R Under development (unstable) (2024-09-17 r87161 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> 
> library("tram")
Loading required package: mlt
Loading required package: basefun
Loading required package: variables
Loading required package: mvtnorm
> library("sandwich")
> set.seed(29)
> 
> chk <- function(x, y, tol = 1e-3) 
+     stopifnot(isTRUE(all.equal(x, y, check.attributes = FALSE, tol = tol)))
> 
> if (require("mlbench")) {
+ 
+   data("BostonHousing2", package = "mlbench")
+ 
+   m <- Lm(cmedv ~ nox, data = BostonHousing2)
+ 
+   d <- predict(as.mlt(m), type = "density")
+   ll <- sum(log(d))
+   chk(c(logLik(m)), ll)
+   d1 <- predict(as.mlt(m), newdata = BostonHousing2[1:6,], type = "density")
+   d2 <- diag(predict(as.mlt(m), newdata = BostonHousing2[1:6,-6], q =
+              BostonHousing2[1:6,6], type = "density"))
+   chk(d1, d2)
+   prb <- 1:9 / 10
+   q <- predict(as.mlt(m), newdata = BostonHousing2[1:2,-6], prob = prb,
+                type = "quantile")
+   p1 <- round(predict(as.mlt(m), newdata = BostonHousing2[1:2,-6], q = q[,1], 
+                       type = "distribution")[,1], 2)
+   p2 <- round(predict(as.mlt(m), newdata = BostonHousing2[1:2,-6], q = q[,2], 
+                       type = "distribution")[,2], 2)
+   chk(p1, prb)
+   chk(p2, prb)
+ 
+   m <- Lm(cmedv ~ nox | rm, data = BostonHousing2)
+ 
+   d <- predict(as.mlt(m), type = "density")
+   ll <- sum(log(d))
+   chk(c(logLik(m)), ll)
+   d1 <- predict(as.mlt(m), newdata = BostonHousing2[1:6,], type = "density")
+   d2 <- diag(predict(as.mlt(m), newdata = BostonHousing2[1:6,-6], q =
+              BostonHousing2[1:6,6], type = "density"))
+   chk(d1, d2)
+   prb <- 1:9 / 10
+   q <- predict(as.mlt(m), newdata = BostonHousing2[1:2,-6], prob = prb,
+                type = "quantile")
+   p1 <- round(predict(as.mlt(m), newdata = BostonHousing2[1:2,-6], q = q[,1], 
+                       type = "distribution")[,1], 2)
+   p2 <- round(predict(as.mlt(m), newdata = BostonHousing2[1:2,-6], q = q[,2], 
+                       type = "distribution")[,2], 2)
+   chk(p1, prb)
+   chk(p2, prb)
+ 
+ }
Loading required package: mlbench
> 
> if (suppressPackageStartupMessages(require("gamlss"))) {
+ 
+   N <- 1000
+   x1 <- rnorm(N)
+   x2 <- rnorm(N)
+   y <- rnorm(N, mean = 2 * x1, sd = 2 * sqrt(exp(.5 * x2)))
+   d <- data.frame(y = y, x1 = x1, x2 = x2)
+ 
+   ### shift-scale transformation model
+   tm <- Lm(y ~ x1 | x2, data = d, scale_shift = TRUE)
+ 
+   ### same model, via gamlss
+   gm <- gamlss(y ~ x1, sigma.formula = ~ x2, data = d, family = NO2, trace = FALSE)
+   chk(logLik(gm), logLik(tm))
+   chk(-coef(tm)["scl_x2"], coefAll(gm)[[2]]["x2"])
+ 
+   X1 <- model.matrix(~ x1, data = d)
+   X2 <- model.matrix(~ x2, data = d)
+ 
+   #### logLik gamlss
+   glp1 <- X1 %*% coefAll(gm)[[1]]
+   glp2 <- X2 %*% coefAll(gm)[[2]]
+   chk(c(logLik(gm)), 
+       sum(dnorm(y, mean = glp1, sd = sqrt(exp(glp2)), log = TRUE)))
+ 
+   ### shift-scale transformation model
+   Y <- model.matrix(~ y, data = d)
+   h0 <- Y %*% coef(as.mlt(tm))[1:2]
+   cf <- coef(tm)
+   tlp1 <- X1[,-1,drop = FALSE] %*% cf[-grep("scl", names(cf))]
+   tlp2 <- X2[,-1,drop = FALSE] %*% cf[grep("scl", names(cf))]
+   st <- exp(tlp2)
+   xi <- coef(as.mlt(tm))["y"]
+ 
+   ### transformation function
+   chk(c(sqrt(st) * (h0 - tlp1)), tr <- predict(tm, type = "trafo"))
+ 
+   ### logLik
+   chk(c(logLik(tm)), 
+       sum(log(1 / sqrt(2 * pi)) + .5 * tlp2 + log(xi) - 1 / 2 * tr^2))
+ 
+   ### score function
+   # score alpha
+   sa <- sum(-sqrt(st) * tr)
+   # score xi
+   sx <- sum(1 / xi - sqrt(st) * tr * y)
+   # score beta
+   sb <- sum(sqrt(st) * tr * X1[,-1,drop = FALSE])
+   # score gamma
+   sg <- sum(1 / 2 * (1 - tr^2) * X2[,-1, drop = FALSE])
+ 
+   chk(c(sa, sx, sb, sg), -Gradient(tm))
+ 
+   ### Hessian
+   # alpha, alpha
+   Haa <- sum(-st)
+   # xi, xi
+   Hxx <- sum(- xi^(-2) - st * y^2)
+   # beta, beta
+   Htm <- crossprod(-st * X1[, -1, drop = FALSE], X1[, -1, drop = FALSE])
+   # gamma, gamma
+   Hgg <- crossprod(-1 / 2 * tr^2 * X2[, -1, drop = FALSE], X2[, -1, drop = FALSE])
+   # alpha, xi
+   Hax <- sum(-st * y)
+   # alpha, beta
+   Hab <- colSums(st * X1[, -1, drop = FALSE])
+   # alpha, gamma
+   Hag <- colSums(- sqrt(st) * tr * X2[, -1, drop = FALSE])
+   # beta, xi
+   Hbx <- colSums(st * y * X1[, -1, drop = FALSE])
+   # beta, gamma
+   Hbg <- crossprod(sqrt(st) * tr * X1[, -1, drop = FALSE], X2[, -1, drop = FALSE])
+   # xi, gamma
+   Hxg <- colSums(- sqrt(st) * tr * y * X2[, -1, drop = FALSE])
+   H <- diag(c(Haa, Hxx, Htm, Hgg))
+   H[1, 2] <- H[2, 1] <- Hax
+   H[1, 3] <- H[3, 1] <- Hab
+   H[1, 4] <- H[4, 1] <- Hag
+   H[2, 3] <- H[3, 2] <- Hbx
+   H[2, 4] <- H[4, 2] <- Hxg
+   H[3, 4] <- H[4, 3] <- Hbg
+ 
+   chk(-H, Hessian(as.mlt(tm)))
+ 
+   ### scale terms are defined analoguously in Lm and gamlss(NO2)
+   ### compare z statistics
+   chk(-coef(tm)["scl_x2"] / sqrt(vcov(tm)["scl_x2", "scl_x2"]),
+       coefAll(gm)[[2]]["x2"] / sqrt(vcov(gm)["x2", "x2"]))
+ }
> 
> ### predict
> 
> N <- 10000
> x1 <- runif(N)
> x2 <- runif(N)
> s <- gl(k <- 11, N/k)
> y <- rnorm(N, mean = 2 * x1, sd = sqrt(exp(.5 * x2)))
> d <- data.frame(y = y, x1 = x1, x2 = x2, s = s)
> 
> fm <- formula(y ~ s + x1)
> m <- Coxph(fm, data = d)
> try(a <- predict(m, type = "distribution")) ### works
> 
> sfm <- as.formula(y ~ s | x1)
> sm <- Coxph(sfm, data = d)
> try(b <- predict(sm, type = "distribution")) ### didn't work
> 
> ### check discrete models
> data("wine", package = "ordinal")
> m <- Polr(rating ~ temp | contact, data = wine, 
+           optim = mltoptim(spg = list(maxit = 10000, checkGrad = TRUE)))
> 
> ### inference methods & residuals
> N <- 1000
> x1 <- runif(N)
> x2 <- runif(N)
> z <- rnorm(N)
> y <- (z + .2 * x1) * sqrt(exp(.5 * x2))
> d <- data.frame(y = y, x1 = x1, x2 = x2, one = 1)
> 
> ### Wald, Score and Likelihood intervals
> m2 <- Lm(y ~ x1 | x2, data = d)
> ciW <- confint(m2)
> ciL <- confint(profile(m2))
> ciS <- rbind(score_test(m2, "x1")$conf,
+              score_test(m2, "scl_x2")$conf)
> 
> chk(ciW, ciL, tol = 1e-3)
> chk(ciW, ciS, tol = 1e-3)
> 
> ### not quite the same!
> round(ciW[1,], 3)
 2.5 % 97.5 % 
 0.162  0.592 
> round(perm_test(m2, "x1")$conf, 3)
[1] 0.166 0.589
attr(,"conf.level")
[1] 0.95
attr(,"achieved.conf.level")
[1] 0.9501
> 
> ### check residuals
> m0 <- Lm(y ~ 1, data = d)
> rs <- c(estfun(as.mlt(m0)) %*% coef(as.mlt(m0))) * .5
> rs2 <- resid(m0, what = "scaling")
> chk(rs, rs2)
> 
> ### residuals, nonparametrically
> N <- 50
> x1 <- runif(N)
> x2 <- runif(N)
> z <- rnorm(N)
> y <- (z + .2 * x1) * sqrt(exp(.5 * x2))
> d <- data.frame(y = y, x1 = x1, x2 = x2, one = 1)
> 
> d$yR <- R(y, as.R.ordered = TRUE)
> m0 <- Polr(yR ~ 1, data = d)
> rs <- c(estfun(as.mlt(m0)) %*% coef(as.mlt(m0))) * .5
> rs2 <- resid(m0, what = "scaling")
> chk(rs, rs2)
> 
> ### linear predictor
> aq <- airquality[complete.cases(airquality),]
> aq <- as.data.frame(lapply(aq, as.double))
> m <- Lm(Ozone ~ Solar.R + Wind + Temp + Month + Day | Solar.R + Wind + Temp + Month + Day, 
+         data = aq)
> 
> lp1 <- predict(m, type = "lp", what = "shifting")
> lp2 <- predict(m, type = "lp", what = "scaling")
> 
> X <- model.matrix(~ Solar.R + Wind + Temp + Month + Day, data = aq)[, -1L]
> stopifnot(identical(lp1, X %*% coef(m, with_baseline = FALSE)[m$shiftcoef]))
> stopifnot(identical(lp2, X %*% coef(m, with_baseline = FALSE)[m$scalecoef]))
> 
> proc.time()
   user  system elapsed 
   8.78    0.90    9.67