Last updated on 2024-03-28 09:51:36 CET.
Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
---|---|---|---|---|---|---|
r-devel-linux-x86_64-debian-clang | 1.1-3 | 11.14 | 161.21 | 172.35 | OK | |
r-devel-linux-x86_64-debian-gcc | 1.1-3 | 9.50 | 127.04 | 136.54 | OK | |
r-devel-linux-x86_64-fedora-clang | 1.1-3 | 213.01 | OK | |||
r-devel-linux-x86_64-fedora-gcc | 1.1-3 | 225.33 | OK | |||
r-devel-windows-x86_64 | 1.1-3 | 18.00 | 2098.00 | 2116.00 | ERROR | |
r-patched-linux-x86_64 | 1.1-3 | 14.01 | 160.81 | 174.82 | OK | |
r-release-linux-x86_64 | 1.1-3 | 11.72 | 160.10 | 171.82 | OK | |
r-release-macos-arm64 | 1.1-3 | 109.00 | OK | |||
r-release-macos-x86_64 | 1.1-3 | 223.00 | OK | |||
r-release-windows-x86_64 | 1.1-3 | 26.00 | 206.00 | 232.00 | OK | |
r-oldrel-macos-arm64 | 1.1-3 | 89.00 | OK | |||
r-oldrel-windows-x86_64 | 1.1-3 | 22.00 | 208.00 | 230.00 | OK |
Version: 1.1-3
Check: tests
Result: ERROR
Running 'aster.R' [1s]
Comparing 'aster.Rout' to 'aster.Rout.save' ... OK
Running 'bogus.R' [1s]
Comparing 'bogus.Rout' to 'bogus.Rout.save' ... OK
Running 'cache.R' [3s]
Comparing 'cache.Rout' to 'cache.Rout.save' ... OK
Running 'chk.R' [0s]
Comparing 'chk.Rout' to 'chk.Rout.save' ... OK
Running 'famber.R' [159s]
Running 'famnegbin.R' [158s]
Running 'famnorloc.R' [169s]
Running 'famnzp.R' [0s]
Comparing 'famnzp.Rout' to 'famnzp.Rout.save' ... OK
Running 'fampoi.R' [0s]
Comparing 'fampoi.Rout' to 'fampoi.Rout.save' ... OK
Running 'famtnb.R' [0s]
Comparing 'famtnb.Rout' to 'famtnb.Rout.save' ... OK
Running 'foobar.R' [0s]
Comparing 'foobar.Rout' to 'foobar.Rout.save' ... OK
Running 'formula.R' [0s]
Comparing 'formula.Rout' to 'formula.Rout.save' ... OK
Running 'gradmat.R' [158s]
Running 'ktnb.R' [164s]
Running 'ktp.R' [0s]
Comparing 'ktp.Rout' to 'ktp.Rout.save' ... OK
Running 'matops.R' [0s]
Comparing 'matops.Rout' to 'matops.Rout.save' ... OK
Running 'mlogl-c-export.R' [161s]
Running 'mlogl-cond.R' [169s]
Running 'mlogl-unco.R' [153s]
Running 'newfam.R' [0s]
Comparing 'newfam.Rout' to 'newfam.Rout.save' ... OK
Running 'newpickle.R' [147s]
Running 'nzp.R' [0s]
Comparing 'nzp.Rout' to 'nzp.Rout.save' ... OK
Running 'parm.R' [158s]
Running 'penmlogl.R' [1s]
Comparing 'penmlogl.Rout' to 'penmlogl.Rout.save' ... OK
Running 'pickle.R' [3s]
Comparing 'pickle.Rout' to 'pickle.Rout.save' ... OK
Running 'predict.R' [1s]
Comparing 'predict.Rout' to 'predict.Rout.save' ... OK
Running 'quickle.R' [164s]
Running 'raster.R' [163s]
Running 'reaster-warn.R' [2s]
Comparing 'reaster-warn.Rout' to 'reaster-warn.Rout.save' ... OK
Running 'reaster.R' [7s]
Comparing 'reaster.Rout' to 'reaster.Rout.save' ... OK
Running 'reaster1.R' [1s]
Comparing 'reaster1.Rout' to 'reaster1.Rout.save' ... OK
Running 'trans.R' [0s]
Comparing 'trans.Rout' to 'trans.Rout.save' ... OK
Running the tests in 'tests/famber.R' failed.
Complete output:
>
> library(aster)
>
> ifam <- fam.bernoulli()
>
> p <- seq(0.1, 0.9, 0.1)
> theta <- log(p) - log(1 - p)
>
> zeroth <- double(length(p))
> first <- double(length(p))
> second <- double(length(p))
>
> for (i in seq(along = p)) {
+ zeroth[i] <- famfun(ifam, 0, theta[i])
+ first[i] <- famfun(ifam, 1, theta[i])
+ second[i] <- famfun(ifam, 2, theta[i])
+ }
>
> all.equal(zeroth, - log(1 - p))
[1] TRUE
> all.equal(first, p)
[1] TRUE
> all.equal(second, p * (1 - p))
[1] TRUE
>
>
> proc.time()
user system elapsed
0.25 0.06 0.29
Running the tests in 'tests/famnegbin.R' failed.
Complete output:
>
> library(aster)
>
> alpha <- 2.222
> ifam <- fam.negative.binomial(alpha)
>
> ##### usual size theta #####
>
> p <- seq(0.9, 0.1, -0.1)
> theta <- log(1 - p)
> qq <- exp(theta)
> pp <- (- expm1(theta))
> all.equal(p, pp)
[1] TRUE
> all.equal(pp, 1 - qq)
[1] TRUE
>
> zeroth <- double(length(theta))
> first <- double(length(theta))
> second <- double(length(theta))
>
> for (i in seq(along = theta)) {
+ zeroth[i] <- famfun(ifam, 0, theta[i])
+ first[i] <- famfun(ifam, 1, theta[i])
+ second[i] <- famfun(ifam, 2, theta[i])
+ }
>
> all.equal(zeroth, alpha * (- log(pp)))
[1] TRUE
> all.equal(first, alpha * qq / pp)
[1] TRUE
> all.equal(second, alpha * qq / pp^2)
[1] TRUE
>
> ##### very large negative theta #####
>
> rm(p)
>
> theta <- seq(-100, -10, 10)
> qq <- exp(theta)
> pp <- (- expm1(theta))
>
> zeroth <- double(length(theta))
> first <- double(length(theta))
> second <- double(length(theta))
>
> for (i in seq(along = theta)) {
+ zeroth[i] <- famfun(ifam, 0, theta[i])
+ first[i] <- famfun(ifam, 1, theta[i])
+ second[i] <- famfun(ifam, 2, theta[i])
+ }
>
> all.equal(zeroth, alpha * (- log(pp)))
[1] TRUE
> all.equal(first, alpha * qq / pp)
[1] TRUE
> all.equal(second, alpha * qq / pp^2)
[1] TRUE
>
> ##### very small negative theta #####
>
> theta <- (- 10^(- c(1:9, seq(10, 100, 10))))
> qq <- exp(theta)
> pp <- (- expm1(theta))
>
> zeroth <- double(length(theta))
> first <- double(length(theta))
> second <- double(length(theta))
>
> for (i in seq(along = theta)) {
+ zeroth[i] <- famfun(ifam, 0, theta[i])
+ first[i] <- famfun(ifam, 1, theta[i])
+ second[i] <- famfun(ifam, 2, theta[i])
+ }
>
> all.equal(zeroth, alpha * (- log(pp)))
[1] TRUE
> all.equal(first, alpha * qq / pp)
[1] TRUE
> all.equal(second, alpha * qq / pp^2)
[1] TRUE
>
> ##### random #####
>
> nind <- 50
> theta <- rep(- 1.75, nind)
> pred <- 0
> fam <- 1
> root <- seq(1, by = 0.5, length = nind)
> theta <- cbind(theta)
> root <- cbind(root)
>
> set.seed(42)
> rout <- raster(theta, pred, fam, root, famlist = list(ifam))
>
> set.seed(42)
> rout.too <- rnbinom(nind, size = alpha * as.numeric(root),
+ prob = as.numeric(1 - exp(theta)))
>
> all.equal(as.numeric(rout), rout.too)
[1] TRUE
>
>
>
>
> proc.time()
user system elapsed
0.23 0.06 0.28
Running the tests in 'tests/famnorloc.R' failed.
Complete output:
>
> library(aster)
>
> sigma <- 2.222
> ifam <- fam.normal.location(sigma)
>
> ##### usual size theta #####
>
> theta <- seq(-3.0, 3.0, 0.1)
>
> zeroth <- double(length(theta))
> first <- double(length(theta))
> second <- double(length(theta))
>
> for (i in seq(along = theta)) {
+ zeroth[i] <- famfun(ifam, 0, theta[i])
+ first[i] <- famfun(ifam, 1, theta[i])
+ second[i] <- famfun(ifam, 2, theta[i])
+ }
>
> all.equal(zeroth, sigma^2 * theta^2 / 2)
[1] TRUE
> all.equal(first, sigma^2 * theta)
[1] TRUE
> all.equal(second, sigma^2 * theta^0)
[1] TRUE
>
> ##### random #####
>
> theta <- seq(-3.5, 3.5, 0.1)
> nind <- length(theta)
> pred <- 0
> fam <- 1
> root <- seq(1, 5, length = nind)
> theta <- cbind(theta)
> root <- cbind(root)
>
> set.seed(42)
> rout <- raster(theta, pred, fam, root, famlist = list(ifam))
>
> set.seed(42)
> moo <- sigma^2 * theta * as.numeric(root)
> cow <- sigma * sqrt(as.numeric(root))
> rout.too <- rnorm(nind, mean = moo, sd = cow)
>
> all.equal(as.numeric(rout), rout.too)
[1] TRUE
>
>
> proc.time()
user system elapsed
0.23 0.06 0.28
Running the tests in 'tests/gradmat.R' failed.
Complete output:
>
> library(aster)
>
> # needed because of the change in R function "sample" in R-devel
> suppressWarnings(RNGversion("3.5.2"))
>
> set.seed(42)
>
> nind <- 25
>
> vars <- c("l2", "l3", "f2", "f3", "h2", "h3")
> pred <- c(0, 1, 1, 2, 3, 4)
> fam <- c(1, 1, 1, 1, 3, 3)
> length(pred) == length(fam)
[1] TRUE
> nnode <- length(pred)
>
> theta <- matrix(0, nind, nnode)
> root <- matrix(1, nind, nnode)
> x <- raster(theta, pred, fam, root)
> dimnames(x) <- list(NULL, vars)
>
> data <- as.data.frame(x)
> site <- factor(sample(LETTERS[1:4], nind, replace = TRUE))
> foo <- rnorm(nind)
> data <- data.frame(x, site = site, foo = foo, root = 1)
>
> redata <- reshape(data, varying = list(vars),
+ direction = "long", timevar = "varb", times = as.factor(vars),
+ v.names = "resp")
>
> out <- aster(resp ~ foo + site + varb, pred, fam, varb, id, root,
+ data = redata)
> sout1 <- summary(out, show.graph = TRUE)
>
> out2 <- aster(x, root, pred, fam, modmat = out$modmat)
> sout2 <- summary(out2)
>
> out3 <- aster(x, root, pred, fam, modmat = out$modmat, type = "cond")
> sout3 <- summary(out3)
>
> foo <- new.env(parent = emptyenv())
> bar <- suppressWarnings(try(load("gradmat.rda", foo), silent = TRUE))
> if (inherits(bar, "try-error")) {
+ save(sout1, sout2, sout3, file = "gradmat.rda")
+ } else {
+ print(all.equal(sout1, foo$sout1))
+ print(all.equal(sout2, foo$sout2))
+ print(all.equal(sout3, foo$sout3))
+ }
[1] TRUE
[1] TRUE
[1] TRUE
>
> foo <- match(sort(unique(site)), site)
> modmat.pred <- out$modmat[foo, , ]
>
> ##### case 1: eta = theta, zeta = phi
>
> beta.hat <- out3$coef
>
> modmat.pred.mat <- matrix(modmat.pred, ncol = length(beta.hat))
>
> theta.hat <- matrix(modmat.pred.mat %*% beta.hat, nrow = dim(modmat.pred)[1])
>
> nind <- dim(modmat.pred)[1]
> nnode <- dim(modmat.pred)[2]
> ncoef <- dim(modmat.pred)[3]
>
> aster:::setfam(fam.default())
>
> phi.hat <- .C(aster:::C_aster_theta2phi,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ theta = as.double(theta.hat),
+ phi = matrix(as.double(0), nind, nnode))$phi
>
> my.phi.hat <- theta.hat
> my.phi.hat[ , 4] <- my.phi.hat[ , 4] - log(exp(exp(theta.hat[ , 6])) - 1)
> my.phi.hat[ , 3] <- my.phi.hat[ , 3] - log(exp(exp(theta.hat[ , 5])) - 1)
> my.phi.hat[ , 2] <- my.phi.hat[ , 2] - log(1 + exp(theta.hat[ , 4]))
> my.phi.hat[ , 1] <- my.phi.hat[ , 1] - log(1 + exp(theta.hat[ , 3]))
> my.phi.hat[ , 1] <- my.phi.hat[ , 1] - log(1 + exp(theta.hat[ , 2]))
> all.equal(my.phi.hat, phi.hat)
[1] TRUE
>
> gradmat <- 0 * modmat.pred
> storage.mode(gradmat) <- "double"
>
> gradmat <- .C(aster:::C_aster_D_beta2theta2phi,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ ncoef = as.integer(ncoef),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ theta = as.double(theta.hat),
+ modmat = as.double(modmat.pred),
+ gradmat = gradmat)$gradmat
>
> my.gradmat <- 0 * gradmat
> epsilon <- 1e-9
> for (k in 1:ncoef) {
+ beta.epsilon <- beta.hat
+ beta.epsilon[k] <- beta.hat[k] + epsilon
+ theta.epsilon <- matrix(modmat.pred.mat %*% beta.epsilon, nrow = nind)
+ phi.epsilon <- .C(aster:::C_aster_theta2phi,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ theta = as.double(theta.epsilon),
+ phi = matrix(as.double(0), nind, nnode))$phi
+ my.gradmat[ , , k] <- (phi.epsilon - phi.hat) / epsilon
+ }
>
> all.equal(gradmat, my.gradmat, tolerance = sqrt(epsilon))
[1] TRUE
>
> ##### case 2: eta = phi, zeta = theta
>
> beta.hat <- out2$coef
>
> phi.hat <- matrix(modmat.pred.mat %*% beta.hat, nrow = nind)
>
> theta.hat <- .C(aster:::C_aster_phi2theta,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ phi = as.double(phi.hat),
+ theta = matrix(as.double(0), nind, nnode))$theta
>
> gradmat <- .C(aster:::C_aster_D_beta2phi2theta,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ ncoef = as.integer(ncoef),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ theta = as.double(theta.hat),
+ modmat = as.double(modmat.pred),
+ gradmat = gradmat)$gradmat
>
> my.gradmat <- 0 * gradmat
> epsilon <- 1e-9
> for (k in 1:ncoef) {
+ beta.epsilon <- beta.hat
+ beta.epsilon[k] <- beta.hat[k] + epsilon
+ phi.epsilon <- matrix(modmat.pred.mat %*% beta.epsilon, nrow = nind)
+ theta.epsilon <- .C(aster:::C_aster_phi2theta,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ phi = as.double(phi.epsilon),
+ theta = matrix(as.double(0), nind, nnode))$theta
+ my.gradmat[ , , k] <- (theta.epsilon - theta.hat) / epsilon
+ }
>
> all.equal(gradmat, my.gradmat, tolerance = sqrt(epsilon))
[1] TRUE
>
> ##### case 3: eta = phi, zeta = tau
>
> root.pred <- matrix(1, nind, nnode)
>
> beta.hat <- out2$coef
>
> beta2tau <- function(beta) {
+
+ phi <- matrix(modmat.pred.mat %*% beta, nrow = nind)
+
+ theta <- .C(aster:::C_aster_phi2theta,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ phi = as.double(phi),
+ theta = matrix(as.double(0), nind, nnode))$theta
+
+ ctau <- .C(aster:::C_aster_theta2ctau,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ theta = as.double(theta),
+ ctau = double(nind * nnode))$ctau
+
+ tau <- .C(aster:::C_aster_ctau2tau,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ root = as.double(root.pred),
+ ctau = as.double(ctau),
+ tau = double(nind * nnode))$tau
+
+ return(tau)
+ }
>
> tau.hat <- beta2tau(beta.hat)
>
> gradmat <- .C(aster:::C_aster_D_beta2phi2tau,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ ncoef = as.integer(ncoef),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ beta = as.double(beta.hat),
+ root = as.double(root.pred),
+ origin = rep(as.double(0), nind * nnode),
+ modmat = as.double(modmat.pred),
+ gradmat = gradmat)$gradmat
>
> my.gradmat <- 0 * gradmat
> epsilon <- 1e-9
> for (k in 1:ncoef) {
+ beta.epsilon <- beta.hat
+ beta.epsilon[k] <- beta.hat[k] + epsilon
+ tau.epsilon <- beta2tau(beta.epsilon)
+ my.gradmat[ , , k] <- (tau.epsilon - tau.hat) / epsilon
+ }
>
> all.equal(gradmat, my.gradmat, tolerance = sqrt(epsilon))
[1] TRUE
>
> ##### case 4: eta = theta, zeta = tau
>
> beta.hat <- out3$coef
>
> beta2tau <- function(beta) {
+
+ theta <- matrix(modmat.pred.mat %*% beta, nrow = nind)
+
+ ctau <- .C(aster:::C_aster_theta2ctau,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ theta = as.double(theta),
+ ctau = double(nind * nnode))$ctau
+
+ tau <- .C(aster:::C_aster_ctau2tau,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ root = as.double(root.pred),
+ ctau = as.double(ctau),
+ tau = double(nind * nnode))$tau
+
+ return(tau)
+ }
>
> tau.hat <- beta2tau(beta.hat)
>
> gradmat <- .C(aster:::C_aster_D_beta2theta2tau,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ ncoef = as.integer(ncoef),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ beta = as.double(beta.hat),
+ root = as.double(root.pred),
+ modmat = as.double(modmat.pred),
+ gradmat = gradmat)$gradmat
>
> my.gradmat <- 0 * gradmat
> for (k in 1:ncoef) {
+ beta.epsilon <- beta.hat
+ beta.epsilon[k] <- beta.hat[k] + epsilon
+ tau.epsilon <- beta2tau(beta.epsilon)
+ my.gradmat[ , , k] <- (tau.epsilon - tau.hat) / epsilon
+ }
>
> all.equal(gradmat, my.gradmat, tolerance = sqrt(epsilon))
[1] TRUE
>
>
> proc.time()
user system elapsed
0.34 0.03 0.36
Running the tests in 'tests/ktnb.R' failed.
Complete output:
>
> library(aster)
>
> do.chisq.test <- function(x, alpha, k, mu, max.bin) {
+ stopifnot(all(x > k))
+ stopifnot(k + 1 < max.bin)
+ xx <- seq(k + 1, max.bin)
+ yy <- dnbinom(xx, size = alpha, mu = mu)
+ yy[length(yy)] <- pnbinom(max.bin - 1, size = alpha, mu = mu,
+ lower.tail = FALSE)
+ pp <- yy / sum(yy)
+ ecc <- length(x) * pp
+ if (any(ecc < 5.0))
+ warning("violates rule of thumb about > 5 expected in each cell")
+ cc <- tabulate(x, max.bin)
+ cc <- cc[xx]
+ cc[length(cc)] <- nsim - sum(cc[- length(cc)])
+ chisqstat <- sum((cc - ecc)^2 / ecc)
+ pval <- pchisq(chisqstat, length(ecc) - 1, lower.tail = FALSE)
+ if (exists("save.min.pval")) {
+ save.min.pval <<- min(pval, save.min.pval)
+ save.ntests <<- save.ntests + 1
+ } else {
+ save.min.pval <<- pval
+ save.ntests <<- 1
+ }
+ list(chisqstat = chisqstat, df = length(ecc) - 1, pval = pval,
+ observed = cc, expected = ecc, x = xx)
+ }
>
> set.seed(42)
> nsim <- 1e4
>
> alpha <- 2.222
> mu <- 10
> k <- 2
> x <- rktnb(nsim, alpha, k, mu)
> chisqout1 <- do.chisq.test(x, alpha, k, mu, 40)
>
> alpha <- 2.222
> mu <- 3.5
> k <- 2
> x <- rktnb(nsim, alpha, k, mu)
> chisqout2 <- do.chisq.test(x, alpha, k, mu, 20)
>
> alpha <- 2.222
> mu <- 2.5
> k <- 2
> x <- rktnb(nsim, alpha, k, mu)
> chisqout3 <- do.chisq.test(x, alpha, k, mu, 16)
>
> alpha <- 2.222
> mu <- 1.5
> k <- 2
> x <- rktnb(nsim, alpha, k, mu)
> chisqout4 <- do.chisq.test(x, alpha, k, mu, 12)
>
> alpha <- 2.222
> mu <- 0.5
> k <- 2
> x <- rktnb(nsim, alpha, k, mu)
> chisqout5 <- do.chisq.test(x, alpha, k, mu, 8)
>
> alpha <- 2.222
> mu <- 0.1
> k <- 2
> x <- rktnb(nsim, alpha, k, mu)
> chisqout6 <- do.chisq.test(x, alpha, k, mu, 5)
>
> nsim <- 2e5
> alpha <- 2.222
> mu <- 0.01
> k <- 2
> x <- rktnb(nsim, alpha, k, mu)
> chisqout7 <- do.chisq.test(x, alpha, k, mu, 5)
>
> alpha <- 2.222
> mu <- 1.5
> xpred <- 0:10
> save.seed <- .Random.seed
> x <- rktp(xpred, k, mu, xpred)
> .Random.seed <- save.seed
> my.x <- rep(0, length(xpred))
> for (i in seq(along = xpred))
+ if (xpred[i] > 0)
+ for (j in 1:xpred[i])
+ my.x[i] <- my.x[i] + rktp(1, k, mu)
> all.equal(x, my.x)
[1] TRUE
>
> nsim <- 1e4
> alpha <- 5.55
> k <- 5
> mu <- pi
> x <- rktnb(nsim, alpha, k, mu)
> chisqout8 <- do.chisq.test(x, alpha, k, mu, 16)
>
> alpha <- 5.55
> k <- 10
> mu <- exp(2)
> x <- rktnb(nsim, alpha, k, mu)
> chisqout9 <- do.chisq.test(x, alpha, k, mu, 29)
>
> cat("number of tests:", save.ntests, "\n")
number of tests: 9
> save.ntests * save.min.pval > 0.05
[1] TRUE
>
> #####
>
> set.seed(42)
> nind <- 25
> nnode <- 1
> ncoef <- 1
> alpha <- 3.333
> k <- 2
>
> pred <- 0
> fam <- 1
> ifam <- fam.truncated.negative.binomial(size = alpha, trunc = k)
> aster:::setfam(list(ifam))
> theta.origin <- aster:::getfam()[[1]]$origin
>
> theta <- (- 4 / 3)
> p <- 1 - exp(theta)
> x <- rnbinom(1000, size = alpha, prob = p)
> x <- x[x > k]
> x <- x[1:nind]
> modmat <- matrix(1, nrow = nind, ncol = 1)
>
> out <- mlogl(theta - theta.origin, pred, fam, x, modmat, modmat,
+ deriv = 2, type = "conditional", famlist = list(ifam))
>
> xxx <- seq(0, 100)
> ppp <- dnbinom(xxx, size = alpha, prob = p)
> ppp[xxx <= k] <- 0
> ppp <- ppp / sum(ppp)
> tau <- sum(xxx * ppp)
>
> my.grad.logl <- sum(x - tau)
> all.equal(- out$gradient, my.grad.logl)
[1] TRUE
>
> my.fish.info <- length(x) * sum((xxx - tau)^2 * ppp)
> all.equal(as.numeric(out$hessian), my.fish.info)
[1] TRUE
>
> foo <- new.env(parent = emptyenv())
> bar <- suppressWarnings(try(load("ktnb.rda", foo), silent = TRUE))
> if (inherits(bar, "try-error")) {
+ save(list = c(paste("chisqout", 1:9, sep = ""), "out"), file = "ktnb.rda")
+ } else {
+ print(all.equal(chisqout1, foo$chisqout1))
+ print(all.equal(chisqout2, foo$chisqout2))
+ print(all.equal(chisqout3, foo$chisqout3))
+ print(all.equal(chisqout4, foo$chisqout4))
+ print(all.equal(chisqout5, foo$chisqout5))
+ print(all.equal(chisqout6, foo$chisqout6))
+ print(all.equal(chisqout7, foo$chisqout7))
+ print(all.equal(chisqout8, foo$chisqout8))
+ print(all.equal(chisqout9, foo$chisqout9))
+ print(all.equal(out, foo$out))
+ }
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
>
>
> proc.time()
user system elapsed
0.37 0.09 0.42
Running the tests in 'tests/mlogl-c-export.R' failed.
Complete output:
>
> # copied from ../man/aster.Rd examples section
>
> library(aster)
> data(echinacea)
> vars <- c("ld02", "ld03", "ld04", "fl02", "fl03", "fl04",
+ "hdct02", "hdct03", "hdct04")
> redata <- reshape(echinacea, varying = list(vars), direction = "long",
+ timevar = "varb", times = as.factor(vars), v.names = "resp")
> redata <- data.frame(redata, root = 1)
> pred <- c(0, 1, 2, 1, 2, 3, 4, 5, 6)
> fam <- c(1, 1, 1, 1, 1, 1, 3, 3, 3)
> hdct <- grepl("hdct", as.character(redata$varb))
> redata <- data.frame(redata, hdct = as.integer(hdct))
> level <- gsub("[0-9]", "", as.character(redata$varb))
> redata <- data.frame(redata, level = as.factor(level))
> aout <- aster(resp ~ varb + level : (nsloc + ewloc) + hdct : pop,
+ pred, fam, varb, id, root, data = redata)
>
> # redo with new function
>
> theta <- predict(aout, parm.type = "canonical", model.type = "conditional")
> phi <- predict(aout, parm.type = "canonical", model.type = "unconditional")
>
> nind <- nrow(echinacea)
> nnode <- length(pred)
>
> aster:::setfam(fam.default())
>
> val.theta <- .C(aster:::C_aster_export_exerciser,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ parm = as.double(theta),
+ root = as.double(redata$root),
+ response = as.double(redata$resp),
+ is.unco = FALSE,
+ value = double(1))$value
>
> val.phi <- .C(aster:::C_aster_export_exerciser,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ parm = as.double(phi),
+ root = as.double(redata$root),
+ response = as.double(redata$resp),
+ is.unco = TRUE,
+ value = double(1))$value
>
> aster:::clearfam()
>
> all.equal(val.theta, aout$deviance / 2)
[1] TRUE
> all.equal(val.phi, aout$deviance / 2)
[1] TRUE
>
>
> proc.time()
user system elapsed
0.68 0.04 0.71
Running the tests in 'tests/mlogl-cond.R' failed.
Complete output:
>
> library(aster)
> library(numDeriv)
>
> # needed because of the change in R function "sample" in R-devel
> suppressWarnings(RNGversion("3.5.2"))
>
> set.seed(42)
>
> nind <- 25
> nnode <- 5
> ncoef <- nnode + 1
>
> famlist <- fam.default()
> fam <- c(1, 1, 2, 3, 3)
> pred <- c(0, 1, 1, 2, 3)
>
> modmat <- array(0, c(nind, nnode, ncoef))
> modmat[ , , 1] <- 1
> for (i in 2:nnode)
+ modmat[ , i, i] <- 1
> modmat[ , , ncoef] <- rnorm(nind * nnode)
>
> beta <- rnorm(ncoef) / 10
>
> theta <- matrix(modmat, ncol = ncoef) %*% beta
> theta <- matrix(theta, ncol = nnode)
>
> root <- sample(1:3, nind * nnode, replace = TRUE)
> root <- matrix(root, nind, nnode)
>
> x <- raster(theta, pred, fam, root)
>
> out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
+ type = "conditional")
>
> my.value <- 0
> for (j in 1:nnode) {
+ ifam <- fam[j]
+ k <- pred[j]
+ if (k > 0)
+ xpred <- x[ , k]
+ else
+ xpred <- root[ , j]
+ for (i in 1:nind)
+ my.value <- my.value -
+ sum(x[i, j] * theta[i, j] -
+ xpred[i] * famfun(famlist[[ifam]], 0, theta[i, j]))
+ }
> all.equal(out$value, my.value)
[1] TRUE
>
> foo <- function(beta) {
+ stopifnot(is.numeric(beta))
+ stopifnot(is.finite(beta))
+ mlogl(beta, pred, fam, x, root, modmat, type = "conditional")$value
+ }
>
> g <- grad(foo, beta)
> all.equal(g, out$gradient)
[1] TRUE
>
> h <- hessian(foo, beta)
> all.equal(h, out$hessian)
[1] TRUE
>
> ##########
>
> objfun <- function(beta) {
+ out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 1,
+ type = "conditional")
+ result <- out$value
+ attr(result, "gradient") <- out$gradient
+ return(result)
+ }
> out1 <- nlm(objfun, beta, fscale = nind)
>
> ##########
>
> objfun <- function(beta) {
+ out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
+ type = "conditional")
+ result <- out$value
+ attr(result, "gradient") <- out$gradient
+ attr(result, "hessian") <- out$hessian
+ return(result)
+ }
> out <- nlm(objfun, beta, fscale = nind)
> all.equal(out1$minimum, out$minimum)
[1] TRUE
> all.equal(out1$estimate, out$estimate, tolerance = 1e-4)
[1] TRUE
>
> ########## expected Fisher information ##########
>
> aster:::setfam(fam.default())
>
> fout <- .C(aster:::C_aster_fish_cond,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ ncoef = as.integer(ncoef),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ beta = as.double(out$estimate),
+ root = as.double(root),
+ x = as.double(x),
+ modmat = as.double(modmat),
+ fish = matrix(as.double(0), ncoef, ncoef))
>
> mout <- mlogl(out$estimate, pred, fam, x, root, modmat,
+ deriv = 2, type = "conditional")
>
> aster:::setfam(fam.default())
>
> theta <- .C(aster:::C_aster_mat_vec_mult,
+ nrow = as.integer(nind * nnode),
+ ncol = as.integer(ncoef),
+ a = as.double(modmat),
+ b = as.double(out$estimate),
+ c = matrix(as.double(0), nind, nnode))$c
> ctau <- .C(aster:::C_aster_theta2ctau,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ theta = as.double(theta),
+ ctau = matrix(as.double(0), nind, nnode))$ctau
> tau <- .C(aster:::C_aster_ctau2tau,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ root = as.double(root),
+ ctau = as.double(ctau),
+ tau = matrix(as.double(0), nind, nnode))$tau
> psiDoublePrime <- .C(aster:::C_aster_theta2whatsis,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ deriv = as.integer(2),
+ theta = as.double(theta),
+ result = matrix(as.double(0), nind, nnode))$result
>
> my.hessian <- theta * NaN
> my.fish <- theta * NaN
>
> for (i in 1:nind) {
+ for (j in 1:nnode) {
+ k <- pred[j]
+ if (k > 0) {
+ my.hessian[i, j] <- x[i, k] * psiDoublePrime[i, j]
+ my.fish[i, j] <- tau[i, k] * psiDoublePrime[i, j]
+ } else {
+ my.hessian[i, j] <- root[i, j] * psiDoublePrime[i, j]
+ my.fish[i, j] <- root[i, j] * psiDoublePrime[i, j]
+ }
+ }
+ }
>
> modmat <- matrix(as.double(modmat), ncol = ncoef)
> my.hessian <- as.numeric(my.hessian)
> my.fish <- as.numeric(my.fish)
> my.hessian <- t(modmat) %*% diag(my.hessian) %*% modmat
> my.fish <- t(modmat) %*% diag(my.fish) %*% modmat
>
> all.equal(my.hessian, mout$hessian)
[1] TRUE
> all.equal(my.fish, fout$fish)
[1] TRUE
>
>
> proc.time()
user system elapsed
0.48 0.07 0.50
Running the tests in 'tests/mlogl-unco.R' failed.
Complete output:
>
> library(aster)
> library(numDeriv)
>
> set.seed(42)
>
> # needed because of the change in R function "sample" in R-devel
> suppressWarnings(RNGversion("3.5.2"))
>
> nind <- 25
> nnode <- 5
> ncoef <- nnode + 1
>
> famlist <- fam.default()
> fam <- c(1, 1, 2, 3, 3)
> pred <- c(0, 1, 1, 2, 3)
>
> modmat <- array(0, c(nind, nnode, ncoef))
> modmat[ , , 1] <- 1
> for (i in 2:nnode)
+ modmat[ , i, i] <- 1
> modmat[ , , ncoef] <- rnorm(nind * nnode)
>
> beta <- rnorm(ncoef) / 10
>
> phi <- matrix(modmat, ncol = ncoef) %*% beta
> phi <- matrix(phi, ncol = nnode)
>
> aster:::setfam(fam.default())
>
> theta <- .C(aster:::C_aster_phi2theta,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ phi = as.double(phi),
+ theta = matrix(as.double(0), nind, nnode))$theta
>
> root <- sample(1:3, nind * nnode, replace = TRUE)
> root <- matrix(root, nind, nnode)
>
> x <- raster(theta, pred, fam, root)
>
> zip <- rep(0, nind * nnode)
>
> out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
+ type = "unco", origin = zip)
>
> aster:::setfam(fam.default())
>
> a <- .C(aster:::C_aster_theta2phi,
+ nind = as.integer(nind),
+ nnode = as.integer(nnode),
+ pred = as.integer(pred),
+ fam = as.integer(fam),
+ theta = as.double(zip),
+ phi = matrix(as.double(0), nind, nnode))$phi
>
> M <- matrix(modmat, ncol = ncoef)
>
> alpha <- as.numeric(lm(as.numeric(a) ~ 0 + M)$coefficients)
>
> out.too <- mlogl(beta - alpha, pred, fam, x, root, modmat, deriv = 2,
+ type = "unco")
> all.equal(out, out.too)
[1] TRUE
>
> beta.old <- beta
> beta <- beta - alpha
>
> my.value <- 0
> for (j in 1:nnode) {
+ ifam <- fam[j]
+ k <- pred[j]
+ if (k > 0)
+ xpred <- x[ , k]
+ else
+ xpred <- root[ , j]
+ for (i in 1:nind)
+ my.value <- my.value -
+ sum(x[i, j] * theta[i, j] -
+ xpred[i] * famfun(famlist[[ifam]], 0, theta[i, j]))
+ }
> all.equal(out$value, my.value)
[1] TRUE
>
> foo <- function(beta) {
+ stopifnot(is.numeric(beta))
+ stopifnot(is.finite(beta))
+ mlogl(beta, pred, fam, x, root, modmat, type = "unco")$value
+ }
>
> g <- grad(foo, beta)
> all.equal(g, out$gradient)
[1] TRUE
>
> h <- hessian(foo, beta)
> all.equal(h, out$hessian)
[1] TRUE
>
> ##########
>
> objfun <- function(beta) {
+ out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 1,
+ type = "unco")
+ result <- out$value
+ attr(result, "gradient") <- out$gradient
+ return(result)
+ }
> nout1 <- nlm(objfun, beta, fscale = nind)
Warning message:
In nlm(objfun, beta, fscale = nind) :
NA/Inf replaced by maximum positive value
> nout <- nlm(objfun, nout1$estimate, fscale = nind)
> all.equal(nout1$minimum, nout$minimum)
[1] TRUE
> all.equal(nout1$estimate, nout$estimate)
[1] TRUE
>
> beta.mle.new <- nout$estimate
> beta.mle.old <- beta.mle.new + alpha
> mout.new <- mlogl(beta.mle.new, pred, fam, x, root, modmat, deriv = 1,
+ type = "unco")
> mout.old <- mlogl(beta.mle.old, pred, fam, x, root, modmat, deriv = 1,
+ type = "unco", origin = zip)
> all.equal(mout.new, mout.old, tol = 1e-7)
[1] TRUE
>
> ##########
>
> objfun <- function(beta) {
+ out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
+ type = "unco")
+ result <- out$value
+ attr(result, "gradient") <- out$gradient
+ attr(result, "hessian") <- out$hessian
+ return(result)
+ }
> nout1 <- nlm(objfun, beta, fscale = nind)
> nout <- nlm(objfun, nout1$estimate, fscale = nind, iterlim = 1000)
> all.equal(nout1$minimum, nout$minimum, tol = 1e-4)
[1] TRUE
> all.equal(nout1$estimate, nout$estimate, tol = 2e-2)
[1] TRUE
>
> objfun.old <- function(beta) {
+ out <- mlogl(beta, pred, fam, x, root, modmat, deriv = 2,
+ type = "unco", origin = zip)
+ result <- out$value
+ attr(result, "gradient") <- out$gradient
+ attr(result, "hessian") <- out$hessian
+ return(result)
+ }
> nout.old <- nlm(objfun.old, beta.mle.old, fscale = nind, iterlim = 1000)
> all.equal(nout$minimum, nout.old$minimum)
[1] TRUE
> all.equal(nout$estimate, nout.old$estimate - alpha, tol = 1e-4)
[1] TRUE
>
>
> proc.time()
user system elapsed
0.40 0.10 0.46
Running the tests in 'tests/newpickle.R' failed.
Complete output:
>
> library(aster)
>
> data(radish)
>
> pred <- c(0,1,2)
> fam <- c(1,3,2)
>
> ### need object of type aster to supply to penmlogl and pickle
>
> aout <- aster(resp ~ varb + fit : (Site * Region + Block + Pop),
+ pred, fam, varb, id, root, data = radish)
>
> ### model matrices for fixed and random effects
>
> modmat.fix <- model.matrix(resp ~ varb + fit : (Site * Region),
+ data = radish)
> modmat.blk <- model.matrix(resp ~ 0 + fit:Block, data = radish)
> modmat.pop <- model.matrix(resp ~ 0 + fit:Pop, data = radish)
>
> rownames(modmat.fix) <- NULL
> rownames(modmat.blk) <- NULL
> rownames(modmat.pop) <- NULL
>
> idrop <- match(aout$dropped, colnames(modmat.fix))
> idrop <- idrop[! is.na(idrop)]
> modmat.fix <- modmat.fix[ , - idrop]
>
> nfix <- ncol(modmat.fix)
> nblk <- ncol(modmat.blk)
> npop <- ncol(modmat.pop)
>
> ### try newpickle
>
> sigma.start <- c(1, 1)
> alpha.start <- aout$coefficients[match(colnames(modmat.fix),
+ names(aout$coefficients))]
> cee.start <- rep(0, nblk + npop)
> alphaceesigma.start <- c(alpha.start, cee.start, sigma.start)
>
> foo <- newpickle(alphaceesigma.start, fixed = modmat.fix,
+ random = list(modmat.blk, modmat.pop), obj = aout)
> names(foo)
[1] "value"
>
>
> proc.time()
user system elapsed
0.37 0.14 0.50
Running the tests in 'tests/parm.R' failed.
Complete output:
>
> library(aster)
>
> data(echinacea)
>
> # cut and paste from help(predict.aster)
> vars <- c("ld02", "ld03", "ld04", "fl02", "fl03", "fl04",
+ "hdct02", "hdct03", "hdct04")
> redata <- reshape(echinacea, varying = list(vars), direction = "long",
+ timevar = "varb", times = as.factor(vars), v.names = "resp")
> redata <- data.frame(redata, root = 1)
> pred <- c(0, 1, 2, 1, 2, 3, 4, 5, 6)
> fam <- c(1, 1, 1, 1, 1, 1, 3, 3, 3)
> hdct <- grepl("hdct", as.character(redata$varb))
> redata <- data.frame(redata, hdct = as.integer(hdct))
> level <- gsub("[0-9]", "", as.character(redata$varb))
> redata <- data.frame(redata, level = as.factor(level))
> aout <- aster(resp ~ varb + level : (nsloc + ewloc) + hdct : pop,
+ pred, fam, varb, id, root, data = redata)
> # end of cut and paste from help(predict.aster)
>
> length(aout$dropped)
[1] 1
> # so would have had a problem with restart in version 1.0-3 and before
> aout <- aster(resp ~ varb + level : (nsloc + ewloc) + hdct : pop,
+ pred, fam, varb, id, root, data = redata, parm = aout$coefficients,
+ optout = TRUE)
> aout$optout$converged
[1] TRUE
> aout$optout$iterations == 1
[1] TRUE
>
>
> proc.time()
user system elapsed
0.95 0.12 1.06
Running the tests in 'tests/quickle.R' failed.
Complete output:
>
> library(aster)
>
> data(radish)
>
> pred <- c(0,1,2)
> fam <- c(1,3,2)
>
> ### try reaster
>
> rout <- reaster(resp ~ varb + fit : (Site * Region),
+ list(block = ~ 0 + fit : Block, pop = ~ 0 + fit : Pop),
+ pred, fam, varb, id, root, data = radish)
> srout <- summary(rout)
> names(rout)
[1] "obj" "fixed" "random" "dropped" "sigma"
[6] "nu" "c" "b" "alpha" "zwz"
[11] "response" "origin" "iterations" "counts" "deviance"
[16] "formula" "call"
>
> foo <- new.env(parent = emptyenv())
> bar <- suppressWarnings(try(load("quickle.rda", foo), silent = TRUE))
> if (inherits(bar, "try-error")) {
+ save(srout, file = "quickle.rda")
+ } else {
+ srout$object$iterations <- NULL
+ foo$srout$object$iterations <- NULL
+ print(all.equal(srout, foo$srout, tol = 1e-4))
+ }
[1] TRUE
>
> alpha.mle <- rout$alpha
> bee.mle <- rout$b
> nu.mle <- rout$sigma^2
> zwz.mle <- rout$zwz
> obj <- rout$obj
> fixed <- rout$fixed
> random <- rout$random
> alphanu.mle <- c(alpha.mle, nu.mle)
>
> qout <- quickle(alphanu.mle, bee.mle, fixed, random, obj,
+ zwz = zwz.mle, deriv = 2)
>
> objfun <- function(alphanu) quickle(alphanu, bee.mle, fixed, random,
+ obj, zwz = zwz.mle)$value
> gradfun <- function(alphanu) quickle(alphanu, bee.mle, fixed, random,
+ obj, zwz = zwz.mle, deriv = 1)$gradient
> oout <- optim(alphanu.mle, objfun, gradfun, method = "BFGS", hessian = TRUE)
> all.equal(qout$hessian, oout$hessian, check.attributes = FALSE,
+ tolerance = 0.002)
[1] TRUE
>
>
> proc.time()
user system elapsed
3.35 0.40 3.79
Running the tests in 'tests/raster.R' failed.
Complete output:
>
> library(aster)
>
> # needed because of the change in R function "sample" in R-devel
> suppressWarnings(RNGversion("3.5.2"))
>
> set.seed(42)
> nind <- 25
> nnode <- 5
>
> theta <- rnorm(nind * nnode) / 10
> theta <- matrix(theta, nind, nnode)
>
> root <- sample(1:3, nind * nnode, replace = TRUE)
> root <- matrix(root, nind, nnode)
>
> fam <- c(1, 1, 2, 3, 3)
> pred <- c(0, 1, 1, 2, 3)
> famnam <- sapply(fam.default(), as.character)
>
> save.seed <- .Random.seed
> x <- raster(theta, pred, fam, root)
>
> .Random.seed <- save.seed
> my.x <- NaN * x
>
> for (i in 1:nnode) {
+ ipred <- pred[i]
+ if (ipred == 0) {
+ xpred <- root[ , i]
+ } else {
+ xpred <- my.x[ , ipred]
+ }
+ if (famnam[fam[i]] == "bernoulli") {
+ p <- 1 / (1 + exp(- theta[ , i]))
+ xi <- rep(0, nind)
+ ii <- xpred > 0
+ xi[ii] <- rbinom(sum(ii), xpred[ii], p[ii])
+ my.x[ , i] <- xi
+ }
+ if (famnam[fam[i]] == "poisson") {
+ mu <- exp(theta[ , i])
+ xi <- rep(0, nind)
+ ii <- xpred > 0
+ xi[ii] <- rpois(sum(ii), xpred[ii] * mu[ii])
+ my.x[ , i] <- xi
+ }
+ if (famnam[fam[i]] == "truncated.poisson(truncation = 0)") {
+ mu <- exp(theta[ , i])
+ my.x[ , i] <- rnzp(nind, mu, xpred)
+ }
+ }
>
> all.equal(x, my.x)
[1] TRUE
>
>
> proc.time()
user system elapsed
0.21 0.09 0.29
Flavor: r-devel-windows-x86_64