foreachとdoparはオブジェクトを作成せず、NULLを返す

Question

私はいくつかの空間解析にMaxentを使用しています。私はリストに集めた出力が多い長いスクリプトを持っています。それはラスタースタックのforループと低解像度の気候予測子でうまく動作します（私のコアi5、6GBノートブック）。しかし、私はラスタの高解像度セットを使用する必要があり、すべての問題はこの問題から来ています。 16コア、32ギガバイトの仮想マシンを使用しても、処理は遅いですし、3日後にはメモリが足りず、ループ（約92種）で約50回転後に実行が終了します。私はこのスクリプトを改善するために、ガベージを収集してメモリを清掃し、doParallelを使用しています。高解像度だから

を予測因子で新しいスクリプトがきれいに低解像度の予測因子で実行された後、私はそれを試してみましょう、私の代わりにforのforeachを使用するために私のスクリプトを変更し、と%dopar%

しかし、これまでのところ私は、結果としてこれを取得しています：私は同じ問題について別の質問を見た

[[1]] 
NULL 

[[2]] 
NULL 

[[3]] 
NULL 

[[4]] 
NULL 

が、非常に簡単な解決策が必要な男が...私のためには適用されませんので、任意のヒントは非常に歓迎されている

#install.packages("dismo") 
library(dismo) 
#install.packages("scales") 
library(scales) 
#install.packages("rgdal") 
library(rgdal) 
#install.packages("rgeos") 
library(rgeos) 
#install.packages("rJava") 
library(rJava) 
#install.packages("foreach") 
library(foreach) 
#install.packages("doParallel") 
library(doParallel) 

#Colors to use in the plots 
MyRbw2<-c('#f4f4f4','#3288bd','#66c2a5','#e6f598','#fee08b','#f46d43','#9e0142') 
colfunc_myrbw2<-colorRampPalette(MyRbw2) 

#Create empty lists to recieve outputs 
xm_list<-list() 
xm_spc_list<-list() 
e_spc_list<-list() 
px_spc_list<-list() 
tr_spc_list<-list() 
spc_pol1<-list() 
spc_pol5<-list() 
tr<-list() 


#Create empty data frame to recieve treshold values for each species 
tr_df<-data.frame(matrix(NA, nrow=92, ncol=7)) 
tr_df[,1]<-as.character(tree_list) 
names(tr_df)<- c('spp',"kappa","spec_sens","no_omission","prevalence","equal_sens_spec","sensitivity") 


# Assigning objects to run Maxent 
data_points <- tree_cd_points # this is a list with SpatialPoints for 92 species 
data_list <- tree_list # list with the species names 
counts_data<- counts_tree_cd # number of points for each species 
predictors2<-predictors_low # rasterStack of Bioclim layers (climatic variables), low resolution 

#Stablishing extent for Maxent predictions 
xmin=-120; xmax=-35; ymin2=-40; ymax=35 
limits2 <- c(xmin, xmax, ymin2, ymax) 

# Making the cluster for doParallel 
cores<-detectCores() # I have 16 
cl <- makeCluster(cores[1]-1) #not to overload your computer 
registerDoParallel(cl) 

#Just to keep track of time 
ptime1 <- proc.time() 



pdf("C:/Users/thai/Desktop/Ecologicos/w2/SpDistModel/SEM9/spp/treesp_maxent_20170823.pdf", 
    paper = "letter", height = 11, width=8,5, pointsize=12,pagecentre = TRUE) 
#I have 92 species, but I'll run just the first 4 to test 
foreach(i=1:4, .packages=c("dismo","scales","rgdal","rgeos","rJava")) %dopar% { #Runs only species with 5 or more points to avoid maxent problems 

    if (counts_data$n[i]>4) { #If the species has more than 4 occurrence points, run maxent 
    tryCatch({ #makes the loop go on despite errors 


     #Sets train, test and total points for Maxent 
     group <- kfold(x=data_points[[i]], 5) 
     pres_train<- data_points[[i]][group != 1, ] 
     pres_test <- data_points[[i]][group == 1, ] 
     spoints<- data_points[[i]] 

     #Sets background points for Maxent 
     backg <- randomPoints(predictors2, n=20000, ext=limits2, extf = 1.25) 
     colnames(backg) = c('lon', 'lat') 
     group <- kfold(backg, 5) 
     backg_train <- backg[group != 1, ] 
     backg_test <- backg[group == 1, ] 



     #The maxent itself (put the xm in the empty list that I created earlier to store all xms) 
     xm_spc_list[[i]] <- maxent(x=predictors2, p=spoints, a=backg , 
        factors='ecoreg', 
        args=c('visible=true', 
          'betamultiplier=1', 
          'randomtestpoints=20', 
          'randomseed=true', 
          'linear=true', 
          'quadratic=true', 
          'product=true', 
          'hinge=true', 
          'threads=4', 
          'responsecurves=true', 
          'jackknife=true', 
          'removeduplicates=false', 
          'extrapolate=true', 
          'pictures=true', 
          'cache=true', 
          'maximumiterations=5000', 
          'askoverwrite=false'), 
        path=paste0("C:/Users/thai/Desktop/Ecologicos/w2/SpDistModel/SEM9/spp/xm/",data_list[i]), overwrite=TRUE) 


     par(mfrow=c(1,1),mar = c(2,2, 2, 2)) 
     plot(xm_spc_list[[i]], main=paste(data_list[i])) 
     response(xm_spc_list[[i]]) 


     #Evaluating how good is the model and putting the evaluation values in a list 
     e_spc_list[[i]] <- evaluate(pres_test, backg_test, xm_spc_list[[i]], predictors2) 



     #Predicting the climatic envelopes and Sending to a list os predictions 
     px_spc_list[[i]] <- predict(predictors2, xm_spc_list[[i]], ext=limits2, progress='text', 
        filename=paste0("C:/Users/thai/Desktop/Ecologicos/w2/SpDistModel/SEM9/spp/xm/",data_list[i],"/",gsub('\\s+', '_', data_list[i]),"_pred.grd"), overwrite=TRUE) 



     tr_df[i,2:7]<-threshold(e_spc_list[[i]]) 
     tr[[i]]<-threshold(e_spc_list[[i]], 'spec_sens') 


     #Pol 1 will be the regular polygon, default treshold 
     spc_pol1[[i]] <- rasterToPolygons(px_spc_list[[i]]>tr[[i]],function(x) x == 1,dissolve=T) 
     writeOGR(obj = spc_pol1[[i]], dsn = paste0("C:/Users/thai/Desktop/Ecologicos/w2/SpDistModel/SEM9/spp/xm1/",data_list[i]), driver = "ESRI Shapefile", 
       layer = paste0(gsub('\\s+', '_', data_list[i]),"_pol"), overwrite_layer = TRUE) 




     #Pol 5 will be a 100km^2 circle around the occurrence points 
     circ <- circles(spoints, d=5642,lonlat=TRUE) 
     circ <- circ@polygons 
     crs(circ)<-crs(wrld_cropped) 
     circ <- gIntersection(wrld_cropped, circ, byid = TRUE, drop_lower_td = TRUE) 

     #To write de polygon to a file, the function writeOGR needs an object SPDF, so... 
     #Getting Polygon IDs 
     circ_df<- as.data.frame(sapply(slot(circ, "polygons"), function(x) slot(x, "ID"))) 
     #Making the IDs row names 
     row.names(circ_df) <- sapply(slot(circ, "polygons"), function(x) slot(x, "ID")) 
     # Make spatial polygon data frame 
     circ_SPDF <- SpatialPolygonsDataFrame(circ, data =circ_df) 

     #Save the polygon, finally 
     writeOGR(obj = circ_SPDF, dsn = paste0("C:/Users/thai/Desktop/Ecologicos/w2/SpDistModel/SEM9/spp/xm5/",data_list[i]), driver = "ESRI Shapefile", 
       layer = paste0(gsub('\\s+', '_', data_list[i]),"_pol"), overwrite_layer = TRUE) 

     spc_pol5[[i]]<-circ_SPDF 

     #Now the plots 
     par(mfrow=c(2,3),mar = c(2,1, 1, 1)) 

     plot(px_spc_list[[i]], axes=FALSE, legend=TRUE, legend.shrink=1, col=colfunc_myrbw2(20), main=paste((data_list[i]),' - Maxent')) 
     plot(wrld_cropped,add=TRUE, border='dark grey',axes=FALSE) 
     points(data_points[[i]], pch=21,col="white", bg='hotpink', lwd=0.5, cex=0.7) 

     plot(wrld_cropped, border='dark grey', col="#f9f9f9",axes=FALSE, main='px>tr') 
     plot(spc_pol1[[i]] , main=paste((data_list[i]),' - Range'), add=TRUE, col=alpha("green3",0.8),border=alpha("green3",0.8),axes=FALSE) 
     points(data_points[[i]], pch="°",col="black", cex=0.7) 

     plot(wrld_cropped, border='dark grey', col="#f9f9f9",axes=FALSE, main=paste(data_list[i],"circles")) 
     plot(circ, add=TRUE, col=alpha("green3",0.8),border=alpha("green3",0.8)) 
    }, error=function(e){cat("Warning message:",conditionMessage(e), "\n")}) 


    #But sometimes, even with >4 occurrence points, Maxent fails... 
    #So I'll make sure that if I have >4 points but maxent didn't work, I get the circles anyway 
    f<-paste("C:/Users/thai/Desktop/Ecologicos/w2/SpDistModel/SEM9/spp/xm/",data_list[i],"/",gsub('\\s+', '_', data_list[i]),"_pred.grd", sep="") 

    gc() #Just collecting garbage to speed up the process 

    if (!file.exists(f)){ # then, if f (maxent output) doesn't exist, create the circles at least 

     spoints<- data_points[[i]] 

     circ <- circles(spoints, d=5642,lonlat=TRUE) 
     circ <- circ@polygons 
     crs(circ)<-crs(wrld_cropped) 
     circ <- gIntersection(wrld_cropped, circ, byid = TRUE, drop_lower_td = TRUE) 

     #To write de polygon to a file, the function writeOGR needs an object SPDF, so... 
     #Getting Polygon IDs 
     circ_df<- as.data.frame(sapply(slot(circ, "polygons"), function(x) slot(x, "ID"))) 
     #Making the IDs row names 
     row.names(circ_df) <- sapply(slot(circ, "polygons"), function(x) slot(x, "ID")) 
     # Make spatial polygon data frame 
     circ_SPDF <- SpatialPolygonsDataFrame(circ, data =circ_df) 

     #Save the polygon, finally 
     #dir.create(paste("C:/Users/thai/Desktop/Ecologicos/w2/SpDistModel/SEM9/spp/xm5/",data_list[i],sep="")) 
     writeOGR(obj = circ_SPDF, dsn = paste0("C:/Users/thai/Desktop/Ecologicos/w2/SpDistModel/SEM9/spp/xm5/",data_list[i],sep=""), driver = "ESRI Shapefile", 
       layer = paste0(gsub('\\s+', '_', data_list[i]),"_pol"), overwrite_layer = TRUE) 

     spc_pol5[[i]]<-circ_SPDF 


     plot(wrld_cropped, border='dark grey', col="#f9f9f9",axes=FALSE, main=data_list[i]) 
     plot(circ, add=TRUE, col=alpha("green3",0.8),border=alpha("green3",0.8)) 
     #plot(spoints,pch=21,col="white", bg='hotpink', lwd=0.1, cex=0.5, add=TRUE) 

    } 


    } else { #If the species does not have more than 4 points, 
      #do not run maxent, but create a circles polygon 

    spoints<- data_points[[i]] 

    #For the circle to have 100km2, d should be 5641.9 ... 
    circ <- circles(spoints, d=5642,lonlat=TRUE) 
    circ <- circ@polygons 
    crs(circ)<-crs(wrld_cropped) 
    circ <- gIntersection(wrld_cropped, circ, byid = TRUE, drop_lower_td = TRUE) 

    circ_df<- as.data.frame(sapply(slot(circ, "polygons"), function(x) slot(x, "ID"))) 
    row.names(circ_df) <- sapply(slot(circ, "polygons"), function(x) slot(x, "ID")) 
    circ_SPDF <- SpatialPolygonsDataFrame(circ, data =circ_df) 

    writeOGR(obj = circ_SPDF, dsn = paste0("C:/Users/thai/Desktop/Ecologicos/w2/SpDistModel/SEM9/spp/xm5/",data_list[i],sep=""), driver = "ESRI Shapefile", 
      layer = paste0(gsub('\\s+', '_', data_list[i]),"_pol"), overwrite_layer = TRUE) 

    par(mfrow=c(1,1),mar = c(2,2, 2, 2)) 
    plot(wrld_cropped, border='dark grey', col="#f9f9f9",axes=FALSE, main=data_list[i]) 
    plot(circ, add=TRUE, col=alpha("green3",0.8),border=alpha("green3",0.8)) 
    spc_pol5[[i]]<-circ_SPDF 

    gc() #collecting garbage before a nuw run 
    } 

} 
dev.off() 
dev.off() #to close that pdf I started before the loop 


ptime2<- proc.time() - ptime1 #just checking the time 
ptime2 

Answer 1

あなたはこのようなのように「コレクター」変数を指定foreachを呼び出すことができます。

results <- foreach(i=1:4, .packages=c("dismo","scales","rgdal","rgeos","rJava")) %dopar%

次に、foreachループの終了前に、あなたが共通リストと引き換えに、すべての結果変数を収集することができますそれら：

foreachのは、「結合」resulによってあなたのためにそれの世話をしますので、foreachの中で、あなたがそのような xm_spc_list[[i]] <-などの構造を使用することを避けることができることに注意してください

out <- list(xm_spc_list= xm_spc_list, 
      e_spc_list = e_spc_list, 
      px_spc_list = px_spc_list, 
      ... = ..., 
      ... = ....) 
return(out) 
} 

tsをリストの（順序付けられた）リストに入れます。

（手元の例を考えると、テストすることは不可能だが）

xm_spc_list <- data.table::rbindlist(do.call(c,lapply(results, "[", 1))) 
e_spc_list <- data.table::rbindlist(do.call(c,lapply(results, "[", 2))) 
.... 
.... 

HTH：

は、あなたが、その後のようなものを使用することができ、foreachの後にリストの resultsリストから、「シングル」の出力を取得するには