}
sampleY_monoind = function(ids, Yvals, Bvals, psivals, alphavals,
h0vals, h1vals, currentllpois, currentllnorm) {
accept = rep(0, length(which(ids)))
currentll = currentllnorm + currentllpois
llretnorm = currentllnorm
llretpois = currentllpois
Yret = Yvals
Yprop = Yvals
lowertrunc = rep(NA, n * q)
uppertrunc = lowertrunc
lowertrunc[seq(1, n * q, q)] = Yvals[seq(2, n * q, q)]
uppertrunc[seq(1, n * q, q)] = Inf
uppertrunc[seq(2, n * q, q)] = Yvals[seq(1, n * q, q)]
lowertrunc[seq(2, n * q, q)] = -Inf
Yprop[ids] = rtnorm(length(which(ids)), Yvals[ids], propysds[ids],
lower = lowertrunc[ids], upper = uppertrunc[ids])
llpropnorm = loglike_norm_monoind(Yprop, Bvals, psivals)
llproppois = loglike_pois(Yprop, alphavals, h0vals, h1vals)
propdens_prop = dtnorm(Yprop[ids], Yvals[ids], propysds[ids],
lower = lowertrunc[ids], upper = uppertrunc[ids],
log = TRUE)
propdens_current = dtnorm(Yvals[ids], Yprop[ids], propysds[ids],
lower = lowertrunc[ids], upper = uppertrunc[ids],
log = TRUE)
llprop = llpropnorm + llproppois
ratio = exp(llprop - currentll + sum(propdens_current -
propdens_prop))
ratio[ratio > 1] = 1
if (runif(1) <= ratio) {
Yret = Yprop
llretnorm = llpropnorm
llretpois = llproppois
accept = rep(1, length(which(ids)))
}
outlist = list(Yret, llretnorm, llretpois, accept)
names(outlist) = c("Y", "llnorm", "llpois", "accepted")
return(outlist)
}
params = c(initpsis, initalpha)
nparams = length(params)
propsds = c(psipropsds)
alphapropcovs = list(alpha0propcov, alpha1propcov)
Bpropcov = matrix(0, p, p)
Bpropcov[1:(p/2), 1:(p/2)] = B0propcov
Bpropcov[((p/2) + 1):p, ((p/2) + 1):p] = B1propcov
psiindex = 1:2
alpha0index = 3:(2 + p_h)
alpha1index = (max(alpha0index) + 1):(max(alpha0index) +
p_h)
accepted = rep(0, nparams)
accepted_y = rep(0, n * q)
accepted_B = rep(0, p)
binsize = 10
rho = 0
notpd = 0
samples = matrix(NA, nrow = length(index), ncol = nparams +
length(B))
dimnames(samples)[[2]] = c(paste("Psi", 1:q, sep = ""), paste("alpha0",
(0:(p_h - 1)), sep = ""), paste("alpha1", (0:(p_h - 1)),
sep = ""), paste("B0", 0:((p/2) - 1), sep = ""), paste("B1",
0:((p/2) - 1), sep = ""))
ysims = matrix(NA, nrow = length(index), ncol = length(Y))
hsims = matrix(NA, nrow = length(index), ncol = length(H))
llnorm = loglike_norm_monoind(Yvals = Y, Bvals = B, psivals = params[psiindex])
llpois = loglike_pois(Y, params[c(alpha0index, alpha1index)],
H[seq(1, n * 2, 2)], H[seq(2, n * 2, 2)])
ll = llnorm + llpois
iterno = 1
donesampling = FALSE
kk = 1
while (donesampling == FALSE) {
mhstep = MHstep_B(Y, B, params[psiindex], llnorm)
B = mhstep$B
llnorm = mhstep$ll
accepted_B = accepted_B + mhstep$accepted
if (llnorm == Inf)
print(paste("Infinite Likelihood while/after updating B at iterno=",
iterno))
ll = llnorm + llpois
mhstep = MHstep_psi(whichpsi = 0, Yvals = Y, Bvals = B,
psivals = params[psiindex], currentll = llnorm)
params[psiindex] = mhstep$psi
llnorm = mhstep$ll
accepted[psiindex[1]] = accepted[psiindex[1]] + mhstep$accepted
if (llnorm == Inf)
print(paste("Infinite Likelihood while/after updating Psi0 at iterno=",
iterno))
mhstep = MHstep_psi(1, Y, B, params[psiindex], llnorm)
params[psiindex] = mhstep$psi
llnorm = mhstep$ll
accepted[psiindex[2]] = accepted[psiindex[2]] + mhstep$accepted
if (llnorm == Inf)
print(paste("Infinite Likelihood while/after updating Psi1 at iterno=",
iterno))
ll = llnorm + llpois
mhstep = MHstep_pois(0, Y, params[c(alpha0index, alpha1index)],
H[seq(1, 2 * n, 2)], H[seq(2, 2 * n, 2)], llpois)
params[c(alpha0index, alpha1index)] = mhstep$alpha
llpois = mhstep$ll
accepted[alpha0index] = accepted[alpha0index] + mhstep$accepted
mhstep = MHstep_pois(1, Y, params[c(alpha0index, alpha1index)],
H[seq(1, 2 * n, 2)], H[seq(2, 2 * n, 2)], llpois)
params[c(alpha0index, alpha1index)] = mhstep$alpha
llpois = mhstep$ll
accepted[alpha1index] = accepted[alpha1index] + mhstep$accepted
for (whichysamp in which(ismissy)) {
whichy = rep(FALSE, n * q)
whichy[whichysamp] = TRUE
mhstep = sampleY_monoind(whichy, Y, B, params[psiindex],
params[c(alpha0index, alpha1index)], H[seq(1,
n * 2, 2)], H[seq(2, n * 2, 2)], llpois, llnorm)
Y = mhstep$Y
llnorm = mhstep$llnorm
llpois = mhstep$llpois
accepted_y[whichy] = accepted_y[whichy] + mhstep$accepted
}
H[seq(1, 2 * n, 2)][data$a == 1] = sampleH(0, params[c(alpha0index,
alpha1index)], Y)
H[seq(2, 2 * n, 2)][data$a == 0] = sampleH(1, params[c(alpha0index,
alpha1index)], Y)
llpois = loglike_pois(Y, params[c(alpha0index, alpha1index)],
H[seq(1, n * 2, 2)], H[seq(2, n * q, 2)])
ll = llnorm + llpois
if (iterno %in% index) {
samples[kk, ] = c(params[psiindex], params[c(alpha0index,
alpha1index)], B)
ysims[kk, ] = Y
hsims[kk, ] = H
kk <- kk + 1
}
if (iterno%%binsize == 0) {
print(paste("Iteration:", iterno))
print(c("Accepted:", round(accepted/iterno, 3), round(mean(accepted_y[ismissy]/iterno),
3)))
}
if (iterno >= nsamp) {
donesampling = TRUE
}
iterno = iterno + 1
}
out <- list()
out$samples <- samples
out$y0 <- ysims[, seq(1, n * q, q)]
out$y1 <- ysims[, seq(2, n * q, q)]
out$h0 <- hsims[, seq(1, n * 2, 2)]
out$h1 <- hsims[, seq(2, n * 2, 2)]
out$trt <- data$a
out$formula <- formula
out$formula_h <- formula_h
return(out)
}
czigler/HEIfunctions documentation built on May 14, 2019, 1:43 p.m.
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Usage
1 |
Arguments
formula |
|
data |
|
trt |
|
formula_h |
|
denom |
|
nsamp |
|
nburn |
|
thin |
|
tuning |
|
prior |
|
starting |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (formula, data, trt, formula_h, denom, nsamp, nburn,
thin, tuning = list(psi = 0.2, alpha0 = alpha0prop, alpha1 = alpha1prop,
B0 = B0prop, B1 = B1prop, Y = ypropsd), prior = list(psi = rep(0.01,
2)), starting = list(B = NULL, psi = NULL, alpha0 = NULL,
alpha1 = NULL))
{
require(MCMCpack)
require(corpcor)
require(MASS)
require(msm)
ncov <- dim(model.matrix(formula, data))[2]
print("Note: function does not accept missing data in covariates.")
logit = function(theta, a, b) {
return(log((theta - a)/(b - theta)))
}
logitInv = function(z, a, b) {
return(b - (b - a)/(1 + exp(z)))
}
index <- seq(from = (nburn + 1), to = nsamp, by = thin)
n <- dim(data)[1]
q <- 2
names <- colnames(model.matrix(formula, data))
data$a <- data[, trt]
data$y <- data[, all.vars(formula)[1]]
data$ytemp <- 1
nterm <- length(labels(terms(formula)))
terms <- NULL
for (ii in 1:nterm) {
if (ii == 1)
terms <- labels(terms(formula))[ii]
if (ii > 1)
terms <- paste(terms, "+", labels(terms(formula))[ii],
sep = " ")
}
formulatemp <- as.formula(paste("ytemp ~ ", terms))
covs <- model.matrix(formulatemp, data)
names_h <- colnames(model.matrix(formula_h, data))
data$h <- data[, all.vars(formula_h)[1]]
data$ytemp <- 1
nterm <- length(labels(terms(formula_h)))
terms <- NULL
for (ii in 1:nterm) {
if (ii == 1)
terms <- labels(terms(formula_h))[ii]
if (ii > 1)
terms <- paste(terms, "+", labels(terms(formula_h))[ii],
sep = " ")
}
formulatemp <- as.formula(paste("ytemp ~ ", terms))
covs_h <- model.matrix(formulatemp, data)
makeXmat = function(Xvals) {
p = dim(Xvals)[[2]]
Xout = matrix(0, n * q, p * q)
Xout[seq(1, n * q, q), 1:p] = Xvals
Xout[seq(2, n * q, q), (p + 1):(2 * p)] = Xvals
return(Xout)
}
X = makeXmat(covs)
p = ncol(X)
X_h = covs_h
p_h = ncol(X_h) + 1
y0 = rep(NA, n)
y1 = rep(NA, n)
y0[data$a == 0] = data$y[data$a == 0]
y1[data$a == 1] = data$y[data$a == 1]
Y = rep(-999, n * q)
Y[seq(1, n * q, q)] = y0
Y[seq(2, n * q, q)] = y1
ismissy = (is.na(Y))
nwithmissing = sum(ismissy)
H = rep(-999, n * 2)
H[seq(1, n * 2, 2)][data$a == 0] = data$h[data$a == 0]
H[seq(2, n * 2, 2)][data$a == 1] = data$h[data$a == 1]
denom = data[, denom]
a = rep(NA, n * q)
a[seq(1, n * q, q)] = data$a
a[seq(2, n * q, q)] = data$a
psiig_a = prior$psi
psiig_b = prior$psi
my0 = summary(lm(y0 ~ data$ps))
my1 = summary(lm(y1 ~ data$ps))
missy1 = ismissy[seq(1, n * q, q)]
missy2 = ismissy[seq(2, n * q, q)]
missboth = rep(FALSE, n)
missboth[is.na(Y[seq(1, n * q, q)]) & is.na(Y[seq(2, n *
q, q)])] = TRUE
Y[seq(1, n * q, q)][missboth == TRUE] = rnorm(sum(missboth ==
TRUE), data$base[missboth == TRUE], 0.2 * sd(dat$y, na.rm = TRUE))
Y[seq(2, n * q, q)][missy2 == 1] = rtnorm(sum(missy2 == 1),
mean(Y[seq(2, n * q, q)], na.rm = T), 0.2 * sd(Y[seq(2,
n * 1, q)], na.rm = T), lower = -Inf, upper = Y[seq(1,
n * q, q)][missy2 == 1])
Y[seq(1, n * q, q)][missy1 == 1] = rtnorm(sum(missy1 == 1),
mean(Y[seq(1, n * q, q)], na.rm = T), 0.2 * sd(Y[seq(1,
n * q, q)], na.rm = T), lower = Y[seq(2, n * q, q)][missy1 ==
1], upper = Inf)
H[seq(1, n * 2, 2)][data$a == 1] = rpois(sum(data$a == 1),
10)
H[seq(2, n * 2, 2)][data$a == 0] = rpois(sum(data$a == 0),
10)
if (is.null(starting$B) == F) {
B = starting$B
}
if (is.null(starting$B) == T) {
B = rep(0, ncov * 2)
}
if (is.null(starting$psi) == T) {
initpsis = c(my0$sigma^2, my1$sigma^2)
}
else {
initpsis = starting$psi
}
if (is.null(starting$alpha0) == T) {
initalpha = rep(0, 2 * p_h)
}
else {
initalpha = c(starting$alpha0, starting$alpha1)
}
psipropsds = tuning$psi
alpha0propcov = tuning$alpha0
alpha1propcov = tuning$alpha1
B0propcov = tuning$B0
B1propcov = tuning$B1
propysds = tuning$Y
loglike_norm_monoind = function(Yvals, Bvals, psivals) {
lowertrunc = rep(NA, n * q)
uppertrunc = lowertrunc
lowertrunc[seq(1, n * q, q)] = Yvals[seq(2, n * q, q)]
uppertrunc[seq(1, n * q, q)] = Inf
uppertrunc[seq(2, n * q, q)] = Yvals[seq(1, n * q, q)]
lowertrunc[seq(2, n * q, q)] = -Inf
m = as.vector(X %*% Bvals)
index = seq(1, n * q, q)
llike = sum(dtnorm(Yvals[index], mean = m[index], sd = sqrt(psivals[1]),
lower = lowertrunc[index], upper = uppertrunc[index],
log = TRUE))
index = seq(2, n * q, q)
llike = llike + sum(dtnorm(Yvals[index], mean = m[index],
sd = sqrt(psivals[2]), lower = lowertrunc[index],
upper = uppertrunc[index], log = TRUE))
llike = llike + log(dinvgamma(psivals[1], psiig_a[1],
psiig_b[1])) + log(dinvgamma(psivals[2], psiig_a[2],
psiig_b[2]))
return(llike)
}
loglike_pois = function(Yvals, alphavals, h0vals, h1vals) {
ya0 = Yvals[seq(1, n * q, q)]
ya1 = Yvals[seq(2, n * q, q)]
Xmat0 = as.matrix(cbind(X_h, ya0))
alpha0 = alphavals[1:p_h]
lambda0 = exp(Xmat0 %*% alpha0 + log(denom))
Xmat1 = as.matrix(cbind(X_h, ya1))
alpha1 = alphavals[(p_h + 1):length(alphavals)]
lambda1 = exp(Xmat1 %*% alpha1 + log(denom))
llike = sum(h0vals * log(lambda0) - lambda0 - lfactorial(h0vals))
llike = llike + sum(h1vals * log(lambda1) - lambda1 -
lfactorial(h1vals))
return(llike)
}
MHstep_pois = function(whichalphas, Yvals, alphavals, h0vals,
h1vals, currentll) {
accept = 0
llret = currentll
alpharet = alphavals
alphaprop = alphavals
index = 1:p_h
if (whichalphas == 1) {
index = (p_h + 1):length(alphavals)
}
alphaprop[index] = mvrnorm(1, alpharet[index], alphapropcovs[[whichalphas +
1]])
llprop = loglike_pois(Yvals, alphaprop, h0vals, h1vals)
ratio = exp(llprop - currentll)
ratio[ratio > 1] = 1
if (runif(1) <= ratio) {
alpharet = alphaprop
llret = llprop
accept = 1
}
outlist = list(alpharet, llret, accept)
names(outlist) = c("alpha", "ll", "accepted")
return(outlist)
}
MHstep_B = function(Yvals, Bvals, psivals, currentll) {
accept = 0
llret = currentll
Bret = Bvals
Bprop = Bvals
Bprop = mvrnorm(1, Bret, Bpropcov)
llprop = loglike_norm_monoind(Yvals, Bprop, psivals)
ratio = exp(llprop - currentll)
if (runif(1) <= ratio) {
Bret = Bprop
llret = llprop
accept = 1
}
outlist = list(Bret, llret, accept)
names(outlist) = c("B", "ll", "accepted")
return(outlist)
}
MHstep_psi = function(whichpsi, Yvals, Bvals, psivals, currentll) {
accept = 0
llret = currentll
psiret = psivals
psiprop = psivals
psiprop[whichpsi + 1] = rnorm(1, psiret[whichpsi + 1],
psipropsds[whichpsi + 1])
llprop = loglike_norm_monoind(Yvals, Bvals, psiprop)
ratio = exp(llprop - currentll)
if (runif(1) <= ratio) {
psiret = psiprop
llret = llprop
accept = 1
}
outlist = list(psiret, llret, accept)
names(outlist) = c("psi", "ll", "accepted")
return(outlist)
}
sampleH = function(whicha, alphavals, Yvals) {
ya0 = Yvals[seq(1, n * q, q)]
ya1 = Yvals[seq(2, n * q, q)]
index = 1:p_h
Xmat = cbind(X_h, ya0)
if (whicha == 1) {
index = (p_h + 1):length(alphavals)
Xmat = cbind(X_h, ya1)
}
alphs = alphavals[index]
lamda = exp(Xmat %*% alphs + log(denom))
h = rpois(sum(data$a == (1 - whicha)), lamda[data$a ==
(1 - whicha)])
return(h)
}
sampleY_monoind = function(ids, Yvals, Bvals, psivals, alphavals,
h0vals, h1vals, currentllpois, currentllnorm) {
accept = rep(0, length(which(ids)))
currentll = currentllnorm + currentllpois
llretnorm = currentllnorm
llretpois = currentllpois
Yret = Yvals
Yprop = Yvals
lowertrunc = rep(NA, n * q)
uppertrunc = lowertrunc
lowertrunc[seq(1, n * q, q)] = Yvals[seq(2, n * q, q)]
uppertrunc[seq(1, n * q, q)] = Inf
uppertrunc[seq(2, n * q, q)] = Yvals[seq(1, n * q, q)]
lowertrunc[seq(2, n * q, q)] = -Inf
Yprop[ids] = rtnorm(length(which(ids)), Yvals[ids], propysds[ids],
lower = lowertrunc[ids], upper = uppertrunc[ids])
llpropnorm = loglike_norm_monoind(Yprop, Bvals, psivals)
llproppois = loglike_pois(Yprop, alphavals, h0vals, h1vals)
propdens_prop = dtnorm(Yprop[ids], Yvals[ids], propysds[ids],
lower = lowertrunc[ids], upper = uppertrunc[ids],
log = TRUE)
propdens_current = dtnorm(Yvals[ids], Yprop[ids], propysds[ids],
lower = lowertrunc[ids], upper = uppertrunc[ids],
log = TRUE)
llprop = llpropnorm + llproppois
ratio = exp(llprop - currentll + sum(propdens_current -
propdens_prop))
ratio[ratio > 1] = 1
if (runif(1) <= ratio) {
Yret = Yprop
llretnorm = llpropnorm
llretpois = llproppois
accept = rep(1, length(which(ids)))
}
outlist = list(Yret, llretnorm, llretpois, accept)
names(outlist) = c("Y", "llnorm", "llpois", "accepted")
return(outlist)
}
params = c(initpsis, initalpha)
nparams = length(params)
propsds = c(psipropsds)
alphapropcovs = list(alpha0propcov, alpha1propcov)
Bpropcov = matrix(0, p, p)
Bpropcov[1:(p/2), 1:(p/2)] = B0propcov
Bpropcov[((p/2) + 1):p, ((p/2) + 1):p] = B1propcov
psiindex = 1:2
alpha0index = 3:(2 + p_h)
alpha1index = (max(alpha0index) + 1):(max(alpha0index) +
p_h)
accepted = rep(0, nparams)
accepted_y = rep(0, n * q)
accepted_B = rep(0, p)
binsize = 10
rho = 0
notpd = 0
samples = matrix(NA, nrow = length(index), ncol = nparams +
length(B))
dimnames(samples)[[2]] = c(paste("Psi", 1:q, sep = ""), paste("alpha0",
(0:(p_h - 1)), sep = ""), paste("alpha1", (0:(p_h - 1)),
sep = ""), paste("B0", 0:((p/2) - 1), sep = ""), paste("B1",
0:((p/2) - 1), sep = ""))
ysims = matrix(NA, nrow = length(index), ncol = length(Y))
hsims = matrix(NA, nrow = length(index), ncol = length(H))
llnorm = loglike_norm_monoind(Yvals = Y, Bvals = B, psivals = params[psiindex])
llpois = loglike_pois(Y, params[c(alpha0index, alpha1index)],
H[seq(1, n * 2, 2)], H[seq(2, n * 2, 2)])
ll = llnorm + llpois
iterno = 1
donesampling = FALSE
kk = 1
while (donesampling == FALSE) {
mhstep = MHstep_B(Y, B, params[psiindex], llnorm)
B = mhstep$B
llnorm = mhstep$ll
accepted_B = accepted_B + mhstep$accepted
if (llnorm == Inf)
print(paste("Infinite Likelihood while/after updating B at iterno=",
iterno))
ll = llnorm + llpois
mhstep = MHstep_psi(whichpsi = 0, Yvals = Y, Bvals = B,
psivals = params[psiindex], currentll = llnorm)
params[psiindex] = mhstep$psi
llnorm = mhstep$ll
accepted[psiindex[1]] = accepted[psiindex[1]] + mhstep$accepted
if (llnorm == Inf)
print(paste("Infinite Likelihood while/after updating Psi0 at iterno=",
iterno))
mhstep = MHstep_psi(1, Y, B, params[psiindex], llnorm)
params[psiindex] = mhstep$psi
llnorm = mhstep$ll
accepted[psiindex[2]] = accepted[psiindex[2]] + mhstep$accepted
if (llnorm == Inf)
print(paste("Infinite Likelihood while/after updating Psi1 at iterno=",
iterno))
ll = llnorm + llpois
mhstep = MHstep_pois(0, Y, params[c(alpha0index, alpha1index)],
H[seq(1, 2 * n, 2)], H[seq(2, 2 * n, 2)], llpois)
params[c(alpha0index, alpha1index)] = mhstep$alpha
llpois = mhstep$ll
accepted[alpha0index] = accepted[alpha0index] + mhstep$accepted
mhstep = MHstep_pois(1, Y, params[c(alpha0index, alpha1index)],
H[seq(1, 2 * n, 2)], H[seq(2, 2 * n, 2)], llpois)
params[c(alpha0index, alpha1index)] = mhstep$alpha
llpois = mhstep$ll
accepted[alpha1index] = accepted[alpha1index] + mhstep$accepted
for (whichysamp in which(ismissy)) {
whichy = rep(FALSE, n * q)
whichy[whichysamp] = TRUE
mhstep = sampleY_monoind(whichy, Y, B, params[psiindex],
params[c(alpha0index, alpha1index)], H[seq(1,
n * 2, 2)], H[seq(2, n * 2, 2)], llpois, llnorm)
Y = mhstep$Y
llnorm = mhstep$llnorm
llpois = mhstep$llpois
accepted_y[whichy] = accepted_y[whichy] + mhstep$accepted
}
H[seq(1, 2 * n, 2)][data$a == 1] = sampleH(0, params[c(alpha0index,
alpha1index)], Y)
H[seq(2, 2 * n, 2)][data$a == 0] = sampleH(1, params[c(alpha0index,
alpha1index)], Y)
llpois = loglike_pois(Y, params[c(alpha0index, alpha1index)],
H[seq(1, n * 2, 2)], H[seq(2, n * q, 2)])
ll = llnorm + llpois
if (iterno %in% index) {
samples[kk, ] = c(params[psiindex], params[c(alpha0index,
alpha1index)], B)
ysims[kk, ] = Y
hsims[kk, ] = H
kk <- kk + 1
}
if (iterno%%binsize == 0) {
print(paste("Iteration:", iterno))
print(c("Accepted:", round(accepted/iterno, 3), round(mean(accepted_y[ismissy]/iterno),
3)))
}
if (iterno >= nsamp) {
donesampling = TRUE
}
iterno = iterno + 1
}
out <- list()
out$samples <- samples
out$y0 <- ysims[, seq(1, n * q, q)]
out$y1 <- ysims[, seq(2, n * q, q)]
out$h0 <- hsims[, seq(1, n * 2, 2)]
out$h1 <- hsims[, seq(2, n * 2, 2)]
out$trt <- data$a
out$formula <- formula
out$formula_h <- formula_h
return(out)
}
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