R 光栅中不准确的随机概率：：使用type="；问题；

我在raster:：predict函数中遇到了一个问题，该函数与生成cforest对象存在的概率预测相关

当我生成一个分类输出时，我获得了一个正确的输出——然而，当添加type=“prob”以获得概率时，输出是一个奇怪的带状，与分类输出不对应。我在这里附上了两张照片：这张照片详细介绍了这些问题

正如您将注意到的，输出上的棋盘格模式随机放置高概率和低概率的随机区域，而分类输出在预期位置创建存在（1）和不存在（0）块

下面是一小段代码，其中包含训练数据和用于预测的光栅块段

如果有人了解我如何使用此函数获得有效概率，我将不胜感激

我是否遗漏了predfun功能中的一些重要内容

提前谢谢你

以下是示例数据：

library(raster)
training = structure(list(
 Presence = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("0", "1"), class = "factor"), 
 Elevation = c(3937.63541666689, 384.003472222217, 401.357638888879, 226.43749999999, 21.3055555555572, 305.399305555546, 38.3402777777742, 347.302083333335, 168.156250000001, 700.708333333328, 1034.2013888889, 1033.78125, 1426.99305555577, 912.874999999952, 665.854166666672, 657.187499999983, 1181.97916666667, 696.062499999948, 976.812500000002, 1017.98263888889), 
 Region = structure(c(3L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 3L, 3L, 1L, 4L, 2L, 4L, 4L), .Label = c("1", "2", "3", "4", "5"), class = "factor"), 
 Protected = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L), .Label = c("0","1"), class = "factor"), 
 Population = structure(c(17L, 3L, 4L, 1L, 13L, 2L, 5L, 2L, 9L, 13L, 6L, 5L, 5L, 7L, 8L, 14L, 1L, 13L, 7L, 1L), .Label = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20"), class = "factor"), 
 Mean_Diunal_Range = c(126,151, 152, 153, 138, 125, 137, 158, 129, 137, 170, 172, 115,151, 150, 178, 149, 146, 158, 165), 
 Isothermality = c(31,49, 46, 49, 54, 51, 59, 48, 47, 62, 54, 54, 61, 37, 47, 63,61, 75, 55, 57), 
 Mean_Temp_Wettest_Q = c(-87, 338, 348, 135,236, 193, 305, 322, 247, 253, 249, 247, 210, 105, 313, 252,238, 267, 250, 255), 
 Mean_Temp_Driest_Q = c(64, 231, 253,230, 324, 194, 263, 261, 328, 236, 133, 130, 158, 315, 188,232, 173, 248, 166, 151), 
 Precip_Wettest_M = c(119, 30, 18,3, 16, 8, 22, 12, 11, 165, 62, 59, 71, 13, 11, 201, 85, 110,121,91), 
 Precip_Dryest_m = c(2, 0, 0, 0, 0, 0, 0, 0, 0,0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0), 
 Precip_Seasonality = c(81,133, 136, 65, 106, 65, 126, 85, 101, 110, 77, 76, 111, 85,111, 125, 116, 123, 106, 93), 
 Precip_Warmest_Q = c(19, 55,29, 0, 0, 6, 49, 23, 1, 152, 148, 146, 56, 0, 15, 143, 63,140, 174, 195), 
 Precip_Coldest_Q = c(201, 0, 1, 8, 14, 14,5, 6, 17, 0, 9, 10, 0, 29, 0, 0, 2, 0, 1, 2), 
 Agriculture_Date = c(9000,3000, 3000, 7200, 7600, 3500, 3500, 3500, 7600, 5000, 1000,1700, 1250, 9500, 4000, 2700, 1250, 4000, 1000, 1000), 
 distance_to_highways = c(0.697535828668298,3.21145962385929, 4.70553674201054, 1.26025341029057, 0.0306302671744576,1.29795751420045, 0.341335382947427, 2.01515485126633, 1.90935593837289,1.00778111672525, 0.0841242925270876, 0.0135315745860043,2.39038097221274, 1.49284290327056, 1.21019159581485, 5.71817373942967,1.64045219527117, 0.121375728043842, 0.535675418474612, 1.08581690073317)), 
row.names = c(NA, -20L), class = c("data.table", "data.frame"))

##Create Prediction Raster Brick

## Create Data Frame for Raster Brick
data_segment <- structure(list(
 Elevation = c(1187.9, 1173.5, 1158.2, 1143.3, 1125.6, 1112.4, 1232.7, 1203.3, 1176.7, 1156.8, 1138.9, 1140.8, 1249.9, 1216.9, 1193.2, 1171.4, 1153.5, 1157.6, 1261.5, 1233.2, 1208.2, 1185.6, 1175.5, 1184.2, 1289.1, 1256.4, 1240.7, 1212.2, 1208.1, 1220.9, 1304.6, 1297.2, 1286.6, 1249.2, 1231, 1256.1, 1341.5, 1329.3, 1328.5, 1291.8, 1250.2, 1288.6, 1379.2, 1322.8, 1305.6, 1293.7, 1234.9, 1260.5, 1354.5, 1274.5, 1257.7, 1281.1, 1230.8, 1196.3, 1288.5, 1237.4, 1221.5, 1241.6, 1212.7, 1162.3, 1199.4, 1185.4, 1194.5, 1216.1, 1207.4, 1149.8, 1150.6, 1157, 1176, 1176.1, 1155.6, 1138, 1145, 1131.9, 1121.1, 1116.3, 1109.4, 1116.2), 
 Region = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), 
 Protected = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), 
 Population = c(10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 15, 11, 11, 12, 12, 12, 15, 11, 11, 12, 12, 12, 15, 11, 11, 12, 12, 12, 16, 11, 11, 12, 12, 12, 16, 11, 11, 12, 12, 12, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12), 
 Mean_Diunal_Range = c(10.5, 10.5, 10.6, 10.7, 10.8, 10.9, 10.4, 10.5, 10.5, 10.5, 10.6, 10.6, 10.3, 10.4, 10.5, 10.5, 10.5, 10.4, 10.2, 10.3, 10.3, 10.4, 10.3, 10.4, 10.2, 10.2, 10.2, 10.2, 10.3, 10.3, 10.3, 10.2, 10.1, 10.2, 10.3, 10.3, 10.3, 10.2, 10.2, 10.2, 10.2, 10.1, 10.2, 10.3, 10.2, 10.1, 10.1, 10.1, 10.1, 10.2, 10.2, 10, 10, 10.1, 10.1, 10.1, 10, 9.9, 9.9, 10, 10, 10, 9.9, 9.9, 9.9, 9.9, 10, 9.9, 9.8, 9.9, 9.8, 9.8, 10.1, 10.1, 10, 9.8, 9.8, 9.7), 
 Isothermality = c(68.5, 68.7, 69.3, 69.4, 69.6, 69.7, 68.4, 68.5, 68.8, 68.7, 69.2, 69.2, 67.8, 68.1, 68.4, 68.6, 68.6, 68.3, 67.4, 67.4, 67.6, 67.9, 68, 68.1, 67.2, 66.8, 66.9, 67.3, 67.5, 67.3, 67.2, 66.4, 66.6, 66.9, 66.7, 67, 66.7, 66.7, 66.5, 66.4, 66.5, 66.2, 65.7, 66.3, 66, 65.7, 65.5, 65.7, 65.4, 65.1, 65.1, 65.2, 65.2, 65.3, 64.2, 64.5, 64.5, 64.1, 64.6, 64.7, 63.8, 64, 64.1, 63.7, 63.8, 64.2, 63.4, 63.7, 63.4, 63.6, 63.5, 63.7, 63.8, 64.1, 63.5, 63, 63.2, 63.1), 
 Mean_Temp_Wettest_Q = c(24.5, 24.5, 24.7, 24.8, 25, 25, 24.2, 24.4, 24.5, 24.7, 24.8, 24.8, 24.1, 24.3, 24.5, 24.6, 24.7, 24.8, 24, 24.2, 24.4, 24.5, 24.6, 24.7, 24, 24.1, 24.2, 24.4, 24.5, 24.5, 23.9, 24, 24, 24.3, 24.5, 24.4, 23.8, 23.8, 23.9, 24, 24.3, 24.1, 23.5, 23.9, 24, 24, 24.3, 24.3, 23.7, 24.1, 24.2, 24.1, 24.4, 24.6, 24, 24.4, 24.4, 24.3, 24.5, 24.8, 24.5, 24.6, 24.5, 24.5, 24.5, 24.9, 24.8, 24.8, 24.6, 24.7, 24.8, 24.9, 24.8, 25, 25, 25, 25, 25), 
 Mean_Temp_Driest_Q = c(22.4, 22.4, 22.5, 22.7, 22.8, 22.9, 22.1, 22.3, 22.4, 22.5, 22.7, 22.7, 22, 22.2, 22.4, 22.5, 22.6, 22.6, 21.9, 22.1, 22.3, 22.4, 22.5, 22.5, 21.8, 22, 22, 22.2, 22.3, 22.3, 21.7, 21.7, 21.8, 22.1, 22.2, 22.1, 21.5, 21.5, 21.6, 21.8, 22, 21.8, 21.2, 21.6, 21.7, 21.7, 22, 21.9, 21.3, 21.8, 21.9, 21.8, 22, 22.2, 21.6, 21.9, 22, 21.9, 22, 22.3, 22, 22.1, 22, 22, 22, 22.4, 22.2, 22.2, 22.1, 22.2, 22.2, 22.4, 22.3, 22.8, 22.4, 22.5, 22.5, 22.5),
 Precip_Wettest_M = c(161, 160, 161, 161, 159, 158, 163, 162, 163, 163, 162, 161, 166, 165, 164, 164, 163, 163, 169, 167, 166, 166, 166, 166, 172, 169, 169, 168, 169, 169, 173, 172, 173, 171, 170, 170, 174, 176, 176, 174, 171, 173, 179, 175, 175, 175, 171, 173, 180, 176, 174, 176, 172, 171, 181, 177, 176, 177, 174, 170, 177, 176, 176, 177, 175, 170, 175, 175, 177, 178, 175, 174, 176, 175, 175, 175, 173, 174), 
 Precip_Dryest_m = c(6, 6, 6, 6, 6, 6, 7, 6, 6, 6, 6, 6, 7, 6, 6, 6, 6, 6, 7, 6, 6, 6, 6, 6, 7, 6, 6, 6, 6, 6, 7, 6, 7, 6, 6, 6, 7, 7, 7, 6, 6, 6, 8, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7), 
 Precip_Seasonality = c(100.8, 101.7, 103, 105.2, 105.9, 107.2, 99.8, 101.8, 103.4, 105.5, 106.3, 106.8, 101.1, 101.8, 103.4, 105.1, 106.2, 106.8, 100.8, 102, 103.1, 105.3, 106.6, 107.6, 101.4, 102.5, 103.8, 105.3, 106.9, 107.2, 100.8, 102.2, 102.7, 104.9, 106.3, 106.6, 100.1, 101.3, 102.6, 104.1, 105.7, 106.3, 98.8, 101.3, 101.9, 103.6, 104.8, 105.8, 99.2, 102, 102.6, 103.5, 104.5, 106.4, 100.1, 102.4, 103.8, 104.2, 105, 106.8, 100.7, 103, 103.6, 104.1, 104.7, 105.8, 101.2, 102.2, 103.9, 104.6, 105.6, 106.7, 101.3, 102.5, 103.4, 104.2, 105.5, 106.7), 
 Precip_Warmest_Q = c(97, 96, 94, 89, 86, 84, 100, 97, 94, 90, 87, 86, 100, 99, 95, 91, 88, 87, 102, 99, 96, 92, 89, 87, 102, 98, 96, 92, 91, 90, 105, 100, 98, 95, 92, 92, 106, 104, 101, 97, 93, 93, 109, 101, 101, 97, 92, 93, 108, 100, 97, 97, 93, 89, 105, 99, 96, 96, 93, 89, 101, 98, 96, 95, 94, 89, 98, 97, 96, 94, 91, 89, 99, 96, 94, 93, 90, 88), 
 Precip_Coldest_Q = c(23, 22, 21, 21, 21, 19, 25, 22, 22, 21, 21, 20, 24, 23, 21, 21, 21, 20, 25, 23, 22, 21, 21, 21, 25, 23, 22, 21, 21, 21, 25, 24, 25, 22, 21, 21, 27, 27, 25, 23, 22, 22, 30, 27, 26, 25, 24, 23, 30, 27, 26, 26, 24, 23, 30, 27, 26, 26, 25, 21, 28, 26, 26, 26, 25, 24, 28, 26, 26, 26, 25, 24, 28, 27, 26, 26, 24, 24), 
 Agriculture_Date = c(4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000), 
 distance_to_highways = c(0.1, 0, 0, 0.1, 0.1, 0.1, 0, 0, 0, 0.1, 0.1, 0.1, 0, 0, 0, 0.1, 0.1, 0.2, 0.1, 0, 0, 0.1, 0.1, 0.1, 0.1, 0, 0, 0.1, 0.1, 0.1, 0.1, 0, 0, 0, 0.1, 0.1, 0.1, 0, 0, 0, 0.1, 0.1, 0, 0, 0, 0, 0.1, 0.1, 0, 0, 0, 0.1, 0.1, 0.1, 0, 0, 0.1, 0.1, 0.1, 0.1, 0, 0, 0, 0.1, 0.1, 0.2, 0, 0, 0, 0.1, 0.1, 0.2, 0, 0, 0, 0.1, 0.1, 0.2)), 
class = "data.frame", row.names = c(NA, -78L))

## Create RasterBrick and assign values from dataframe
segment_brick <- brick(nrow=13, ncol=6, xmn=39, xmx=39.25, ymn=3.833333, ymx=4.375,crs="+proj=longlat +datum=WGS84 +no_defs", nl=15)
values(segment_brick) <-  as.matrix(data_segment)

库（光栅）
培训=结构（列表）(
存在=结构（c（1L、1L、1L、1L、1L、1L、1L、1L、1L、1L、2L、2L、2L、2L、2L、2L、2L、2L、2L、2L、2L、2L、2L），标签=c（“0”、“1”），类别=“系数”），
标高=c(3937.63541666689, 384.003472222217, 401.357638888879, 226.43749999999, 21.3055555555572, 305.399305555546, 38.3402777777742, 347.302083333335, 168.156250000001, 700.708333333328, 1034.2013888889, 1033.78125, 1426.99305555577, 912.874999999952, 665.854166666672, 657.187499999983, 1181.97916666667, 696.062499999948, 976.812500000002, 1017.98263888889), 
区域=结构（c（3L，5L，5L，3L，3L，3L，3L，3L，2L，4L，4L，3L，1L，4L，2L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L），标签=c（“1”，“2”，“3”，“4”，“5”），类别=“系数”），
保护=结构（c（1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，2L，1L，1L，1L，1L，2L，1L，1L，1L，1L，1L，2L），.Label=c（“0”，“1”），class=“factor”），
人口=结构（c（17L，3L，4L，1L，13L，2L，5L，2L，9L，13L，6L，5L，5L，7L，8L，14L，1L，13L，7L，1L，1L），标签=c（“1”，“2”，“3”，“4”，“5”，“6”，“7”，“8”，“9”，“10”，“11”，“12”，“13”，“14”，“15”，“16”，“18”，“19”，“20”），类别=“系数”），
平均日变化范围=c（126151、152、153、138、125、137、158、129、137、170、172、115151、150、178、149、146、158、165），
等温性=c（31,49,46,49,54,51,59,48,47,62,54,61,37,47,63,61,75,55,57），
平均温度最湿温度Q=c（-87338348135236193305322247247253249247212105313325223826725255），
平均温度=摄氏度（6423125323324194263261328328236133 130 158315188232 173248166 151），
Precip_最湿_M=c（119,30,18,3,16,8,22,12,11,165,62,59,71,13,11,201,85,110121,91），
Precip_dreyest_m=c（2,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0），
Precip_季节性=c（81133、136、65、106、65、126、85、101、110、77、76、111、85111、125、116、123、106、93），
最暖的温度Q=c（19,55,29,0,0,6,49,23,1152,148,146,56,0,15,143,63140,174,195），
最冷温度Q=c（201,0,1,8,14,14,5,6,17,0,9,10,0,29,0,0,2,0,1,2），
农业日期=c（900003000300720076003500 3500 3500 3500 76005000100017001250500040002700125040001000），
_至_公路的距离=c(0.697535828668298,3.21145962385929, 4.70553674201054, 1.26025341029057, 0.0306302671744576,1.29795751420045, 0.341335382947427, 2.01515485126633, 1.90935593837289,1.00778111672525, 0.0841242925270876, 0.0135315745860043,2.39038097221274, 1.49284290327056, 1.21019159581485, 5.71817373942967,1.64045219527117, 0.121375728043842, 0.535675418474612, 1.08581690073317)), 
row.names=c（NA，-20L），class=c（“data.table”，“data.frame”））
##创建预测光栅砖
##为光栅砖创建数据框
数据段进行此操作的方法是查看模型的预测函数返回的结果

p <- predict(cforest_example, newdata=training[1:3,], OOB=TRUE, type="prob")
p
#$`1`
#     Presence.0 Presence.1
#[1,]  0.5461538  0.4538462
#
#$`2`
#     Presence.0 Presence.1
#[1,]  0.5461538  0.4538462
#
#$`3`
#     Presence.0 Presence.1
#[1,]  0.5461538  0.4538462

这就解释了带状结构

你可以用这样的预测函数来解决这个问题

pfun <- function(m, d, ...) {
    p <- predict(m, newdata=d, ...)
    matrix(unlist(p), ncol=2, byrow=TRUE)
}
    
pfun(cforest_example, training[1:3,], OOB=TRUE, type="prob")
#          [,1]      [,2]
#[1,] 0.5461538 0.4538462
#[2,] 0.5461538 0.4538462
#[3,] 0.5461538 0.4538462

由于您只有2个概率，因此可以简化函数以仅返回一个类的概率（在本例中不存在）：

pfun
unlist(p)
#       11        12        21        22        31        32 
#0.5461538 0.4538462 0.5461538 0.4538462 0.5461538 0.4538462 

pfun <- function(m, d, ...) {
    p <- predict(m, newdata=d, ...)
    matrix(unlist(p), ncol=2, byrow=TRUE)
}
    
pfun(cforest_example, training[1:3,], OOB=TRUE, type="prob")
#          [,1]      [,2]
#[1,] 0.5461538 0.4538462
#[2,] 0.5461538 0.4538462
#[3,] 0.5461538 0.4538462

prob = raster::predict(segment_brick, cforest_example, OOB=TRUE, factors=f, fun=pfun, index=1:2, na.rm=TRUE,type="prob")
plot(prob)
prob
#class      : RasterBrick 
#dimensions : 13, 6, 78, 2  (nrow, ncol, ncell, nlayers)
#resolution : 0.04166667, 0.04166669  (x, y)
#extent     : 39, 39.25, 3.833333, 4.375  (xmin, xmax, ymin, ymax)
#crs        : +proj=longlat +datum=WGS84 +no_defs 
#source     : memory
#names      :   layer.1,   layer.2 
#min values : 0.5461538, 0.4538462 
#max values : 0.5461538, 0.4538462 

pfun <- function(m, d, ...) {
    p <- predict(m, newdata=d, ...)
    matrix(unlist(p), ncol=2, byrow=TRUE)[,1]
}
prob = raster::predict(segment_brick, cforest_example, OOB=TRUE, factors=f, fun=pfun, na.rm=TRUE,type="prob")