Single value group 51

NEWS.md

@@ -3,6 +3,7 @@
 * `simulated.tidyvpcobj()` detects if the number of simulated rows is not an integer multiple of the number of observed rows and adds the new `xsim` argument to test that x values match between replicated simulations.  It will suggest that MDV filtering may not have occurred if either of these fails [#35](https://github.com/certara/tidyvpc/issues/35).
 * Prevent division by zero in `predcorrect()` transformation [#31](https://github.com/certara/tidyvpc/issues/31).
 * Usability enhancements for prediction corrected VPC (pcVPC), which include support for `binning.tidyvpcobj()` either before or after usage of `predcorrect.tidyvpcobj()`, and automatically performing LOESS pcVPC when `binless.tidyvpcobj()` is used. As a result, the `loess.ypc` argument is no longer required[#43](https://github.com/certara/tidyvpc/issues/43).
+* VPC can work with a single value in a group [#51](https://github.com/certara/tidyvpc/issues/51)
 
 # tidyvpc 1.4.0
 * Fix for npde calculation fix npde calc [#16](https://github.com/certara/tidyvpc/pull/16)

R/vpcstats.R

@@ -525,7 +525,7 @@ binning.tidyvpcobj <- function(o, bin, data=o$data, xbin="xmedian", centers, bre
     stop("Invalid xbin")
   }
   vpc.method <- list(method = "binning")
-  
+
   # check if user supplied predcorrect before binning
   if (!is.null(o$predcor) && o$predcor) {
     pred <- o$pred
@@ -540,7 +540,7 @@ binning.tidyvpcobj <- function(o, bin, data=o$data, xbin="xmedian", centers, bre
       o$sim[, ypc := ifelse(rep(pred, times = nrow(o$sim) / nrow(o$obs)) == 0, 0, (mpred / pred) * y)]
     }
   }
-  
+
   update(o, xbin=xbin, vpc.method = vpc.method)
 }
 
@@ -606,13 +606,13 @@ predcorrect.tidyvpcobj <- function(o, pred, data=o$data, ..., log=FALSE) {
 
   stratbin <- o$.stratbin
   # predcorrect after binning, check if binning/binless has already been specified
-  
+
   if (!is.null(o$vpc.method)) {
     if(o$vpc.method$method == "binless") {
       o$vpc.method$loess.ypc <- TRUE
     } else { #binning specified, perform ypc calculcation
       mpred <- data.table(stratbin, pred)[, mpred := median(pred), by = stratbin]$mpred
-      
+
       if (log) {
         o$obs[, ypc := (mpred - pred) + y]
         o$sim[, ypc := (mpred - pred) + y]
@@ -858,32 +858,37 @@ bin_by_classInt <- function(style, nbins=NULL) {
     nbins <- .check_nbins(nbins)
   }
   function(x, ...) {
-    args <- list(var=x, style=style)
-    if (!is.null(nbins)) {
-      nbins <- .resolve_nbins(nbins, ...)
-      args$n <- nbins
-    }
-    args <- c(args, list(...))
-    if (style %in% c("kmeans", "hclust", "dpih")) {
-      # These don't accept '...' arguments
-      args1 <- args[intersect(names(args), methods::formalArgs(classInt::classIntervals))]
-      args2 <- if (style == "kmeans") {
-        args[intersect(names(args), methods::formalArgs(stats::kmeans))]
-      } else if (style == "hclust") {
-        args[intersect(names(args), methods::formalArgs(stats::hclust))]
-      } else if (style == "dpih") {
-        has_KernSmooth <- requireNamespace("KernSmooth", quietly=TRUE)
-        if (!has_KernSmooth) {
-          stop("Package 'KernSmooth' is required to use the binning method. Please install it.")
+    if (length(unique(x)) > 1) {
+      args <- list(var=x, style=style)
+      if (!is.null(nbins)) {
+        nbins <- .resolve_nbins(nbins, ...)
+        args$n <- nbins
+      }
+      args <- c(args, list(...))
+      if (style %in% c("kmeans", "hclust", "dpih")) {
+        # These don't accept '...' arguments
+        args1 <- args[intersect(names(args), methods::formalArgs(classInt::classIntervals))]
+        args2 <- if (style == "kmeans") {
+          args[intersect(names(args), methods::formalArgs(stats::kmeans))]
+        } else if (style == "hclust") {
+          args[intersect(names(args), methods::formalArgs(stats::hclust))]
+        } else if (style == "dpih") {
+          has_KernSmooth <- requireNamespace("KernSmooth", quietly=TRUE)
+          if (!has_KernSmooth) {
+            stop("Package 'KernSmooth' is required to use the binning method. Please install it.")
+          }
+          args[intersect(names(args), methods::formalArgs(KernSmooth::dpih))]
+        } else {
+          list()
         }
-        args[intersect(names(args), methods::formalArgs(KernSmooth::dpih))]
-      } else {
-        list()
+        args <- c(args1, args2)
       }
-      args <- c(args1, args2)
+      args <- args[!duplicated(args)]
+      breaks <- do.call(classInt::classIntervals, args)$brks
+    } else {
+      # If a group has a single value, `classInt::classIntervals` gives an error
+      breaks <- rep(1, length(x))
     }
-    args <- args[!duplicated(args)]
-    breaks <- do.call(classInt::classIntervals, args)$brks
     cut_at(breaks)(x)
   }
 }

tests/testthat/test-binning.R

@@ -1,46 +1,46 @@
 test_that("obs bins equal stats bins", {
   obs_data <- as.data.table(tidyvpc::obs_data)
   sim_data <- as.data.table(tidyvpc::sim_data)
-  
+
   ## Subest MDV = 0
   obs_data <- obs_data[MDV == 0]
   sim_data <- sim_data[MDV == 0]
-  
+
   unique_bins_obs <- as.factor(unique(obs_data$NTIME))
   #Assign observed and simulated data to tidyvpc object
   vpc <- observed(obs_data, x = TIME, y = DV )
-  
+
   vpc <- simulated(vpc, sim_data, y = DV)
-  
+
   vpc <- binning(vpc, bin = NTIME)
-  
+
   vpc <- vpcstats(vpc)
-  
+
   unique_bins_vpc <- unique(vpc$stats$bin)
 
   #Check that bins match for binning on xvar NTIME
   expect_equal(unique_bins_obs, unique_bins_vpc)
-  
+
 })
 
 
 test_that("cat obs vpcstats is correct", {
   obs_cat_data <- as.data.table(tidyvpc::obs_cat_data)
   sim_cat_data <- as.data.table(tidyvpc::sim_cat_data)
-  
+
   vpc <- observed(obs_cat_data, x = agemonths, y = zlencat )
   vpc <- simulated(vpc, sim_cat_data, y = DV)
   vpc <- binning(vpc, bin = round(agemonths, 0))
   vpc <- vpcstats(vpc, vpc.type = "categorical")
-  
+
   location <- system.file("extdata/Binning","cat_stats.csv",package="tidyvpc")
-  
+
   stats <- fread(location, colClasses = c(pname = "factor"))
   stats$bin <- as.factor(stats$bin)
-  
+
   setkeyv(stats, c("xbin"))
-  
-  
+
+
   #Check for equality, dispatches to data.table::all.equal method
   expect_identical(all.equal(vpc$stats, stats), TRUE)
 
@@ -51,129 +51,147 @@ test_that("cat obs vpcstats is correct", {
 test_that("cat obs strat vpcstats is correct", {
   obs_cat_data <- as.data.table(tidyvpc::obs_cat_data)
   sim_cat_data <- as.data.table(tidyvpc::sim_cat_data)
-  
+
   vpc <- observed(obs_cat_data, x = agemonths, y = zlencat )
   vpc <- simulated(vpc, sim_cat_data, y = DV)
   vpc <- stratify(vpc, ~ Country_ID_code)
   vpc <- binning(vpc, bin = round(agemonths, 0))
   vpc <- vpcstats(vpc, vpc.type = "categorical")
-  
+
   location <- system.file("extdata/Binning","cat_strat_stats.csv",package="tidyvpc")
-  
+
   stats <- fread(location, colClasses = c(pname = "factor"))
   stats$bin <- as.factor(stats$bin)
 
   setkeyv(stats, c(names(vpc$strat), "xbin"))
-  
-  
+
+
   #Check for equality, dispatches to data.table::all.equal method
   expect_identical(all.equal(vpc$stats, stats), TRUE)
-  
+
 })
 
 test_that("binning methods are valid", {
 
   ## Subest MDV = 0
   obs <- obs_data[MDV == 0]
   sim <- sim_data[MDV == 0]
-  
+
   vpc <- observed(obs, x = TIME, y = DV )
   vpc <- simulated(vpc, sim, y = DV)
-  
+
   centers <- c(0,1,5,8,12)
   vpc <- binning(vpc, bin = "centers", centers = centers)
   expect_equal(vpc$xbin$bin, as.factor(centers))
-  
+
   vpc <- binning(vpc, bin = "breaks", breaks = c(1,3,6,9,11))
   expect_true(length(levels(vpc$xbin$bin)) == 11)
-  
+
   vpc <- binning(vpc, bin = "breaks", breaks = c(1,3,6,9,11))
   expect_true(length(levels(vpc$xbin$bin)) == 11)
-  
+
   vpc <- binning(vpc, bin = "pam", nbins = 6)
   expect_true(max(vpc$xbin$xbin) < 12)
-  
+
   vpc <- binning(vpc, bin = "ntile", nbins = 6)
   expect_true(nrow(vpc$xbin) == 6)
-  
+
   vpc <- binning(vpc, bin = "eqcut", nbins = 12)
   expect_true(nrow(vpc$xbin) == 12)
-  
+
   vpc <- binning(vpc, bin = "sd", nbins = 4)
   expect_true(nrow(vpc$xbin) == 6)
-  
+
 })
 
 
 test_that("binning by stratum works", {
   obs_data <- obs_data[MDV == 0]
   sim_data <- sim_data[MDV == 0]
   obs_data$PRED <- sim_data[REP == 1, PRED]
-  
+
   vpc <- observed(obs_data, x=TIME, y=DV)
   vpc <- simulated(vpc, sim_data, y=DV)
-  vpc <- stratify(vpc, ~ GENDER + STUDY) 
+  vpc <- stratify(vpc, ~ GENDER + STUDY)
   vpc <- binning(vpc, stratum = list(GENDER = "M", STUDY = "Study A"), bin = "jenks", nbins = 5, by.strata = T)
   vpc <- binning(vpc, stratum = list(GENDER = "F", STUDY = "Study A"), bin = "centers", centers = c(0.5,3,5,10,15), by.strata = T)
   vpc <- binning(vpc, stratum = list(GENDER = "M", STUDY = "Study B"), bin = "kmeans", by.strata = T)
   vpc <- binning(vpc, stratum = list(GENDER = "F", STUDY = "Study B"), bin = "pam", nbins = 5, by.strata = T)
-  vpc <- predcorrect(vpc, pred=PRED) 
+  vpc <- predcorrect(vpc, pred=PRED)
   vpc <- vpcstats(vpc)
-  
+
   expect_true(inherits(vpc, "tidyvpcobj") && vpc$bin.by.strata)
-  
+
 })
 
-  
+
 test_that("binning errors are valid", {
-  
+
   obs <- obs_data[MDV == 0]
   sim <- sim_data[MDV == 0]
-  
+
   vpc <- observed(obs, x = TIME, y = DV )
   vpc <- simulated(vpc, sim, y = DV)
   expect_true(inherits(binning(vpc, xbin = NTIME), "tidyvpcobj"))
   expect_error(binning(vpc, xbin = c(1:5)))
-  
+
 })
 
 test_that("binning can be used after predcorrect", {
   obs_data <- obs_data[MDV == 0]
   sim_data <- sim_data[MDV == 0]
   obs_data$PRED <- sim_data[REP == 1, PRED]
-  
+
   vpc <- observed(obs_data, x = TIME, y = DV )
   vpc <- simulated(vpc, sim_data, y = DV)
   vpc <- stratify(vpc, ~ GENDER)
   vpc <- predcorrect(vpc, pred = PRED)
   vpc <- binning(vpc, bin = NTIME)
   vpc <- vpcstats(vpc)
-  
+
   location <- system.file("extdata/Binning","predcor_strat_stats.csv",package="tidyvpc")
   stats <- fread(location, colClasses = c(qname = "factor"))
   stats[, bin := factor(bin, levels = levels(vpc$stats$bin))]
   setkeyv(stats, c(names(vpc$strat), "xbin"))
-  
+
   expect_equal(vpc$stats, stats)
 })
 
 test_that("binning can be used before predcorrect", {
   obs_data <- obs_data[MDV == 0]
   sim_data <- sim_data[MDV == 0]
   obs_data$PRED <- sim_data[REP == 1, PRED]
-  
+
   vpc <- observed(obs_data, x = TIME, y = DV )
   vpc <- simulated(vpc, sim_data, y = DV)
   vpc <- stratify(vpc, ~ GENDER)
   vpc <- binning(vpc, bin = NTIME)
   vpc <- predcorrect(vpc, pred = PRED)
   vpc <- vpcstats(vpc)
-  
+
   location <- system.file("extdata/Binning","predcor_strat_stats.csv",package="tidyvpc")
   stats <- fread(location, colClasses = c(qname = "factor"))
   stats[, bin := factor(bin, levels = levels(vpc$stats$bin))]
   setkeyv(stats, c(names(vpc$strat), "xbin"))
-  
+
   expect_equal(vpc$stats, stats)
 })
-
+
+test_that("binning works with single-value groups (#51)", {
+  d_obs <-
+    data.frame(
+      group = rep(c("Patient", "Healthy"), each = 5),
+      conc = c(rep(0, 5), 1:5),
+      value = 1:10
+    )
+
+  d_sim <-
+    d_obs[rep(1:nrow(d_obs), 5), ]
+
+  value <-
+    observed(d_obs, x = conc, yobs = value) %>%
+    simulated(d_sim, xsim = conc, ysim = value) %>%
+    stratify(~group) %>%
+    binning(bin = "jenks")
+  expect_s3_class(value, "tidyvpcobj")
+})