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sdmAusenciasReais.R
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##registrando hora do incio
timeOne = Sys.time()
##abrindo pacotes necessarios
library(raster)
library(rJava)
library(biomod2)
Sys.setenv(JAVA_HOME = "/usr/lib/jvm/java-7-openjdk-amd64")
options(java.parameters = "Xmx7g")
# ##definindo prametros e variaveis globais (NOTEBOOK)
# projectFolder = "/home/anderson/Documentos/Projetos/Improved pseudo-absences_TESTE" #pasta do projeto
# envVarFolder = "/home/anderson/PosDoc/dados_ambientais/dados_projeto" #pasta com as variaveis ambientais
# envVarPaths = list.files(path=envVarFolder, full.names=TRUE) #lista com os caminhos das camadas no sistema (comp.)
# AmSulShape = rgdal::readOGR("/home/anderson/PosDoc/shapefiles/Am_Sul/borders.shp") #shape da America do Sul
# maxentFolder = '/home/anderson/R/x86_64-pc-linux-gnu-library/3.3/dismo/java/maxent.jar' #pasta para resultados do maxent
# ## spsTypes = c('spHW', 'spHD', 'spCD') #nomes das especies
# ## sdmTypes = 'real'
# sampleSizes = 100 #c(10,20,40,80,160)
# NumRep = 2 #10 #numero de replicas (de cada cenario amostral)
# ##variaveis preditoras
# ## elevation = raster('/home/anderson/PosDoc/dados_ambientais/DEM/DEM.tif')
# predictors = stack(list.files(path=envVarPaths[1],full.names=TRUE, pattern='.asc')) #predictors com todas as variaveis (presente)
# predictors = predictors[[c('bioclim_01','bioclim_12')]]
# predictors = stack(mask(x=predictors, mask=AmSulShape))
# crs(predictors) = CRS('+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0') #ajustando CRS
# Nsp = NumRep #numero de especies a serem criadas e trabalhadas igual ao numero de replicas
# statResultsSDMreal = data.frame() #tabela de estatisticas basicas do modelo
# ##definindo prametros e variaveis globais (LORIEN)
# projectFolder = "J:/Pesquisadorxs/Anderson_Eduardo/high_quality_PA" #pasta do projeto
# envVarFolder = "J:/Pesquisadorxs/Anderson_Eduardo/dados_projeto/000" #pasta com as variaveis ambientais
# envVarPaths = list.files(path=envVarFolder, pattern='.asc', full.names=TRUE) #lista com os caminhos das camadas no sistema (comp.)
# AmSulShape = rgdal::readOGR("J:/Pesquisadorxs/Anderson_Eduardo/shapefiles/Am_Sul/borders.shp") #shape da America do Sul
# maxentFolder = 'C:/Users/WS/Documents/R/win-library/3.4/dismo/java/maxent.jar' #pasta para resultados do maxent
# ## spsTypes = c('spHW', 'spHD', 'spCD') #nomes das especies
# ## sdmTypes = 'real'
# sampleSizes = c(10,20,40,80,160)
# NumRep = 10 #numero de replicas (de cada cenario amostral)
# ##variaveis preditoras
# elevation = raster('J:/Pesquisadorxs/Anderson_Eduardo/DEM/DEM.tif')
# predictors = stack(envVarPaths,elevation) #predictors com todas as variaveis (presente)
# predictors = predictors[[c('bioclim_01','bioclim_12')]]
# predictors = stack(mask(x=predictors, mask=AmSulShape))
# crs(predictors) = CRS('+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0') #ajustando CRS
# Nsp = NumRep #numero de especies a serem criadas e trabalhadas igual ao numero de replicas
# statResultsSDMreal = data.frame() #tabela de estatisticas basicas do modelo
##definindo prametros e variaveis globais (PC nupeg)
projectFolder = "D:/Anderson_Eduardo/SDM com pseudoausencias melhoradas" #pasta do projeto
envVarFolder = "D:/Anderson_Eduardo/variaveis ambientais AmSul 120kyr/000" #pasta com as variaveis ambientais
envVarPaths = list.files(path=envVarFolder, pattern='.asc', full.names=TRUE) #lista com os caminhos das camadas no sistema (comp.)
AmSulShape = rgdal::readOGR("D:/Anderson_Eduardo/shapefiles/Am_Sul/borders.shp") #shape da America do Sul
maxentFolder = 'D:/Anderson_Eduardo/maxent/maxent.jar' #pasta para resultados do maxent
sampleSizes = c(20,40,80,160)
NumRep = 10 #numero de replicas (de cada cenario amostral)
##variaveis preditoras
#elevation = raster('J:/Pesquisadorxs/Anderson_Eduardo/DEM/DEM.tif')
predictors = stack(envVarPaths) #predictors com todas as variaveis (presente)
predictors = predictors[[c('bioclim_01','bioclim_12')]]
predictors = stack(mask(x=predictors, mask=AmSulShape))
crs(predictors) = CRS('+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0') #ajustando CRS
Nsp = NumRep #numero de especies a serem criadas e trabalhadas igual ao numero de replicas
statResultsSDMreal = data.frame() #tabela de estatisticas basicas do modelo
## funcoes autorais :-)
source('D:/Anderson_Eduardo/SDM com pseudoausencias melhoradas/makeSpecies.R')
source('D:/Anderson_Eduardo/rangeByAC.R')
source('D:/Anderson_Eduardo/SDM com pseudoausencias melhoradas/makeOutput.R')
source('D:/Anderson_Eduardo/SDM com pseudoausencias melhoradas/bestModel.R')
##definindo diretorio de trabalho (importante porque o biomod2 salva tudo automaticamente)
setwd(projectFolder)
##PARTE 2: modelando ausencias
for(i in 1:Nsp){
for(j in 1:length(sampleSizes)){
tryCatch({
##diretorio para o biomod2 salvar resultados para SDMreal
setwd(file.path(projectFolder,'ausencias_reais'))
##pontos de ocorrencia
SpDistAC = raster(paste(projectFolder,'/virtual species/sp',i,'.asc',sep=''))
values(SpDistAC)[values(SpDistAC)==0] = NA
occPoints = dismo::randomPoints(mask=SpDistAC, n=sampleSizes[j]) #sorteando pontos da distribuicao modelada
occPoints = data.frame(lon=occPoints[,1],lat=occPoints[,2], occ=1)
## amostrando pontos das areas de ausencia real
SpDistAC = raster(paste(projectFolder,'/virtual species/sp',i,'.asc',sep=''))
values(SpDistAC)[values(SpDistAC)==0] = NA
occAreaPts = as.data.frame(SpDistAC, xy=TRUE, na.rm=TRUE)
backgroundArea = raster(nrows=nrow(SpDistAC), ncols=ncol(SpDistAC),xmn=extent(SpDistAC)[1], xmx=extent(SpDistAC)[2], ymn = extent(SpDistAC)[3], ymx=extent(SpDistAC)[4] )
values(backgroundArea) = 0
backgroundArea = mask(x=backgroundArea, mask=AmSulShape)
absencesPts = dismo::randomPoints(mask=backgroundArea, n=1000, p=occAreaPts[,1:2], excludep=TRUE)
absencesPts = data.frame(absencesPts, occ=0)
names(absencesPts) = names(occPoints)
dataSet = data.frame(rbind(occPoints,absencesPts)) #planilha de dados no formato SWD
dataSetVars = extract(predictors, dataSet[,c('lon','lat')], na.rm=TRUE)
dataSet = data.frame(dataSet, dataSetVars)
##variaveis e parametros locais especificos para o biomod2
myRespName <- paste('sp',i,sep='') # nome do cenario atual (para biomod2)
myResp <- dataSet[,c('occ')] # variavel resposta (para biomod2)
myRespXY <- dataSet[,c('lon','lat')] # coordenadas associadas a variavel resposta (para biomod2)
myExpl = dataSet[,c('bioclim_01','bioclim_12')] #variavel preditora (para biomod2)
##ajuste de dados de entrada para biomod2
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = paste(myRespName,'_sample',sampleSizes[j],'_SDMimproved',sep=''))
## ##inspecionando o objeto gerado pela funcao do biomod2
## myBiomodData
## plot(myBiomodData)
##parametrizando os modelos
myBiomodOption <- BIOMOD_ModelingOptions(
MAXENT.Phillips = list(path_to_maxent.jar= maxentFolder,
linear=TRUE,
quadratic=TRUE,
product=FALSE,
threshold=FALSE,
hinge=FALSE,
maximumiterations=1000,
convergencethreshold=1.0E-5,
threads=2),
GLM = list( type = 'quadratic',
interaction.level = 0,
myFormula = NULL,
test = 'AIC',
family = binomial(link = 'logit'),
mustart = 0.5,
control = glm.control(epsilon = 1e-07, maxit = 100, trace = FALSE)),
GAM = list( algo = 'GAM_mgcv',
type = 's_smoother',
k = -1,
interaction.level = 0,
myFormula = NULL,
family = binomial(link = 'logit'),
method = 'GCV.Cp',
optimizer = c('outer','newton'),
select = FALSE,
knots = NULL,
paraPen = NULL,
control = list(nthreads = 1, irls.reg = 0, epsilon = 1e-07,
maxit = 200, trace = FALSE, mgcv.tol = 1e-07, mgcv.half = 15,
rank.tol = 1.49011611938477e-08,
nlm = list(ndigit=7, gradtol=1e-06, stepmax=2, steptol=1e-04, iterlim=200, check.analyticals=0),
optim = list(factr=1e+07),
newton = list(conv.tol=1e-06, maxNstep=5, maxSstep=2, maxHalf=30, use.svd=0),
outerPIsteps = 0, idLinksBases = TRUE, scalePenalty = TRUE, keepData = FALSE,
scale.est = 'fletcher', edge.correct = FALSE)),
MARS = list( type = 'simple',
interaction.level = 0,
myFormula = NULL,
nk = NULL,
penalty = 2,
thresh = 0.001,
nprune = NULL,
pmethod = 'backward'),
CTA = list( method = 'class',
parms = 'default',
cost = NULL,
control = list(xval = 5, minbucket = 5, minsplit = 5, cp = 0.001, maxdepth = 25)),
GBM = list( distribution = 'bernoulli',
n.trees = 2500,
interaction.depth = 7,
n.minobsinnode = 5,
shrinkage = 0.001,
bag.fraction = 0.5,
train.fraction = 1,
cv.folds = 3,
keep.data = FALSE,
verbose = FALSE,
perf.method = 'cv'),
RF = list( do.classif = TRUE,
ntree = 500,
mtry = 'default',
nodesize = 5,
maxnodes = NULL)
)
##rodando o(s) algoritmo(s) (i.e. SDMs)
myBiomodModelOut <- BIOMOD_Modeling(
data = myBiomodData,
models = c('MAXENT.Phillips', 'GLM', 'GAM', 'MARS', 'CTA', 'GBM', 'RF'),
models.options = myBiomodOption,
NbRunEval = 10,
DataSplit = 75,
models.eval.meth = c('TSS','ROC'),
do.full.models = FALSE,
modeling.id = paste(myRespName,'_sample',sampleSizes[j],'_SDMreal',sep=''))
##My output data
evaluationScores = get_evaluations(myBiomodModelOut)
statResultsSDMreal = makeOutput(evaluationScores, statResultsSDMreal, i, j, 'real', sampleSizes[j])
write.csv(statResultsSDMreal, file=paste(projectFolder,'/StatisticalResults_SDMreal.csv',sep=''), row.names=FALSE)
modelNames = bestModel(evaluationScores, statResultsSDMreal, myBiomodModelOut)
rm(evaluationScores)
##rodando algortmo de projecao (i.e. rodando a projecao)
myExpl = predictors
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = myExpl,
proj.name = paste('sp',i,'_sample',sampleSizes[j],'_SDMreal',sep=''),
selected.models = modelNames,
compress = 'TRUE',
build.clamping.mask = 'TRUE')
##rodando o algoritmo de consenso dos modelos (i.e. ensemble model)
myBiomodEM = BIOMOD_EnsembleModeling(
modeling.output = myBiomodModelOut,
chosen.models = modelNames)
##forecasting com o consenso dos algoritmos (i.e. ensemble projection)
myBiomodEF = BIOMOD_EnsembleForecasting(
EM.output = myBiomodEM,
binary.meth = c('TSS','ROC'),
projection.output = myBiomodProj)
xx = get_predictions(myBiomodEF)
plot(xx)
}, error=function(e){cat("ERROR :",conditionMessage(e), "/n")})
}
}
##PARTE 2: graficos e analise dos resultados
##ambiente para salvar os graficos
setwd("/home/anderson/Documentos/Projetos/Improved pseudo-absences/Resultados completos - versao final/graficos")
## statResultsSDMnormal = read.csv(paste(projectFolder,'/normal','.csv',sep=''), header=TRUE)
projectFolder = '/home/anderson/Documentos/Projetos/Improved pseudo-absences/Resultados completos - versao final'
##planilhas de dados
statResultsSDMimproved = read.csv(paste(projectFolder,'/StatisticalResults_SDMimproved','.csv',sep=''), header=TRUE)
statResultsSDMnormal = read.csv(paste(projectFolder,'/StatisticalResults_SDMnormal','.csv',sep=''), header=TRUE)
statResultsSDMreal = read.csv(paste(projectFolder,'/StatisticalResults_SDMreal','.csv',sep=''), header=TRUE)
## deixando so os cenarios que rodaram para os dois SDMs
dim(statResultsSDMnormal)
dim(statResultsSDMimproved)
dim(statResultsSDMreal)
##obs: usar o merge() para recortar a planilha que for maior
##exemplo:
##newTab=merge(tabB,tabA,by=c('infoA','infoB'))[,-3] ##a terceira coluna e da planilha menor
statResultsSDMnormal = merge(statResultsSDMimproved,
statResultsSDMnormal,
by=c('sampleSize','sp','model'))[,c('SDM.y',
'sampleSize',
'sp',
'model',
'meanTSSValue.y',
'meanTSSspecificity.y',
'maxTSSspecificity.y',
'bestModelTSS.y',
'TSSvalue_bestModel.y',
'meanAUCValue.y',
'meanAUCspecificity.y',
'maxAUCspecificity.y',
'bestModelAUC.y',
'AUCvalue_bestModel.y')]
##ajustando so nomes
names(statResultsSDMnormal) = names(statResultsSDMimproved)
statResultsSDMreal = merge(statResultsSDMimproved,
statResultsSDMreal,
by=c('sampleSize','sp','model'))[,c('SDM.y',
'sampleSize',
'sp',
'model',
'meanTSSValue.y',
'meanTSSspecificity.y',
'maxTSSspecificity.y',
'bestModelTSS.y',
'TSSvalue_bestModel.y',
'meanAUCValue.y',
'meanAUCspecificity.y',
'maxAUCspecificity.y',
'bestModelAUC.y',
'AUCvalue_bestModel.y')]
##ajustando so nomes
names(statResultsSDMreal) = names(statResultsSDMimproved)
##chacagem rapida
for(i in c('MAXENT.Phillips','CTA', 'GAM', 'GBM', 'GLM', 'MARS', 'RF')){
print(paste(i,
' ==>> SDMnormal = ', dim(statResultsSDMnormal[statResultsSDMnormal$model==i,])[1], ' linhas e ', dim(statResultsSDMnormal[statResultsSDMnormal$model==i,])[2], ' colunas',
' // ',
'SDMimproved = ', dim(statResultsSDMimproved[statResultsSDMimproved$model==i,])[1], ' linhas e ',dim(statResultsSDMimproved[statResultsSDMimproved$model==i,])[2], ' colunas',
sep=''))
}
## boxplot AUC - dados totais ##
jpeg(filename='boxplotAUC.jpeg')
boxplot(statResultsSDMreal$AUCvalue_bestModel,statResultsSDMnormal$AUCvalue_bestModel, statResultsSDMimproved$AUCvalue_bestModel, ylim=c(0,1), names=c('SDM real','SDM normal','SDM improved'), ylab='AUC')
dev.off()
jpeg(filename='densidadeAUC.jpeg')
plot(density(statResultsSDMnormal$AUCvalue_bestModel), ylim=c(0,40),col='blue', lwd=3, xlab='AUC values', main="")
lines(density(statResultsSDMimproved$AUCvalue_bestModel),col='red', lwd=3)
lines(density(statResultsSDMreal$AUCvalue_bestModel),col='black', lwd=3)
legend(x='topleft', legend=c('SDM normal', 'SDM improved', 'SDM real'), lty=1, lwd=3, col=c('blue', 'red', 'black'))
dev.off()
## boxplot TSS - dados totais ##
jpeg(filename='boxplotTSS.jpeg')
boxplot(statResultsSDMreal$TSSvalue_bestModel,statResultsSDMnormal$TSSvalue_bestModel, statResultsSDMimproved$TSSvalue_bestModel, ylim=c(0,1), names=c('SDM real','SDM normal','SDM improved'), ylab='TSS')
dev.off()
jpeg(filename='densidadeTSS.jpeg')
plot(density(statResultsSDMnormal$TSSvalue_bestModel), ylim=c(0,12), col='blue', lwd=3, xlab='TSS values', main="")
lines(density(statResultsSDMimproved$TSSvalue_bestModel),col='red', lwd=3)
lines(density(statResultsSDMreal$TSSvalue_bestModel),col='black', lwd=3)
legend(x='topleft', legend=c('SDM normal', 'SDM improved', 'SDM real'), lty=1, lwd=3, col=c('blue', 'red', 'black'))
dev.off()
## teste de significancia - dados totais ##
dados = rbind(statResultsSDMreal, statResultsSDMnormal, statResultsSDMimproved)
kruskal.test(AUCvalue_bestModel~SDM,data=dados) #p << 0.05
kruskal.test(TSSvalue_bestModel~SDM,data=dados) #p << 0.05
##analise post-hoc - teste de Dunn (par a par)
FSA::dunnTest(AUCvalue_bestModel~SDM,data=dados[,]) ##diferencas signif entre todos os pares
FSA::dunnTest(TSSvalue_bestModel~SDM,data=dados[,]) ##diferencas signif entre todos os pares
## AUC x sample size ##
dados = rbind(statResultsSDMreal, statResultsSDMnormal, statResultsSDMimproved)
jpeg(filename='AUC_&_sampleSize.jpeg')
boxplot(AUCvalue_bestModel~SDM*sampleSize,col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)),data=dados,frame.plot=TRUE,axes=FALSE,ylim=c(0.2,1), xlab='Sample sizes', ylab='AUC')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
dev.off()
## por algoritmo ##
jpeg(filename='AUC_&_sampleSize_por_algoritmo.jpeg', width=600, height=800)
par(mfrow=c(4,3), cex=1, mar=c(3,4,2,0.2))
##CTA
boxplot(AUCvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='CTA',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='', ylab='AUC', main='CTA')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##GBM
boxplot(AUCvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='GBM',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='', ylab='', main='GBM')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##RF
boxplot(AUCvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='RF',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='', ylab='', main='RF')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##MAXENT.Phillips
boxplot(AUCvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='MAXENT.Phillips',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='', ylab='AUC', main='Maxent')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##GLM
boxplot(AUCvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='GLM',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='Sample sizes', ylab='', main='GLM')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##GAM
boxplot(AUCvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='GAM',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='Sample sizes', ylab='', main='GAM')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##MARS
boxplot(AUCvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='MARS',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='Sample sizes', ylab='AUC', main='MARS')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.8)
dev.off()
## AUC x sample size - flavour 2 ##
jpeg(filename='AUC_&_sampleSize_boxplot2.jpeg', width=1200)
par(mfrow=c(1,3), cex=1.5, mar=c(4,4,3,0.5), lwd=2)
#SDM normal
stripchart( dados[dados$SDM=='normal',]$AUCvalue_bestModel ~ as.factor(dados[dados$SDM=='normal',]$sampleSize), ylim=c(0.4,1), xlim=c(0.5,4.5), xlab='Sample size', ylab='AUC', main='SDM normal', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='normal',]$AUCvalue_bestModel ~ dados[dados$SDM=='normal',]$sampleSize, col=rgb(1,1,1,0), border='black',pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
#SDM real
stripchart( dados[dados$SDM=='real',]$AUCvalue_bestModel ~ as.factor(dados[dados$SDM=='real',]$sampleSize), ylim=c(0.4,1), xlim=c(0.5,4.5), xlab='Sample size', ylab='', main='SDM real', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='real',]$AUCvalue_bestModel ~ dados[dados$SDM=='real',]$sampleSize, col=rgb(1,1,1,0), border='black', pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
#SDM improved
stripchart( dados[dados$SDM=='improved',]$AUCvalue_bestModel ~ as.factor(dados[dados$SDM=='improved',]$sampleSize), ylim=c(0.4,1), xlim=c(0.5,4.5), xlab='Sample size', ylab='', main='SDM improved', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='improved',]$AUCvalue_bestModel ~ dados[dados$SDM=='improved',]$sampleSize, col=rgb(1,1,1,0), border='black', pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
dev.off()
## graficos de densidade ##
jpeg(filename='AUC_&_sampleSize_densityPlot.jpeg', width=600, height=600)
par(mfrow=c(2,2), cex=1.1, mar=c(4,4,3,0.5), lwd=2)
#samplesize 20
plot( density(dados[dados$SDM=='real' & dados$sampleSize==20,]$AUCvalue_bestModel), col='black', xlab='', main='Sample size 20')
lines( density(dados[dados$SDM=='improved' & dados$sampleSize==20,]$AUCvalue_bestModel), col='red')
lines( density(dados[dados$SDM=='normal' & dados$sampleSize==20,]$AUCvalue_bestModel) , col='blue')
legend('topleft',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.8)
#samplesize 40
plot( density(dados[dados$SDM=='real' & dados$sampleSize==40,]$AUCvalue_bestModel), ylim=c(0,14), col='black', xlab='', ylab='', main='Sample size 40')
lines( density(dados[dados$SDM=='improved' & dados$sampleSize==40,]$AUCvalue_bestModel), col='red')
lines( density(dados[dados$SDM=='normal' & dados$sampleSize==40,]$AUCvalue_bestModel) , col='blue')
legend('topleft',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.8)
#samplesize 80
plot( density(dados[dados$SDM=='real' & dados$sampleSize==80,]$AUCvalue_bestModel), ylim=c(0,100), col='black', xlab='AUC', main='Sample size 80')
lines( density(dados[dados$SDM=='improved' & dados$sampleSize==80,]$AUCvalue_bestModel), col='red')
lines( density(dados[dados$SDM=='normal' & dados$sampleSize==80,]$AUCvalue_bestModel) , col='blue')
legend('topleft',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.8)
#samplesize 160
plot( density(dados[dados$SDM=='real' & dados$sampleSize==160,]$AUCvalue_bestModel), ylim=c(0,40), col='black', xlab='AUC', ylab='', main='Sample size 160')
lines( density(dados[dados$SDM=='improved' & dados$sampleSize==160,]$AUCvalue_bestModel), col='red')
lines( density(dados[dados$SDM=='normal' & dados$sampleSize==160,]$AUCvalue_bestModel) , col='blue')
legend('topleft',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.8)
dev.off()
##teste AUC x sample size
##sample size 20
kruskal.test(AUCvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==20),]) #resultado: p<0.05
FSA::dunnTest(AUCvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==20),]) #diferenca sig entre todos os pares
##sample size 40
kruskal.test(AUCvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==40),]) #resultado: p<0.05
FSA::dunnTest(AUCvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==40),]) #nao signf. entre normal e real
##sample size 80
kruskal.test(AUCvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==80),]) #resultado: p<0.05
FSA::dunnTest(AUCvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==80),]) #diferenca sig entre todos os pares
##sample size 20
kruskal.test(AUCvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==160),]) #resultado: p<0.05
FSA::dunnTest(AUCvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==160),]) #diferenca sig entre todos os pares
##SDMnormal
kruskal.test(AUCvalue_bestModel ~ sampleSize, data=dados[which(dados$SDM=='normal'),]) #resultado: p>0.05
FSA::dunnTest(AUCvalue_bestModel ~ sampleSize, data=dados[which(dados$SDM=='normal'),]) #nenhuma diferenca sig entre os pares
##SDMimproved
kruskal.test(AUCvalue_bestModel ~ sampleSize, data=dados[which(dados$SDM=='improved'),]) #resultado: p<0.05
FSA::dunnTest(AUCvalue_bestModel ~ sampleSize, data=dados[which(dados$SDM=='improved'),])
##SDMreal
kruskal.test(AUCvalue_bestModel ~ sampleSize, data=dados[which(dados$SDM=='real'),]) #resultado: p>0.05
FSA::dunnTest(AUCvalue_bestModel ~ sampleSize, data=dados[which(dados$SDM=='real'),])
##TSS x sample size
dados = rbind(statResultsSDMreal, statResultsSDMnormal, statResultsSDMimproved)
## dados totais
jpeg(filename='TSS_&_sampleSize.jpeg')
boxplot(TSSvalue_bestModel~SDM*sampleSize,col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)),data=dados,frame.plot=TRUE,axes=FALSE,ylim=c(0.2,1), xlab='Sample sizes', ylab='TSS')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
dev.off()
##por algoritmo
jpeg(filename='TSS_&_sampleSize_por_algoritmo.jpeg', width=600, height=800)
par(mfrow=c(4,3), cex=1, mar=c(3,4,2,0.2))
##CTA
boxplot(TSSvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='CTA',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='', ylab='TSS', main='CTA')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##GBM
boxplot(TSSvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='GBM',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='', ylab='', main='GBM')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##RF
boxplot(TSSvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='RF',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='', ylab='', main='RF')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##MAXENT.Phillips
boxplot(TSSvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='MAXENT.Phillips',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='', ylab='TSS', main='Maxent')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##GLM
boxplot(TSSvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='GLM',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='Sample sizes', ylab='', main='GLM')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##GAM
boxplot(TSSvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='GAM',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='Sample sizes', ylab='', main='GAM')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
#legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.7)
##MARS
boxplot(TSSvalue_bestModel~SDM*sampleSize, col=c(rgb(0,0,0,0.5),rgb(0,0,1,0.5),rgb(1,0,0,0.5)), data=dados[dados$model=='MARS',], frame.plot=TRUE, axes=FALSE, ylim=c(0.2,1), xlab='Sample sizes', ylab='TSS', main='MARS')
axis(1,at=c(2,5,8,11),labels=c(20,40,60,160),cex.axis=0.7)
axis(2,at=c(0.2,0.4,0.6,0.8,1.0),labels=c(0.2,0.4,0.6,0.8,1.0),cex.axis=0.7)
abline(v=3.5); abline(v=6.5); abline(v=9.5)
legend('bottomright',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.8)
dev.off()
##TSS x sample size - flavour 2
jpeg(filename='TSS_&_sampleSize_boxplot2.jpeg', width=1200)
par(mfrow=c(1,3), cex=1.5, mar=c(4,4,3,0.5), lwd=2)
#SDM normal
stripchart( dados[dados$SDM=='normal',]$TSSvalue_bestModel ~ as.factor(dados[dados$SDM=='normal',]$sampleSize), ylim=c(0,1), xlim=c(0.5,4.5), xlab='Sample size', ylab='TSS', main='SDM normal', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='normal',]$TSSvalue_bestModel ~ dados[dados$SDM=='normal',]$sampleSize, col=rgb(1,1,1,0), border='black',pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
#SDM real
stripchart( dados[dados$SDM=='real',]$TSSvalue_bestModel ~ as.factor(dados[dados$SDM=='real',]$sampleSize), ylim=c(0,1), xlim=c(0.5,4.5), xlab='Sample size', ylab='', main='SDM real', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='real',]$TSSvalue_bestModel ~ dados[dados$SDM=='real',]$sampleSize, col=rgb(1,1,1,0), border='black', pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
#SDM improved
stripchart( dados[dados$SDM=='improved',]$TSSvalue_bestModel ~ as.factor(dados[dados$SDM=='improved',]$sampleSize), ylim=c(0,1), xlim=c(0.5,4.5), xlab='Sample size', ylab='', main='SDM improved', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='improved',]$TSSvalue_bestModel ~ dados[dados$SDM=='improved',]$sampleSize, col=rgb(1,1,1,0), border='black', pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
dev.off()
##graficos de densidade
jpeg(filename='TSS_&_sampleSize_densityPlot.jpeg', width=600, height=600)
par(mfrow=c(2,2), cex=1.1, mar=c(4,4,3,0.5), lwd=2)
#samplesize 20
plot( density(dados[dados$SDM=='real' & dados$sampleSize==20,]$TSSvalue_bestModel), col='black', xlab='', main='Sample size 20')
lines( density(dados[dados$SDM=='improved' & dados$sampleSize==20,]$TSSvalue_bestModel), col='red')
lines( density(dados[dados$SDM=='normal' & dados$sampleSize==20,]$TSSvalue_bestModel) , col='blue')
legend('topleft',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.8)
#samplesize 40
plot( density(dados[dados$SDM=='real' & dados$sampleSize==40,]$TSSvalue_bestModel), ylim=c(0,6), col='black', xlab='', ylab='', main='Sample size 40')
lines( density(dados[dados$SDM=='improved' & dados$sampleSize==40,]$TSSvalue_bestModel), col='red')
lines( density(dados[dados$SDM=='normal' & dados$sampleSize==40,]$TSSvalue_bestModel) , col='blue')
legend('topleft',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.8)
#samplesize 80
plot( density(dados[dados$SDM=='real' & dados$sampleSize==80,]$TSSvalue_bestModel), ylim=c(0,19), col='black', xlab='TSS', main='Sample size 80')
lines( density(dados[dados$SDM=='improved' & dados$sampleSize==80,]$TSSvalue_bestModel), col='red')
lines( density(dados[dados$SDM=='normal' & dados$sampleSize==80,]$TSSvalue_bestModel) , col='blue')
legend('topleft',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.8)
#samplesize 160
plot( density(dados[dados$SDM=='real' & dados$sampleSize==160,]$TSSvalue_bestModel), ylim=c(0,8), col='black', xlab='TSS', ylab='', main='Sample size 160')
lines( density(dados[dados$SDM=='improved' & dados$sampleSize==160,]$TSSvalue_bestModel), col='red')
lines( density(dados[dados$SDM=='normal' & dados$sampleSize==160,]$TSSvalue_bestModel) , col='blue')
legend('topleft',legend=c('SDM real','SDM normal','SDM improved'),pch=15,col=c('black','blue','red'), cex=0.8)
dev.off()
##teste TSS x sample size
##sample size 20
kruskal.test(TSSvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==20),]) #resultado: p<0.05
FSA::dunnTest(TSSvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==20),]) #diferenca sig entre todos os pares
##sample size 40
kruskal.test(TSSvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==40),]) #resultado: p<0.05
FSA::dunnTest(AUCvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==40),]) #diferenca sig entre o SDM improved e os outros
##sample size 80
kruskal.test(TSSvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==80),]) #resultado: p<0.05
FSA::dunnTest(AUCvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==80),]) #diferenca sig entre todos os pares
##sample size 20
kruskal.test(TSSvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==160),]) #resultado: p<0.05
FSA::dunnTest(TSSvalue_bestModel ~ SDM, data=dados[which(dados$sampleSize==160),]) #diferenca sig entre todos os pares
## TSS e AUC por algoritmo
##AUC boxplot
jpeg(filename='AUC_por_algoritmo_boxplot.jpeg', width=800)
par(mfrow=c(1,3), cex=1.3, las=2, mar=c(8,4,4,1))
## SDM normal
stripchart( dados[dados$SDM=='normal',]$AUCvalue_bestModel ~ as.factor(dados[dados$SDM=='normal',]$model), ylim=c(0,1), xlim=c(0.5,7.5), xlab='', ylab='AUC', main='SDM normal', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='normal',]$AUCvalue_bestModel ~ as.factor(dados[dados$SDM=='normal',]$model), col=rgb(1,1,1,0), border='black', pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
##SDMreal
stripchart( dados[dados$SDM=='real',]$AUCvalue_bestModel ~ as.factor(dados[dados$SDM=='real',]$model), ylim=c(0,1), xlim=c(0.5,7.5), xlab='', ylab='', main='SDM real', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='real',]$AUCvalue_bestModel ~ as.factor(dados[dados$SDM=='real',]$model), col=rgb(1,1,1,0), border='black', pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
##SDMimproved
stripchart( dados[dados$SDM=='improved',]$AUCvalue_bestModel ~ as.factor(dados[dados$SDM=='improved',]$model), ylim=c(0,1), xlim=c(0.5,7.5), xlab='', ylab='', main='SDM improved', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='improved',]$AUCvalue_bestModel ~ as.factor(dados[dados$SDM=='improved',]$model), col=rgb(1,1,1,0), border='black', pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
dev.off()
##TSS boxplot
jpeg(filename='TSS_por_algoritmo_boxplot.jpeg', width=800)
par(mfrow=c(1,3), cex=1.3, las=2, mar=c(8,4,4,1))
## SDM normal
stripchart( dados[dados$SDM=='normal',]$TSSvalue_bestModel ~ as.factor(dados[dados$SDM=='normal',]$model), ylim=c(0,1), xlim=c(0.5,7.5), xlab='', ylab='TSS', main='SDM normal', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='normal',]$TSSvalue_bestModel ~ as.factor(dados[dados$SDM=='normal',]$model), col=rgb(1,1,1,0), border='black', pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
##SDMreal
stripchart( dados[dados$SDM=='real',]$TSSvalue_bestModel ~ as.factor(dados[dados$SDM=='real',]$model), ylim=c(0,1), xlim=c(0.5,7.5), xlab='', ylab='', main='SDM real', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='real',]$TSSvalue_bestModel ~ as.factor(dados[dados$SDM=='real',]$model), col=rgb(1,1,1,0), border='black', pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
##SDMimproved
stripchart( dados[dados$SDM=='improved',]$TSSvalue_bestModel ~ as.factor(dados[dados$SDM=='improved',]$model), ylim=c(0,1), xlim=c(0.5,7.5), xlab='', ylab='', main='SDM improved', col=rgb(0.5,0.5,0.5,0.5), pch=1, cex=1.5, vertical=TRUE)
boxplot( dados[dados$SDM=='improved',]$TSSvalue_bestModel ~ as.factor(dados[dados$SDM=='improved',]$model), col=rgb(1,1,1,0), border='black', pch=19, cex=0.5, lwd=1, outpch=NA, notch=FALSE, boxlwd=2, add=TRUE)
dev.off()
##AUC antes e depois do HCPA
BeforeAfterData = merge(statResultsSDMnormal, statResultsSDMimproved, by=c('sampleSize','sp','model'))[,c('sampleSize','sp','model', 'AUCvalue_bestModel.x','AUCvalue_bestModel.y')]
names(BeforeAfterData) = c("sampleSize", "sp", "model","SDMnormal", "SDMimproved")
jpeg(filename='AUC_beforeXafter_HCPA.jpeg', width=1000, height=1400)
par(mfrow=c(4,3), cex=1.3, mar=c(3,5,2,1))
## CTA ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(AUC)', main='CTA', xlim=c(0.5,2.5), ylim=c(-0.55,0.08), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])), log(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])), log(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMimproved),
angle=15, length=0.2)
##
tendencyCTA = lm(log(c(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])) ) )
abline(tendencyCTA, col='red', lty=2, lwd=2.5)
## MARS ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(AUC)', main='MARS', xlim=c(0.5,2.5), ylim=c(-0.45,0.05), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])), log(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])), log(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMimproved),
angle=15, length=0.2)
##
tendencyMARS = lm(log(c(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])) ) )
abline(tendencyMARS, col='red', lty=2, lwd=2.5)
## GAM ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(AUC)', main='GAM', xlim=c(0.5,2.5), ylim=c(-0.4,0.03), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])), log(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])), log(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMimproved),
angle=15, length=0.2)
##
tendencyGAM = lm(log(c(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])) ) )
abline(tendencyGAM, col='red', lty=2, lwd=2.5)
## MAXENT ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(AUC)', main='Maxent', xlim=c(0.5,2.5), ylim=c(-0.355,0.03), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])), log(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])), log(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMimproved),
angle=15, length=0.2)
##
tendencyMAX = lm(log(c(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])) ) )
abline(tendencyMAX, col='red', lty=2, lwd=2.5)
## GBM ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(AUC)', main='GBM', xlim=c(0.5,2.5), ylim=c(-0.45,0.03), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])), log(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])), log(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMimproved),
angle=15, length=0.2)
##
tendencyGBM = lm(log(c(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])) ) )
abline(tendencyGBM, col='red', lty=2, lwd=2.5)
## RF ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='RF',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(AUC)', main='RF', xlim=c(0.5,2.5), ylim=c(-0.8,0.07), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])), log(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])), log(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMimproved),
angle=15, length=0.2)
##
tendencyRF = lm(log(c(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='RF',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])) ) )
abline(tendencyRF, col='red', lty=2, lwd=2.5)
## GLM ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(AUC)', main='GLM', xlim=c(0.5,2.5), ylim=c(-0.17,0.01), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])), log(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])), log(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMimproved),
angle=15, length=0.2)
##
tendencyGLM = lm(log(c(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])) ) )
abline(tendencyGLM, col='red', lty=2, lwd=2.5)
##
dev.off()
## tabela com a 'forca' do HCPA para cada algoritmo
tabBetaHCPA = data.frame(
Algorithm = c('CTA','MARS','Maxent','GAM','GBM','RF','GLM'),
HCPA_effect = as.numeric(c( coef(tendencyCTA)[2],coef(tendencyMARS)[2],coef(tendencyMAX)[2],coef(tendencyGAM)[2],coef(tendencyGBM)[2], coef(tendencyRF)[2], coef(tendencyGLM)[2]))
)
write.csv(tabBetaHCPA, paste(projectFolder,'/tabBetaHCPA.csv',row.names=FALSE,sep=''))
tabBetaHCPA = tabBetaHCPA[order(tabBetaHCPA$HCPA_effect),]
jpeg(filename='HCPAeffects.jpeg')
barplot(tabBetaHCPA$HCPA_effect, names=tabBetaHCPA$Algorithm, xlab='Algorithm', ylab='HCPA effect')
dev.off()
##TSS antes e depois do HCPA
BeforeAfterData = merge(statResultsSDMnormal, statResultsSDMimproved, by=c('sampleSize','sp','model'))[,c('sampleSize','sp','model', 'TSSvalue_bestModel.x','TSSvalue_bestModel.y')]
names(BeforeAfterData) = c("sampleSize", "sp", "model","SDMnormal", "SDMimproved")
jpeg(filename='TSS_beforeXafter_HCPA.jpeg', width=1000, height=1400)
par(mfrow=c(4,3), cex=1.3, mar=c(3,5,2,1))
## CTA ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(TSS)', main='CTA', xlim=c(0.5,2.5), ylim=c(-1.7,0.08), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])), log(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])), log(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMimproved),
angle=15, length=0.2)
##
tendency = lm(log(c(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='CTA',])) ) )
abline(tendency, col='red', lty=2, lwd=2.5)
## MARS ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(TSS)', main='MARS', xlim=c(0.5,2.5), ylim=c(-1.1,0.05), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])), log(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])), log(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMimproved),
angle=15, length=0.2)
##
tendency = lm(log(c(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MARS',])) ) )
abline(tendency, col='red', lty=2, lwd=2.5)
## GAM ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(TSS)', main='GAM', xlim=c(0.5,2.5), ylim=c(-1,0.03), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])), log(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])), log(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMimproved),
angle=15, length=0.2)
##
tendency = lm(log(c(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GAM',])) ) )
abline(tendency, col='red', lty=2, lwd=2.5)
## MAXENT ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(TSS)', main='Maxent', xlim=c(0.5,2.5), ylim=c(-0.85,0.03), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])), log(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])), log(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMimproved),
angle=15, length=0.2)
##
tendency = lm(log(c(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',])) ) )
abline(tendency, col='red', lty=2, lwd=2.5)
## GBM ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(TSS)', main='GBM', xlim=c(0.5,2.5), ylim=c(-0.7,0.03), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])), log(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])), log(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMimproved),
angle=15, length=0.2)
##
tendency = lm(log(c(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GBM',])) ) )
abline(tendency, col='red', lty=2, lwd=2.5)
## RF ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='RF',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(TSS)', main='RF', xlim=c(0.5,2.5), ylim=c(-2,0.07), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])), log(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])), log(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMimproved),
angle=15, length=0.2)
##
tendency = lm(log(c(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='RF',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='RF',])) ) )
abline(tendency, col='red', lty=2, lwd=2.5)
## GLM ##
stripchart( log(c(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMimproved)) ~
c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])) ),
vertical=TRUE ,pch=19, cex=2, col=rgb(0,0,0,0.5), xlab='', ylab='log(TSS)', main='GLM', xlim=c(0.5,2.5), ylim=c(-0.51,0.03), group.names=c('Before','After') )
##
arrows(rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])), log(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMnormal),
rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])), log(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMimproved),
angle=15, length=0.2)
##
tendency = lm(log(c(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMnormal, BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMimproved))
~c( rep(1, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])), rep(2, nrow(BeforeAfterData[BeforeAfterData$model=='GLM',])) ) )
abline(tendency, col='red', lty=2, lwd=2.5)
##
dev.off()
## ranking dos modelos - com AUC
BeforeAfterData = merge(statResultsSDMnormal, statResultsSDMimproved, by=c('sampleSize','sp','model'))[,c('sampleSize','sp','model', 'AUCvalue_bestModel.x','AUCvalue_bestModel.y')]
names(BeforeAfterData) = c("sampleSize", "sp", "model","SDMnormal", "SDMimproved")
rankingAUC = data.frame(
SDM = c('normal', 'improved'),
CTA = c(median(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMimproved)),
MARS = c(median(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMimproved)),
GAM = c(median(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMimproved)),
Maxent = c(median(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMimproved)),
GBM = c(median(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMimproved)),
RF = c(median(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMimproved)),
GLM = c(median(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMimproved))
)
## ranking dos modelos - com TSS
BeforeAfterData = merge(statResultsSDMnormal, statResultsSDMimproved, by=c('sampleSize','sp','model'))[,c('sampleSize','sp','model', 'TSSvalue_bestModel.x','TSSvalue_bestModel.y')]
names(BeforeAfterData) = c("sampleSize", "sp", "model","SDMnormal", "SDMimproved")
rankingTSS = data.frame(
SDM = c('normal', 'improved'),
CTA = c(median(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMimproved)),
MARS = c(median(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMimproved)),
GAM = c(median(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMimproved)),
Maxent = c(median(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMimproved)),
GBM = c(median(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMimproved)),
RF = c(median(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMimproved)),
GLM = c(median(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMnormal),
median(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMimproved))
)
## teste TSS entre modelos ##
kruskal.test(TSSvalue_bestModel ~ model, data=statResultsSDMnormal) #resultado: p>0.05
FSA::dunnTest(TSSvalue_bestModel ~ model, data=statResultsSDMnormal, method="bh")
kruskal.test(TSSvalue_bestModel ~ model, data=statResultsSDMimproved) #resultado: p<<0.05
##analise post-hoc - teste de Dunn (par a par)
FSA::dunnTest(TSSvalue_bestModel ~ model, data=statResultsSDMimproved, method="bh")
## teste before-after (com TSS) ##
BeforeAfterData = merge(statResultsSDMnormal, statResultsSDMimproved, by=c('sampleSize','sp','model'))[,c('sampleSize','sp','model', 'TSSvalue_bestModel.x','TSSvalue_bestModel.y')]
names(BeforeAfterData) = c("sampleSize", "sp", "model","SDMnormal", "SDMimproved")
##cta
wilcox.test(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMnormal,
BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMimproved, paired=TRUE) # where y1 and y2 are numeric
##mars
wilcox.test(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMnormal,
BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMimproved, paired=TRUE) # where y1 and y2 are numeric
##gam
wilcox.test(BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMnormal,
BeforeAfterData[BeforeAfterData$model=='GAM',]$SDMimproved, paired=TRUE) # where y1 and y2 are numeric
##maxent
wilcox.test(BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMnormal,
BeforeAfterData[BeforeAfterData$model=='MAXENT.Phillips',]$SDMimproved, paired=TRUE) # where y1 and y2 are numeric
##gbm
wilcox.test(BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMnormal,
BeforeAfterData[BeforeAfterData$model=='GBM',]$SDMimproved, paired=TRUE) # where y1 and y2 are numeric
##rf
wilcox.test(BeforeAfterData[BeforeAfterData$model=='RF',]$SDMnormal,
BeforeAfterData[BeforeAfterData$model=='RF',]$SDMimproved, paired=TRUE) # where y1 and y2 are numeric
##GLM
wilcox.test(BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMnormal,
BeforeAfterData[BeforeAfterData$model=='GLM',]$SDMimproved, paired=TRUE) # where y1 and y2 are numeric
## teste AUC entre modleos ##
kruskal.test(AUCvalue_bestModel ~ model, data=statResultsSDMnormal) #resultado: p<0.05
##analise post-hoc - teste de Dunn (par a par)
FSA::dunnTest(AUCvalue_bestModel ~ model, data=statResultsSDMnormal, method="bh")
kruskal.test(AUCvalue_bestModel ~ model, data=statResultsSDMimproved) #resultado: p<<0.05
##analise post-hoc - teste de Dunn (par a par)
FSA::dunnTest(AUCvalue_bestModel ~ model, data=statResultsSDMimproved, method="bh")
## teste before-after (com AUC) ##
BeforeAfterData = merge(statResultsSDMnormal, statResultsSDMimproved, by=c('sampleSize','sp','model'))[,c('sampleSize','sp','model', 'AUCvalue_bestModel.x','AUCvalue_bestModel.y')]
names(BeforeAfterData) = c("sampleSize", "sp", "model","SDMnormal", "SDMimproved")
##cta
wilcox.test(BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMnormal,
BeforeAfterData[BeforeAfterData$model=='CTA',]$SDMimproved, paired=TRUE) # where y1 and y2 are numeric
##mars
wilcox.test(BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMnormal,
BeforeAfterData[BeforeAfterData$model=='MARS',]$SDMimproved, paired=TRUE) # where y1 and y2 are numeric