R Under development (unstable) (2024-07-15 r86902 ucrt) -- "Unsuffered Consequences"
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>
> library("cotram")
Loading required package: tram
Loading required package: mlt
Loading required package: basefun
Loading required package: variables
Loading required package: mvtnorm
> library("survival")
>
> set.seed(25)
>
> ### some basic checks for predictions wrt discrete distributions
> n <- 50
> d <- data.frame(x1 = 0:n, x2 = sample(0:n) + 1, q = sample(0:n))
> mc3 <- cotram(q ~ x1 + x2, data = d, log_first = log_first <- FALSE)
>
> di <- d
> di$qleft <- with(di, q - 1L)
> di$qleft[di$qleft < 0] <- -Inf
> di$q <- with(di, Surv(qleft + log_first, q + log_first, type = "interval2"))
> m3 <- Colr(q ~ x1 + x2, data = di, support = mc3$support, bounds = mc3$bounds, log_first = log_first)
>
> .chk <- function(x)
+ stopifnot(max(abs(x), na.rm = TRUE) < sqrt(.Machine$double.eps))
>
> nd <- d
> nd$q <- NULL
> q <-0:(n+1)
>
> p <- predict(mc3, newdata = nd, q = q, type = "distribution")
> s <- predict(mc3, newdata = nd, q = q, type = "survivor")
>
> ## check discrete distribution & survivor function
> .chk(predict(mc3, newdata = nd, q = q, type = "distribution", log = TRUE) - log(p))
> .chk(predict(mc3, newdata = nd, q = q, type = "distribution", lower.tail = FALSE) - s)
> .chk(predict(mc3, newdata = nd, q = q, type = "distribution",
+ lower.tail = FALSE, log = TRUE) - log(s))
>
> ## check discrete odds function
> o <- predict(mc3, newdata = nd, q = q, type = "odds")
> .chk(o - p / s)
>
> ## check discrete density function
> dd <- predict(mc3, newdata = nd, q = q, type = "density")
> .chk(apply(p, 2, function(x) diff(c(0, x))) - dd)
>
> ## check discrete (cum)-hazard function
> h <- predict(mc3, newdata = nd, q = q , type = "hazard")
>
> .chk(dd / (1 - (p - dd)) - h)
>
> .chk(apply(h, 2, function(x) cumprod(1 - x)) - s)
>
> H <- predict(mc3, newdata = nd, q = q, type = "cumhazard")
>
> .chk(H + log(s))
>
>
> ## simple checks for predict of smooth functions (cotram vs tram)
> .check_spr <- function(mc, m) {
+ plus_one <- ifelse(type == "quantile", log_first, FALSE)
+ .chk(predict(mc, type = type, q = 1:50, newdata = model.frame(mc), smooth = TRUE, prob = 1:9/10) -
+ (predict(m, type = type, q = 1:50 + log_first, newdata = model.frame(mc), prob = 1:9/10) - plus_one))
+ }
>
> for (type in c("trafo", "distribution", "survivor", "density", "logdensity",
+ "hazard", "loghazard", "cumhazard", "quantile")) {
+ print(type)
+ .check_spr(mc3, m3)
+ }
[1] "trafo"
[1] "distribution"
[1] "survivor"
[1] "density"
[1] "logdensity"
[1] "hazard"
[1] "loghazard"
[1] "cumhazard"
[1] "quantile"
>
> ### unconditional model for visual checks
> y <- rpois(1000, lambda = 2)
>
> ## interval-censored response
> yleft <- y - 1L
> yleft[yleft < 0] <- -Inf
>
> ## quantiles & probabilities
> q <- 0:10
> yy <- seq(from = min(q), to = max(q), length.out = 50)
> pr <- 1:9/10
>
> ## simple checks for predict wrt to newdata / q (log_first)
> .check_prd <- function(mc) {
+ ## check q
+ stopifnot(all.equal(predict(mc, newdata = data.frame(q), type = "density"),
+ predict(mc, newdata = data.frame(1), q = q, type = "density")))
+
+ ## check q[1] density vs distribution
+ stopifnot(all.equal(predict(mc, newdata = data.frame(1), q = 0, type = "density"),
+ predict(mc, newdata = data.frame(1), q = 0, type = "distribution")))
+
+ ## rough check discrete density function
+ stopifnot(all.equal(predict(mc, newdata = data.frame(1), q = q, type = "density"),
+ predict(mc, newdata = data.frame(1), q = q, type = "distribution") -
+ c(0, predict(mc, newdata = data.frame(1), q = q[-1] - 1 , type = "distribution"))))
+
+ stopifnot(all.equal(predict(mc, newdata = data.frame(y = q), type = "density"),
+ predict(mc, newdata = data.frame(y = q), type = "distribution") -
+ c(0, predict(mc, newdata = data.frame(y = q[-1] - 1), type = "distribution"))))
+
+ }
>
> ## cotram: log_first = FALSE
> mc1 <- cotram(y ~ 1, log_first = FALSE, prob = prob <- .99, extrapolate = TRUE)
warning: solve(): system is singular (rcond: 8.56911e-18); attempting approx solution
>
> .check_prd(mc1)
>
> m1 <- Colr(yi ~ 1, data = data.frame(yi = Surv(yleft, y, type = "interval2")), extrapolate = TRUE,
+ log_first = FALSE, support = c(0, quantile(y, prob = prob)), bounds = c(-.01, Inf))
warning: solve(): system is singular (rcond: 8.56911e-18); attempting approx solution
>
> ## cotram: log_first = TRUE
> mc2 <- cotram(y ~ 1, log_first = TRUE, extrapolate = TRUE, prob = .99)
warning: solve(): system is singular (rcond: 1.77785e-17); attempting approx solution
Model was fitted to log(y + 1).
>
> .check_prd(mc2)
>
> ## works in package "tram" -- but should it?
> try(predict(mc1, type = "quantile", prob = pr, smooth = TRUE, q = 1:10))
Error in na.m | na.e : non-conformable arrays
>
> ## when problems with one newdata configuration, can't predict at all.
> ## (This comes from "tram")
> try(predict(mc, type = "quantile", prob = pr, smooth = TRUE))
Error : object 'mc' not found
>
> if (FALSE) {
+ layout(matrix(c(1:4), nrow = 2, byrow = TRUE))
+
+ main <- "predict - density: log_first = FALSE"
+ plot(q, predict(mc1, newdata = data.frame(1), q = q , type = "density"),
+ type = "h", main = main)
+ points(q, predict(mc1, newdata = data.frame(1), q = q, type = "density"), pch = 20)
+ points(q, dpois(q, lambda = 2), col = "blue")
+ lines(yy, predict(mc1, newdata = data.frame(1), q = yy, type = "density", smooth = TRUE), col = "grey")
+ lines(yy, predict(mc1, newdata = data.frame(y = yy), type = "density", smooth = TRUE),
+ col = "red")
+ plot(mc1, newdata = data.frame(1), type = "density", smooth = TRUE,
+ col = "red", add = TRUE)
+ plot(mc1, newdata = data.frame(1), type = "density",
+ col = "red", add = TRUE)
+
+ main <- "predict - distribution: log_first = FALSE"
+ plot(q, predict(mc1, newdata = data.frame(1), q = q, type = "distribution"),
+ type = "s", ylim = c(0, 1), main = main)
+ lines(yy, predict(mc1, newdata = data.frame(1), q = yy, type = "distribution", smooth = TRUE), col = "grey")
+ points(q, ppois(q, lambda = 2), col = "blue")
+ points(predict(mc1, newdata = data.frame(1), q = q, prob = pr, type = "quantile",
+ smooth = TRUE), pr, col = "darkgreen")
+ legend("bottomright", legend = c("ppois", "quantiles"), pch = 1,
+ col = c("blue", "green"))
+ plot(mc1, newdata = data.frame(1), type = "distribution",
+ col = "red", smooth = TRUE, add = TRUE)
+ plot(mc1, newdata = data.frame(1), q = q, type = "distribution",
+ col = "red", add = TRUE)
+
+ main <- "predict - density: log_first = TRUE"
+ plot(q, predict(mc2, newdata = data.frame(1), q = q, type = "density"),
+ type = "h", main = main)
+ points(q, predict(mc2, newdata = data.frame(1), q = q, type = "density"), pch = 20)
+ points(q, dpois(q, lambda = 2), col = "blue")
+ lines(yy, predict(mc2, newdata = data.frame(1), type = "density", q = yy, smooth = TRUE))
+ plot(mc2, newdata = data.frame(1), type = "density",
+ col = "red", smooth = TRUE, add = TRUE, ylim = c(0, .3))
+ plot(mc2, newdata = data.frame(1), q = q, type = "density",
+ col = "red", add = TRUE)
+
+ main <- "predict - distribution: log_first = TRUE"
+ plot(q, predict(mc2, newdata = data.frame(1), q = q, type = "distribution"),
+ type = "s", ylim = c(0, 1), main = main)
+ points(q, ppois(q, lambda = 2), col = "blue")
+ lines(yy, predict(mc2, newdata = data.frame(1), q = yy, type = "distribution", smooth = TRUE))
+ points(predict(mc2, newdata = data.frame(1),
+ smooth = TRUE, prob = pr, type = "quantile"), pr, col = "green")
+ legend("bottomright", legend = c("ppois", "quantiles"), pch = 1,
+ col = c("blue", "green"))
+ plot(mc2, newdata = data.frame(1), type = "distribution", q = 0:3,
+ col = "red", smooth = TRUE, add = TRUE)
+ plot(mc2, newdata = data.frame(1), type = "distribution",
+ col = "red", add = TRUE)
+
+ layout(matrix(c(1:6), nrow = 2, byrow = TRUE))
+ plot(mc3, newdata = nd, type = "density", col = rgb(.3, .3, .3, .3))
+ # plot(m3, newdata = nd, type = "density", col = 2, add = TRUE)
+ abline(v = 0)
+
+ plot(mc3, newdata = nd, type = "logdensity", ylim = c(-6, 0), col = rgb(.3, .3, .3, .3))
+ # plot(m3, newdata = nd, type = "logdensity", col = 2, add = TRUE, ylim = c(-6, 0))
+ abline(v = 0)
+
+ plot(mc3, newdata = nd, type = "distribution", col = rgb(.3, .3, .3, .3))
+ # plot(m3, newdata = nd, type = "distribution", col = 2, add = TRUE)
+ abline(v = 0)
+
+ plot(mc3, newdata = nd, type = "trafo", col = rgb(.3, .3, .3, .3))
+ # plot(m3, newdata = nd, type = "trafo", col = 2, add = TRUE)
+ abline(v = 0)
+
+ plot(mc3, newdata = nd, type = "survivor", col = rgb(.3, .3, .3, .3))
+ # plot(m3, newdata = nd, type = "survivor", col = 2, add = TRUE)
+ abline(v = 0)
+
+ plot(mc3, newdata = nd, type = "cumhazard", col = rgb(.3, .3, .3, .3))
+ # plot(m3, newdata = nd, type = "cumhazard", col = 2, add = TRUE)
+ abline(v = 0)
+ }
>
>
> proc.time()
user system elapsed
2.25 0.40 2.64