运行随机森林循环，预测未采样的位置并保存输出

Question

我想经营一个随机的森林100次，节省火车并测试AUC和OOB分数，预测为空间范围建模，保存平均值，标准误差和每个单元的变化系数，然后写入栅格，如果有人知道如何知道如何添加TSS分数。抱歉，有代码的前半部分缺少代码，零散的代码和错误，但是从随机的森林模型开始有问题，任何帮助都将不胜感激。

Packages:
require(raster);require(dismo);require(sp); 
require(pROC);require(FinCal);require(ggplot2);require(randomForest);require (ROCR) 
require(tidyverse); require(dplyr)

n.boot <- 100
length.map <- nrow(predictor_variables)
boot_mat <- array(0, c(length.map,n.boot)) # matrix where rows will store predictions for each 
bootstrap (columns)
influence_mat <- array(0,c(21,n.boot)) # for saving information on predictors 
auc_mat <- array(0,c(n.boot, 4)) #to save the RF training and test AUC and OOB. 

for (i in 1:n.boot) # bootstrap loop
{ # create training and evaluation presence/ relative absence dataframes
train_ind <- sample(seq_len(nrow(sampled)), size = floor(0.75 * nrow(sampled))) # index of 
rows for 75% of presence data
# creat training and evaluation presences 
sampled _train <- sampled [train_ind, ]
sampled _eval <- sampled [-train_ind, ]

#creation of absences
rnd <- sort(sample(seq(1,length.abs),nrow(sampled _train)*2)) 
rnd.ev <- sort(sample(seq(1,length.abs),nrow(sampled _eval)*2)) 
absence.rnd <- absence[rnd,] 
absence.rnd.ev <- absence[rnd.ev,]

# creation of presence and absence
sampled.PresAbs.B <- rbind(sampled _train,absence.rnd) # final presence/absence data
sampled_eval <- rbind(sampled_eval,absence.rnd.ev)

training<- randomForest(response~., sampled.PresAbs.B [,c(5:25)], ntree=1000, mtry = 2, 
importance = TRUE) # column number of predictor variables to be used in RF
# need to save influence/importance of predictors on response for each run.
# need to save training (training) and test AUC for each run.
# need to save OOB error rate too for training and test runs if possible.

# predict spatially to map 
pred_map <-  predict(predictor_variables, training, type = "prob", index = 2) 
boot_mat[,i] <- round(pred.map, digits = 2) 
}

save(boot_mat, file=" sampled_boot_mat.Rdata")
save(auc_mat, file=" sampled_dev_mat.Rdata")
save(influence_mat, file=" sampled_inf_mat.Rdata")

# need to calculate mean and standard error of influence of predictors on response, then write 
to csv.

# calculate mean suitability and CV spatially
sampled.boot.mean<-apply(boot_mat,1,mean)
sampled.boot.sd<-apply(boot_mat,1,sd)
sampled.boot.cv<- sampled.boot.sd/sampled.boot.mean # calculation of Coefficient of variation if needed

# export the maps
sampled.map.mean <- cbind(sna_all_preds[, c("X", "Y")], sampled.boot.mean) # mean prediciton
sampled.map.UC <- cbind(sna_all_preds[, c("X", "Y")], sampled.boot.sd) # uncertainty 
sampled.map.CV <-cbind(sna_all_preds[,c("X", "Y")], sampled.boot.cv) # CV
#can then export as rasters easily enough.

例如数据：

dput(head(sna_all_boffffs_and_preds, 10))
structure(list(site = 1:10, X = c(708396.365970067, 708646.365970067, 
708896.365970067, 709146.365970067, 708396.365970067, 708646.365970067, 
708896.365970067, 709146.365970067, 708396.365970067, 708646.365970067
), Y = c(1243944.65711016, 1243944.65711016, 1243944.65711016, 
1243944.65711016, 1243694.65711016, 1243694.65711016, 1243694.65711016, 
1243694.65711016, 1243444.65711016, 1243444.65711016), sna_boffffs_clip = c(NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_), adet_2010_2019_250_clip = c(-14.4839498108682, 
-14.4839498108682, -14.4839498108682, -14.4839498108682, -14.4839498108682, 
-14.4839498108682, -14.4839498108682, -14.4839498108682, -14.4839498108682, 
-14.4839498108682), bathy_250_slope_clip = c(3.62498998641968, 
1.90744984149933, 4.96259355545044, 8.03348731994629, 2.80171227455139, 
2.33035254478455, 5.86305713653564, 8.246413230896, 5.99569511413574, 
6.80374336242676), bathy_250_vrm_007_clip = c(0.00215762853622437, 
0.00210392475128174, 0.00183302164077759, 0.00150519609451294, 
0.00216823816299438, 0.00220978260040283, 0.0020909309387207, 
0.00188064575195312, 0.00211155414581299, 0.00220668315887451
), btr_100km_clip = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), btr_10km_clip = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0), btr_50km_clip = c(0, 0, 0, 0, 0, 
0, 0, 0, 0, 0), carbonate_250_clip = c(30.7859439849854, 30.7859439849854, 
30.7859439849854, 30.7859439849854, 30.7859439849854, 30.7859439849854, 
30.7859439849854, 30.7859439849854, 30.7859439849854, 30.7859439849854
), Catch_kg_per_km2_AllMethods_250_clip = c(0, 0, 0, 0, 0, 0, 
0, 0, 0, 0), current_speed_250_clip = c(0.17399999499321, 0.17399999499321, 
0.17399999499321, 0.17399999499321, 0.17399999499321, 0.17399999499321, 
0.17399999499321, 0.17399999499321, 0.17399999499321, 0.17399999499321
), dist_shore_250_clip = c(67926.890625, 67982.53125, 68039.0546875, 
68096.4375, 67683.0859375, 67738.9296875, 67795.6484375, 67853.2421875, 
67439.328125, 67495.3671875), ebed_20_av_250_clip = c(0.538848269429723, 
0.538848269429723, 0.538848269429723, 0.538848269429723, 0.538848269429723, 
0.538848269429723, 0.538848269429723, 0.538848269429723, 0.538848269429723, 
0.538848269429723), gravel_250_clip = c(19.7878284454346, 19.7878284454346, 
19.7878284454346, 19.7878284454346, 19.7878284454346, 19.7878284454346, 
19.7878284454346, 19.7878284454346, 19.7878284454346, 19.7878284454346
), hvis_20_av_250_clip = c(21.4873270945488, 21.4873270945488, 
21.4873270945488, 21.4873270945488, 21.4873270945488, 21.4873270945488, 
21.4873270945488, 21.4873270945488, 21.4873270945488, 21.4873270945488
), orb_vel_250_clip = c(150L, 150L, 150L, 150L, 150L, 150L, 150L, 
150L, 150L, 150L), rocky_reef_clip = c(0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L), sal_depth_250_clip = c(35.231746673584, 35.231746673584, 
35.231746673584, 35.231746673584, 35.231746673584, 35.231746673584, 
35.231746673584, 35.231746673584, 35.231746673584, 35.231746673584
), sand_250_clip = c(19.3533153533936, 19.3533153533936, 19.3533153533936, 
19.3533153533936, 19.3533153533936, 19.3533153533936, 19.3533153533936, 
19.3533153533936, 19.3533153533936, 19.3533153533936), sst_20_av_250_clip = c(16.6609603188989, 
16.6609603188989, 16.6609603188989, 16.6609603188989, 16.6609603188989, 
16.6609603188989, 16.6609603188989, 16.6609603188989, 16.6609603188989, 
16.6609603188989), sst_annual_amp_250_clip = c(2.67400002479553, 
2.67400002479553, 2.67400002479553, 2.67400002479553, 2.67400002479553, 
2.67400002479553, 2.67400002479553, 2.67400002479553, 2.67400002479553, 
2.67400002479553), sst_grad_250_clip = c(0.00596274901181459, 
0.00596274901181459, 0.00596274901181459, 0.00596274901181459, 
0.00596274901181459, 0.00596274901181459, 0.00596274901181459, 
0.00596274901181459, 0.00596274901181459, 0.00596274901181459
), temp_res_250_clip = c(4.63328790664673, 4.63328790664673, 
4.63328790664673, 4.63328790664673, 4.63328790664673, 4.63328790664673, 
4.63328790664673, 4.63328790664673, 4.63328790664673, 4.63328790664673
)), row.names = c(NA, 10L), class = "data.frame")