diff --git a/R/npde.R b/R/npde.R index 9511f6d..8aef5bf 100644 --- a/R/npde.R +++ b/R/npde.R @@ -22,7 +22,7 @@ #' \donttest{ #' require(magrittr) #' require(ggplot2) -#' +#' #' obs <- obs_data[MDV==0] #' sim <- sim_data[MDV==0] #' @@ -35,17 +35,21 @@ #' binning("eqcut", nbins=10) %>% #' vpcstats() #' -#' plot(vpc) + +#' plot(vpc) + #' labs(x="Simulation-based Population Prediction", y="Normalized Prediction Distribution Error") #' } #' #' @export -npde <- function(o, ...) UseMethod("npde") +npde <- function(o, ...) + UseMethod("npde") #' @rdname npde #' @export -npde.tidyvpcobj <- function(o, id, data=o$data, smooth=FALSE, ...) { - +npde.tidyvpcobj <- function(o, + id, + data = o$data, + smooth = FALSE, + ...) { obs <- rn <- y <- iter <- NULL if (missing(id)) { @@ -56,89 +60,96 @@ npde.tidyvpcobj <- function(o, id, data=o$data, smooth=FALSE, ...) { if (is.null(id)) { stop("No id specified") } - - niter <- nrow(o$sim)/nrow(o$obs) - - calc.npde <- function(dv, iter, smooth=FALSE) { - yobs <- dv[iter==0] + + niter <- nrow(o$sim) / nrow(o$obs) + + calc.npde <- function(dv, iter, smooth = FALSE) { + yobs <- dv[iter == 0] nobs <- length(yobs) niter <- max(iter) # = length(dv)/nobs - 1 - ysim <- matrix(dv[iter != 0], nrow=nobs, ncol=niter) - + ysim <- matrix(dv[iter != 0], nrow = nobs, ncol = niter) + # -- Non-decorrelated -- - + pd.obs <- apply(ysim < yobs, 1, mean) - + # Handle extremes if (isTRUE(smooth)) { - u <- runif(nobs, 0, 1/niter) - pd.obs[pd.obs == 1] <- rep(1 - (1/niter), len=sum(pd.obs == 1)) + u <- runif(nobs, 0, 1 / niter) + pd.obs[pd.obs == 1] <- + rep(1 - (1 / niter), len = sum(pd.obs == 1)) pd.obs <- pd.obs + u } else { - pd.obs[pd.obs == 0] <- 1/(2*niter) - pd.obs[pd.obs == 1] <- 1 - 1/(2*niter) + pd.obs[pd.obs == 0] <- 1 / (2 * niter) + pd.obs[pd.obs == 1] <- 1 - 1 / (2 * niter) } - + # Normalize npd.obs <- qnorm(pd.obs) - + # Compute for simulated data r <- apply(ysim, 1, rank) - pd.sim <- r/(niter + 1) # +1 prevents Inf after normalization + pd.sim <- r / (niter + 1) # +1 prevents Inf after normalization npd.sim <- qnorm(pd.sim) - + # -- Decorrelated -- - + epred <- apply(ysim, 1, mean) ec <- cov(t(ysim)) - ec.inv.chol <- chol(solve(ec)) # In future, should we consider different decorrelation methods? - + ec.inv.chol <- + solve(chol(ec)) # In future, should we consider different decorrelation methods? + eres <- yobs - epred esres <- ysim - epred - - ewres <- drop(ec.inv.chol %*% eres) - eswres <- ec.inv.chol %*% esres - + + ewres <- drop(t(ec.inv.chol) %*% eres) + eswres <- t(ec.inv.chol) %*% esres + pde.obs <- apply(eswres < ewres, 1, mean) - + # Handle extremes if (isTRUE(smooth)) { # Can use the same uniform as for pd, no need to call runif again - pde.obs[pde.obs == 1] <- rep(1 - (1/niter), len=sum(pde.obs == 1)) + pde.obs[pde.obs == 1] <- + rep(1 - (1 / niter), len = sum(pde.obs == 1)) pde.obs <- pde.obs + u } else { - pde.obs[pde.obs == 0] <- 1/(2*niter) - pde.obs[pde.obs == 1] <- 1 - 1/(2*niter) + pde.obs[pde.obs == 0] <- 1 / (2 * niter) + pde.obs[pde.obs == 1] <- 1 - 1 / (2 * niter) } - + # Normalize npde.obs <- qnorm(pde.obs) - + # Compute for simulated data r <- apply(eswres, 1, rank) - pde.sim <- r/(niter + 1) # +1 prevents Inf after normalization + pde.sim <- r / (niter + 1) # +1 prevents Inf after normalization npde.sim <- qnorm(pde.sim) - + data.table( - iter=iter, - epred=epred, - eres=c(eres, esres), - ewres=c(ewres, eswres), - npd=c(npd.obs, npd.sim), - npde=c(npde.obs, npde.sim)) + iter = iter, + epred = epred, + eres = c(eres, esres), + ewres = c(ewres, eswres), + npd = c(npd.obs, npd.sim), + npde = c(npde.obs, npde.sim) + ) } - + obssim <- data.table( y = c(o$obs$y, o$sim$y), - id = rep(id, length.out=(niter + 1)*nrow(o$obs)), - iter = rep(0:niter, each=nrow(o$obs))) - - obssim <- obssim[, rn := (1:.N)][, cbind(rn, calc.npde(y, iter, smooth=smooth)), by=id][order(rn)][, rn := NULL] - - npdeobs <- obssim[iter==0] - npdesim <- obssim[iter!=0] - - update(o, npdeobs=npdeobs, npdesim=npdesim) + id = rep(id, length.out = (niter + 1) * nrow(o$obs)), + iter = rep(0:niter, each = nrow(o$obs)) + ) + + obssim <- + obssim[, rn := (1:.N)][, cbind(rn, calc.npde(y, iter, smooth = smooth)), by = + id][order(rn)][, rn := NULL] + + npdeobs <- obssim[iter == 0] + npdesim <- obssim[iter != 0] + + update(o, npdeobs = npdeobs, npdesim = npdesim) } # vim: ts=2 sw=2 et diff --git a/inst/extdata/NPDE/npdeobs.csv b/inst/extdata/NPDE/npdeobs.csv index 9c0bcf9..464bb61 100644 --- 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+50,0,77.51983,-6.91983,-0.438363613708399,-0.331853346436817,-0.412463129441405 +50,0,50.69201,-11.39201,-1.26540918284388,-0.495850347347453,-1.2815515655446 diff --git a/tests/testthat/_snaps/plot/censored-plot-with-aql.svg b/tests/testthat/_snaps/plot/censored-plot-with-aql.svg index 8a75992..2e8e1ec 100644 --- a/tests/testthat/_snaps/plot/censored-plot-with-aql.svg +++ b/tests/testthat/_snaps/plot/censored-plot-with-aql.svg @@ -61,12 +61,16 @@ + + + + - - + + - + @@ -96,6 +100,10 @@ + + + + @@ -167,33 +175,33 @@ - -Observed Percentiles -(black lines) - - - - - - -10% -50% -90% - + +Observed Percentiles +(black lines) + + + + + + +10% +50% +90% + Simulated Percentiles Median (lines) 95% CI (areas) - - - - - - - - - -10% -50% -90% + + + + + + + + + +10% +50% +90% @@ -258,17 +266,15 @@ - -Percentage of ALQ -Median (lines) 95% CI (areas) - - - - - - -observed -simulated + +Percentage of ALQ +Median (lines) 95% CI (areas) + + + + +observed +simulated @@ -748,6 +754,10 @@ + + + + @@ -853,6 +863,10 @@ + + + + @@ -891,6 +905,10 @@ + + + + @@ -940,6 +958,10 @@ + + + + @@ -984,6 +1006,10 @@ + + + + @@ -1010,6 +1036,10 @@ + + + + @@ -1066,7 +1096,11 @@ - + + + + + @@ -1091,6 +1125,10 @@ + + + + @@ -1132,33 +1170,33 @@ - -Observed Percentiles -(black lines) - - - - - - -10% -50% -90% - + +Observed Percentiles +(black lines) + + + + + + +10% +50% +90% + Simulated Percentiles Median (lines) 95% CI (areas) - - - - - - - - - -10% -50% -90% + + + + + + + + + +10% +50% +90% @@ -1203,17 +1241,15 @@ - -Percentage of ALQ -Median (lines) 95% CI (areas) - - - - - - -observed -simulated + +Percentage of ALQ +Median (lines) 95% CI (areas) + + + + +observed +simulated @@ -1673,6 +1709,10 @@ + + + + @@ -1763,6 +1803,10 @@ + + + + @@ -1796,6 +1840,10 @@ + + + + @@ -1835,6 +1883,10 @@ + + + + @@ -1859,6 +1911,10 @@ + + + + @@ -1875,6 +1931,10 @@ + + + + diff --git a/tests/testthat/_snaps/plot/censored-plot-with-bql-aql.svg b/tests/testthat/_snaps/plot/censored-plot-with-bql-aql.svg index caea95d..5a30707 100644 --- a/tests/testthat/_snaps/plot/censored-plot-with-bql-aql.svg +++ b/tests/testthat/_snaps/plot/censored-plot-with-bql-aql.svg @@ -70,12 +70,16 @@ + + + + - - + + - + @@ -107,9 +111,13 @@ + + + + - + @@ -142,6 +150,10 @@ + + + + @@ -222,33 +234,33 @@ - -Observed Percentiles -(black lines) - - - - - - -10% -50% -90% - + +Observed Percentiles +(black lines) + + + + + + +10% +50% +90% + Simulated Percentiles Median (lines) 95% CI (areas) - - - - - - - - - -10% -50% -90% + + + + + + + + + +10% +50% +90% @@ -313,17 +325,15 @@ - -Percentage of BLQ -Median (lines) 95% CI (areas) - - - - - - -observed -simulated + +Percentage of BLQ +Median (lines) 95% CI (areas) + + + + +observed +simulated @@ -388,17 +398,15 @@ - -Percentage of ALQ -Median (lines) 95% CI (areas) - - - - - - -observed -simulated + +Percentage of ALQ +Median (lines) 95% CI (areas) + + + + +observed +simulated @@ -878,6 +886,10 @@ + + + + @@ -988,6 +1000,10 @@ + + + + @@ -1093,6 +1109,10 @@ + + + + @@ -1131,6 +1151,10 @@ + + + + @@ -1180,6 +1204,10 @@ + + + + @@ -1224,6 +1252,10 @@ + + + + @@ -1277,6 +1309,10 @@ + + + + @@ -1303,6 +1339,10 @@ + + + + @@ -1329,6 +1369,10 @@ + + + + @@ -1398,7 +1442,11 @@ - + + + + + @@ -1425,6 +1473,10 @@ + + + + @@ -1450,6 +1502,10 @@ + + + + @@ -1495,33 +1551,33 @@ - -Observed Percentiles -(black lines) - - - - - - -10% -50% -90% - + +Observed Percentiles +(black lines) + + + + + + +10% +50% +90% + Simulated Percentiles Median (lines) 95% CI (areas) - - - - - - - - - -10% -50% -90% + + + + + + + + + +10% +50% +90% @@ -1566,17 +1622,15 @@ - -Percentage of BLQ -Median (lines) 95% CI (areas) - - - - - - -observed -simulated + +Percentage of BLQ +Median (lines) 95% CI (areas) + + + + +observed +simulated @@ -1621,17 +1675,15 @@ - -Percentage of ALQ -Median (lines) 95% CI (areas) - - - - - - -observed -simulated + +Percentage of ALQ +Median (lines) 95% CI (areas) + + + + +observed +simulated @@ -2091,6 +2143,10 @@ + + + + @@ -2186,6 +2242,10 @@ + + + + @@ -2276,6 +2336,10 @@ + + + + @@ -2309,6 +2373,10 @@ + + + + @@ -2348,6 +2416,10 @@ + + + + @@ -2387,6 +2459,10 @@ + + + + @@ -2415,6 +2491,10 @@ + + + + @@ -2431,6 +2511,10 @@ + + + + @@ -2447,6 +2531,10 @@ + + + + diff --git a/tests/testthat/_snaps/plot/censored-plot-with-bql.svg b/tests/testthat/_snaps/plot/censored-plot-with-bql.svg index bd971de..f2a49b3 100644 --- a/tests/testthat/_snaps/plot/censored-plot-with-bql.svg +++ b/tests/testthat/_snaps/plot/censored-plot-with-bql.svg @@ -61,12 +61,16 @@ + + + + - - + + - + @@ -98,9 +102,13 @@ + + + + - + @@ -169,33 +177,33 @@ - -Observed Percentiles -(black lines) - - - - - - -10% -50% -90% - + +Observed Percentiles +(black lines) + + + + + + +10% +50% +90% + Simulated Percentiles Median (lines) 95% CI (areas) - - - - - - - - - -10% -50% -90% + + + + + + + + + +10% +50% +90% @@ -260,17 +268,15 @@ - -Percentage of BLQ -Median (lines) 95% CI (areas) - - - - - - -observed -simulated + +Percentage of BLQ +Median (lines) 95% CI (areas) + + + + +observed +simulated @@ -750,6 +756,10 @@ + + + + @@ -860,6 +870,10 @@ + + + + @@ -898,6 +912,10 @@ + + + + @@ -947,6 +965,10 @@ + + + + @@ -991,6 +1013,10 @@ + + + + @@ -1017,6 +1043,10 @@ + + + + @@ -1073,7 +1103,11 @@ - + + + + + @@ -1100,6 +1134,10 @@ + + + + @@ -1141,33 +1179,33 @@ - -Observed Percentiles -(black lines) - - - - - - -10% -50% -90% - + +Observed Percentiles +(black lines) + + + + + + +10% +50% +90% + Simulated Percentiles Median (lines) 95% CI (areas) - - - - - - - - - -10% -50% -90% + + + + + + + + + +10% +50% +90% @@ -1212,17 +1250,15 @@ - -Percentage of BLQ -Median (lines) 95% CI (areas) - - - - - - -observed -simulated + +Percentage of BLQ +Median (lines) 95% CI (areas) + + + + +observed +simulated @@ -1682,6 +1718,10 @@ + + + + @@ -1777,6 +1817,10 @@ + + + + @@ -1810,6 +1854,10 @@ + + + + @@ -1849,6 +1897,10 @@ + + + + @@ -1873,6 +1925,10 @@ + + + + @@ -1889,6 +1945,10 @@ + + + + diff --git a/tests/testthat/_snaps/plot/one-sided-strat-formula-with-facet-wrap.svg b/tests/testthat/_snaps/plot/one-sided-strat-formula-with-facet-wrap.svg index e256863..942c332 100644 --- a/tests/testthat/_snaps/plot/one-sided-strat-formula-with-facet-wrap.svg +++ b/tests/testthat/_snaps/plot/one-sided-strat-formula-with-facet-wrap.svg @@ -18,7 +18,7 @@ - + @@ -859,33 +859,33 @@ TIME Percentiles and associated 95% CI - -Observed Percentiles -(black lines) - - - - - - -10% -50% -90% - + +Observed Percentiles +(black lines) + + + + + + +10% +50% +90% + Simulated Percentiles Median (lines) 95% CI (areas) - - - - - - - - - -10% -50% -90% + + + + + + + + + +10% +50% +90% One sided strat formula with facet_wrap diff --git a/tests/testthat/_snaps/plot/single-value-group.svg b/tests/testthat/_snaps/plot/single-value-group.svg index 41e5f9a..3e4dd18 100644 --- a/tests/testthat/_snaps/plot/single-value-group.svg +++ b/tests/testthat/_snaps/plot/single-value-group.svg @@ -18,7 +18,7 @@ - + @@ -186,33 +186,33 @@ TIME Percentiles and associated 95% CI - -Observed Percentiles -(black lines) - - - - - - -5% -50% -95% - + +Observed Percentiles +(black lines) + + + + + + +5% +50% +95% + Simulated Percentiles Median (lines) 95% CI (areas) - - - - - - - - - -5% -50% -95% + + + + + + + + + +5% +50% +95% single-value group diff --git a/tests/testthat/_snaps/plot/two-sided-strat-formula-with-facet-grid.svg b/tests/testthat/_snaps/plot/two-sided-strat-formula-with-facet-grid.svg index a8921fd..cc8dec9 100644 --- a/tests/testthat/_snaps/plot/two-sided-strat-formula-with-facet-grid.svg +++ b/tests/testthat/_snaps/plot/two-sided-strat-formula-with-facet-grid.svg @@ -18,7 +18,7 @@ - + @@ -863,33 +863,33 @@ TIME Percentiles and associated 95% CI - -Observed Percentiles -(black lines) - - - - - - -10% -50% -90% - + +Observed Percentiles +(black lines) + + + + + + +10% +50% +90% + Simulated Percentiles Median (lines) 95% CI (areas) - - - - - - - - - -10% -50% -90% + + + + + + + + + +10% +50% +90% Two sided strat formula with facet_grid diff --git a/tests/testthat/test_npde.R b/tests/testthat/test_npde.R index 88b384d..b10eb73 100644 --- a/tests/testthat/test_npde.R +++ b/tests/testthat/test_npde.R @@ -1,47 +1,47 @@ test_that("npde colnames are correct", { - obs <- obs_data[MDV==0] - sim <- sim_data[MDV==0] + obs <- obs_data[MDV == 0] + sim <- sim_data[MDV == 0] - npde <- observed(obs, x=NULL, y=DV) %>% - simulated(sim, y=DV) %>% - npde(id=ID) + npde <- observed(obs, x = NULL, y = DV) %>% + simulated(sim, y = DV) %>% + npde(id = ID) - expect_true( - all(colnames(npde$npdeobs) == c("id", "iter","epred", "eres", "ewres", "npd", "npde")) - ) + expect_true(all( + colnames(npde$npdeobs) == c("id", "iter", "epred", "eres", "ewres", "npd", "npde") + )) + + expect_true(all( + colnames(npde$npdesim) == c("id", "iter", "epred", "eres", "ewres", "npd", "npde") + )) - expect_true( - all(colnames(npde$npdesim) == c("id", "iter","epred", "eres", "ewres", "npd", "npde")) - ) - }) test_that("npde results are correct", { skip_on_cran() - obs <- obs_data[MDV==0] - sim <- sim_data[MDV==0] + obs <- obs_data[MDV == 0] + sim <- sim_data[MDV == 0] - npde <- observed(obs, x=NULL, y=DV) - npde <- simulated(npde, sim, y=DV) - npde <- npde(npde, id=ID) - - location=system.file("extdata/NPDE","npdeobs.csv", package="tidyvpc") - - stats <- fread(location) + npde <- observed(obs, x = NULL, y = DV) + npde <- simulated(npde, sim, y = DV) + npde <- npde(npde, id = ID) - expect_equal(npde$npdeobs, stats) + location <- system.file("extdata/NPDE", "npdeobs.csv", package = "tidyvpc") - vpc <- observed(npde$npdeobs, x=epred, y=npde) - vpc <- simulated(vpc, npde$npdesim, y=npde) - vpc <- binning(vpc, "eqcut", nbins=10) + stats <- data.table::fread(location) + + testthat::expect_equal(npde$npdeobs, stats) + + vpc <- observed(npde$npdeobs, x = epred, y = npde) + vpc <- simulated(vpc, npde$npdesim, y = npde) + vpc <- binning(vpc, "eqcut", nbins = 10) vpc <- vpcstats(vpc) expect_true(inherits(vpc, "tidyvpcobj")) vpc_plot <- tidyvpc:::plot.tidyvpcobj(vpc, point.alpha = 0.25) - + expect_true(inherits(vpc_plot, "ggplot")) })