update tests

R/multivariate_change_and_difference.R

@@ -262,9 +262,9 @@ multivariate_difference <- function(df,
 
   # compute time.var2 change from time.var
   output$dispersion_diff <- output$dispersion2 - output$dispersion
-  output$trt_greater_disp <- ifelse(as.numeric(output$dispersion_diff) < 0,
-    output[[treatment.var]],
-    output[[treatment.var2]])
+  idx <- as.numeric(output$dispersion_diff) < 0
+  output$trt_greater_disp <- output[[treatment.var]]
+  output$trt_greater_disp[idx] <- output[[treatment.var2]][idx]
   output$abs_dispersion_diff <- abs(output$dispersion_diff)
   output$composition_diff <- mapply(
     function(x, y) {z <- x - y; sqrt(sum(z*z))},
@@ -355,7 +355,7 @@ dblctr <- function(d) {
 
 # Calculate a full Bray Curtis dissimilarity matrix
 # @param a abundance matrix (columns are species, rows are observations) with no missing values
-# FIXME should probably implement in c
+# FIXME should probably implement in C
 braycurtis <- function(a) {
   n <- nrow(a)
   ij <- combn(n, 2)

tests/testthat/test_multivariate_change.R

@@ -33,7 +33,7 @@ test_that("multivariate_change function returns correct result", {
   expect_is(myresults1, "data.frame")
   expect_equal(nrow(myresults1), 3)
   expect_equal(ncol(myresults1), 4)
-  expect_equal(myresults1$composition_change[1], 0.1325553, tolerance = 0.00001)
+  expect_equal(myresults1$composition_change[1], 0.1244439, tolerance = 0.00001)
   expect_equal(myresults1$dispersion_change[1], 0.03804344, tolerance = 0.000001)
 
   #test that it works with treatment
@@ -70,4 +70,13 @@ test_that("multivariate_change function returns correct result", {
                                species.var = "species",
                                time.var = "year"), "Multiple records for one or more species found at:\n year   plot\n \"2002\" \"25\"")
 
+  # imaginary distance generates warning
+  df <- data.frame(
+    t = rep(0:1, each = 6),
+    r = rep(0:2, each = 2),
+    s = rep(c('a', 'b'), 6),
+    a = c(0, 2, 1, 2, 1, 0, 1, 2, 1, 2, 1, 2)
+  )
+  expect_warning(multivariate_change(df,
+    time.var = 't', replicate.var = 'r', species.var = 's', abundance.var = 'a'))
 })

tests/testthat/test_multivariate_difference.R

@@ -33,7 +33,7 @@ test_that("multivariate_difference function returns correct result", {
   expect_is(myresults1, "data.frame")
   expect_equal(nrow(myresults1), 2)
   expect_equal(ncol(myresults1), 5)
-  expect_equal(myresults1$composition_diff[1], 0.08895478, tolerance = 0.00001)
+  expect_equal(myresults1$composition_diff[1], 0.07108743, tolerance = 0.00001)
   expect_equal(myresults1$abs_dispersion_diff[1], 0.01123982, tolerance = 0.000001)
 
   #test that it works with time and reference treatment