add tests

tests/testthat/test_mla.R

@@ -1,11 +1,9 @@
 library(testthat)
 library(ForesToolboxRS)
-library(stars)
 library(raster)
 library(caret)
 data(endm)
-context("ForesToolboxRS ndfiSMA")
-
+context("ForesToolboxRS mla")
 
 training_split = 80
 vegt <- extract(img_l8, endm)
@@ -57,3 +55,14 @@ MC <- errorMatrix(prediction = prediction, reference = testing[,dim(endm)[2]])
 results <- list(Overall_accuracy = (confusionMatrix(MC$MC_ini)$overall[1:6])*100,
                 Confusion_matrix = MC$MCf,
                 Classification = raster_class)
+
+test_that("try mla", {
+  ndfi <- c(
+    0.86, 0.93, 0.97, 0.91, 0.95, 0.96, 0.91, 0.88, 0.92, 0.89,
+    0.90, 0.89, 0.91, 0.92, 0.89, 0.90, 0.92, 0.84, 0.46, 0.20,
+    0.27, 0.22, 0.52, 0.63, 0.61, 0.67, 0.64, 0.86
+  )
+  ndfi_ts <- ts(ndfi, start = 1990, end = 2017, frequency = 1)
+  cd <- pvts(x = ndfi_ts, startm = 2008, endm = 2008, threshold = 5)
+  expect_equal(as.vector(cd$Threshold[2]), test_pvts_ts(ndfi_ts,  startm = 2008, endm = 2008, threshold = 5))
+})

tests/testthat/test_pvts.R

@@ -1,5 +1,46 @@
 library(testthat)
 library(ForesToolboxRS)
-library(stars)
 library(raster)
 context("ForesToolboxRS pvts")
+
+test_pvts_vector <- function(x, startm, endm, threshold = 5) {
+
+    mean.pvts <- mean(x[1:(startm - 1)])
+
+    std.pvts <- sd(x[1:(startm - 1)])
+
+    li <- mean.pvts - threshold * std.pvts
+
+    return(li)
+}
+
+test_that("try pvts", {
+  x <- c(80, 78, 79, 88, 86, 80, 82, 76, 81, 25, 76)
+  cd <- pvts(x, startm = 10, endm = 10, threshold = 5)
+  expect_equal(as.vector(cd$Threshold[2]), test_pvts_vector(x, startm = 10, endm = 10, threshold = 5))
+})
+
+test_pvts_ts <- function(x, startm, endm, threshold = 5) {
+
+  startm.pvts <- which(time(x) == startm)
+  endm.pvts <- which(time(x) == endm)
+
+  mean.pvts <- mean(x[1:(startm.pvts - 1)])
+
+  std.pvts <- sd(x[1:(startm.pvts - 1)])
+
+  li <- mean.pvts - threshold * std.pvts
+
+  return(li)
+}
+
+test_that("try pvts", {
+  ndfi <- c(
+    0.86, 0.93, 0.97, 0.91, 0.95, 0.96, 0.91, 0.88, 0.92, 0.89,
+    0.90, 0.89, 0.91, 0.92, 0.89, 0.90, 0.92, 0.84, 0.46, 0.20,
+    0.27, 0.22, 0.52, 0.63, 0.61, 0.67, 0.64, 0.86
+    )
+  ndfi_ts <- ts(ndfi, start = 1990, end = 2017, frequency = 1)
+  cd <- pvts(x = ndfi_ts, startm = 2008, endm = 2008, threshold = 5)
+  expect_equal(as.vector(cd$Threshold[2]), test_pvts_ts(ndfi_ts,  startm = 2008, endm = 2008, threshold = 5))
+})

tests/testthat/test_smootH.R

@@ -1,6 +1,5 @@
 library(testthat)
 library(ForesToolboxRS)
-library(stars)
 library(raster)
 library(forecast)
 context("ForesToolboxRS smootH")
@@ -21,7 +20,6 @@ test_smootH <- function(x, interp = "na.interp") {
       stop("Unsupported interpolation method")
     }
 
-    # We apply Hamunyela Smoothing
     for (j in 2:(length(x) - 1)) {
       x[j] <- ifelse(((x[j] - x[j - 1]) < -0.01 * x[j - 1]) & ((x[j] - x[j + 1]) < -0.01 * x[j + 1]),
         (x[j - 1] + x[j + 1]) / 2, x[j]
@@ -44,7 +42,7 @@ test_smootH <- function(x, interp = "na.interp") {
         stop("Unsupported interpolation method")
       }
     }
-    # We apply Hamunyela Smoothing
+
     for (i in 1:dim(x)[1]) {
       for (j in 2:(dim(x)[2] - 1)) {
         x[i, ][j] <- ifelse(((x[i, ][j] - x[i, ][j - 1]) < -0.01 * x[i, ][j - 1]) & ((x[i, ][j] - x[i, ][j + 1]) < -0.01 * x[i, ][j + 1]),