From c1a7b5ceb6fee62c561bcc38dd03579ee95ac210 Mon Sep 17 00:00:00 2001 From: ilizaga <30837036+ivanlizaga@users.noreply.github.com> Date: Mon, 4 Mar 2019 17:29:05 +0100 Subject: [PATCH] Update README.md --- README.md | 38 -------------------------------------- 1 file changed, 38 deletions(-) diff --git a/README.md b/README.md index ff9109c..dacfda0 100644 --- a/README.md +++ b/README.md @@ -36,41 +36,3 @@ library(fingerPro) #AG (cropland) #PI and PI1 (Pine forest, at first looks different but when you display de LDA plot you will see that the wisher decision in join both pines as the same source) #SS (subsoil) -data <- catchment -boxPlot(data, columns = 1:6, ncol = 3) -correlationPlot(data, columns = 1:5, mixtures = T) -LDAPlot(data, P3D=FALSE) -#variables are collinear -#select the optimum set of tracers by implementing the statistical tests -data <- rangeTest(data) -data <- KWTest(data) -data <- DFATest(data) -#Check how the selected tracers discriminate between sources -LDAPlot(data, P3D=F) -LDAPlot(data, P3D=T) -#2D and 3D LDAPlots suggest that two of the sources have to be combined -#reload the original dataset "catchment" -data <- catchment -# Combine sources PI1 and PI based on the previous LDAPlot -data$Land_Use[data$Land_Use == 'PI1'] <- 'PI' -#select the optimum set of tracers by implementing the statistical tests -data <- rangeTest(data) -data <- KWTest(data) -data <- DFATest(data) -LDAPlot(data, P3D=F) -#Now the optimum tracer properties selected show a good discrimination, so proceed with the unmix function -``` -![lda comparisson](https://user-images.githubusercontent.com/30837036/43969407-535dab0c-9cca-11e8-8c3d-18fbc3048fb0.png) -```r -# Figure a) LDAPlot previous to PI1+PI fusion. b) LDAPlot after the fusion of both pines -result <- unmix(data, samples = 100L, iter =100L) -#Display the results -plotResults(result, y_high = 5, n = 1) -``` - -![results](https://user-images.githubusercontent.com/30837036/43969666-2ebd7a1a-9ccb-11e8-8d71-445ad2e15daa.png) -```r - id GOF.mean GOF.SD AG.mean AG.SD PI.mean PI.SD SS.mean SS.SD - 42744 0.94430071 0.03681212 0.18148918 0.061388 0.4726643 0.0785878 0.3458461 0.0654922 - -```