从 dataframe 创建栅格会产生错误

Question

我正在运行一个随机森林，将PC_Count与多个变量进行比较

rf_h_count_km <- randomForest (
  formula = PC_count ~NLCD+Pop_Focal+NLUD+Population+Urban+Slope+NLCD21_f+NLCD22_f+NLCD23_f+NLCD24_f+Density+PC,
  data = training,
  method = 'rf', 
  tuneGrid = tuneGrid,
  ntree = 100,
  nodesize = 5,
  importance = TRUE,
  trControl = ctrl,
  metric = "ROC"
)

随机森林运行成功，但现在我正在尝试将 output 转换为预测栅格。 看到预测变量是从 dataframe 和光栅堆栈中的自变量获得的，我还没有找到直接执行此操作的方法

#predict the response variable with training data 
l<-raster::predict(rf_h_count_km,  type="response", data=testing, index=2, na.rm=TRUE, progress="window", overwrite=TRUE) 

我将 output 数组转换为datframe格式

x<-as.data.frame(l)
names(x) <- c("try")

其次，我创建了一个具有所需尺寸的空栅格

r = raster(ncols=225, nrows=176, xmn=-1788075, xmx=-1584465, ymn=1481625, ymx=1717755, res=1000)

第三，我尝试将 dataframe 中的值添加到栅格中。 但是，这会产生错误

> raster::values(r)<-x$try
Error in setValues(x, value) : 
  length(values) is not equal to ncell(x), or to 1

有什么方法可以将这些值添加到栅格中而不产生错误？

下面是1列的一段dataframe

> dput(head(x,500))
structure(list(try = c(1.25482243953606, 1.29704685733086, 1.59604755116753, 
1.27323310015838, 1.65523336838096, 1.16055734781969, 1.91659455992709, 
1.52214228365861, 1.39167234672577, 1.39345512204005, 1.14707832355435, 
1.38027338524104, 1.43416846811582, 1.19860901400402, 1.36468712553493, 
1.39169436843932, 1.331203829109, 1.92116276432308, 1.76695170198865, 
1.29033362603409, 1.49713229698021, 1.43769464696184, 1.68270039388572, 
1.44802123045305, 1.48484344231137, 1.5923432415293, 1.89790742744141, 
1.32407450302128, 1.41150242816569, 1.56883137580953, 1.39132737542988, 
1.34693405858518, 1.70515171668636, 1.787306308579, 1.53811871225996, 
1.54649976632651, 1.37557485902264, 1.42460519475178, 1.84602421808187, 
1.46107494748112, 1.48574351291447, 1.55515920286892, 1.47435149169411, 
2.06774644973887, 1.67011263561363, 1.53676870286262, 1.51437261683124, 
1.65344434977873, 1.52521033303975, 1.33428311000463, 1.86538942543929, 
1.1728691498302, 1.17687652159234, 1.80537815643604, 1.49692701357964, 
1.00050456141199, 1.47365424021922, 1.47114102470081, 1.39277787775097, 
0.865697705973826, 1.50096649740468, 1.13385260841033, 1.65271338239792, 
1.46884690867827, 1.481906677787, 1.14199273373277, 1.68774387988497, 
1.51872838325436, 1.38328649616044, 1.67114602131701, 1.54848407844392, 
1.3996624303754, 1.71789148484286, 1.33823346156167, 1.25566170818395, 
1.55020641523366, 1.17128373170123, 1.26915332769989, 1.66256952986062, 
1.50038471501031, 1.30659761867545, 1.38389413400123, 1.59603869890816, 
1.28059405159913, 1.49876041289129, 1.45692568883059, 1.82472848165875, 
1.23544716197641, 1.30844258992342, 1.62576923439109, 1.26939004412273, 
1.58507687431416, 1.31481901026701, 1.4575809162353, 1.53809208050283, 
1.16766113344713, 1.56314563435862, 1.55411214306813, 1.78357145366888, 
1.4329858089636, 1.43396598308919, 1.39465154299149, 1.48451992338695, 
1.24684337200367, 1.96110469737173, 1.16625138690017, 0.775810330702323, 
1.00045993259336, 1.37686548806939, 1.62733354682162, 1.68759141765908, 
1.628166880637, 1.94206520355245, 1.61238598818564, 1.96885931093973, 
1.34625059573652, 1.31831686519609, 1.53290631646635, 1.57775601413376, 
1.58363330493889, 1.89319841817133, 0.782837422055985, 1.64643379323501, 
1.66712161497969, 1.29699891021702, 1.47429558125218, 1.68691877809511, 
1.74608775523785, 1.9991622606389, 1.56607510448966, 1.6309907225318, 
1.46078033728118, 1.17142033440308, 1.40255145198544, 1.33112985551097, 
1.15166500669548, 1.80419274569205, 1.62729203127811, 1.83685487185426, 
1.58635321656375, 1.82779066393067, 1.34477410566094, 1.49887518851983, 
1.50264666884424, 1.30559742049033, 1.70529841717443, 1.36461045525143, 
1.35656649147087, 1.37880956124176, 1.32825774615472, 1.4743468228756, 
1.68848953739118, 1.5714969472275, 1.28051735582015, 1.70382287292112, 
1.48697666114366, 1.52509854662891, 2.00215110778257, 1.51573583601317, 
1.52652734904295, 1.83725972310248, 1.43565914024433, 1.65558396263679, 
1.28093702179809, 1.34220737362985, 1.45510583039298, 1.34188715043272, 
1.59560616172484, 1.18911884147528, 1.76384130089723, 1.14960238421033, 
1.36430236202792, 1.62519404962719, 1.41158597502356, 0.797289908270849, 
1.15520086855207, 1.42517561255296, 1.00047571138336, 1.31365223086707, 
1.4129217354065, 1.29535822063539, 1.67078827797151, 1.54029946373479, 
1.84666725485803, 1.31394353069208, 1.30563028604674, 1.81450886504348, 
1.19491662297257, 1.27229869878179, 1.28963013410991, 1.43762367763379, 
1.16928002123748, 0.76442403537918, 1.28823684878266, 1.74785996977029, 
1.64959703066888, 1.5509430052318, 1.45843245579434, 1.63183302686946, 
1.59733661443473, 0.760843648977963, 1.36684005161429, 1.84519682070654, 
1.50049023315098, 1.7050215606835, 1.62763853632365, 1.56200633371077, 
1.51191419294519, 1.6116950079444, 1.76167964082101, 1.6283596872148, 
1.60698192017333, 1.66810316469347, 1.16126887133296, 1.38968215164831, 
1.48455441438511, 0.660013152279427, 1.53085391999465, 1.82842047029258, 
1.1661862475647, 1.00044570589583, 1.70599622205831, 1.38431550482051, 
1.50410961983431, 1.61241963890188, 1.68592745635435, 1.36708568021482, 
1.49941207893968, 1.48543479320907, 1.35809437513195, 1.55296348681227, 
1.59819516191763, 1.48481302388378, 1.4061746887853, 1.36929246790332, 
1.39266730855954, 1.46027138849061, 1.53273782134419, 1.16136716380526, 
1.56032756345299, 1.76303556273634, 1.30009474488364, 0.713431047919119, 
1.27230670052624, 1.74416452489221, 1.61122408130062, 1.89634699686347, 
1.47106580278593, 1.28149781048158, 1.55039187691515, 1.70574909049325, 
1.30713212157724, 1.52846086526416, 1.67346330500358, 1.43438661455238, 
1.18645458508454, 1.63936712727192, 1.41112353469103, 1.2761061429259, 
1.27580024677754, 1.3072009173607, 0.76482905669465, 1.48493548485036, 
1.62951834284185, 1.26147044940371, 1.51789060097793, 1.41333216708036, 
1.33639249077728, 1.45542697933075, 1.43760708672506, 1.71199034891044, 
1.26027699097399, 1.32370075969687, 1.14957580510731, 1.42015421688412, 
1.80770319259332, 1.56027185359019, 1.26335891477022, 1.14435742981936, 
1.21496018704641, 1.1581564288297, 1.53379063258365, 1.51294803803159, 
1.30732487742837, 1.43717349783508, 1.43486086288793, 1.50187858120187, 
1.15120725634633, 1.35400674186641, 1.55364027339135, 1.94536872847578, 
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1.62626167586269, 1.91473409741524, 1.63101761509542, 1.67304098596854, 
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1.34873996399922, 1.37023850292472, 1.53564601207952, 1.57505619181632, 
1.36708995300055, 1.56941164647469, 1.16925475135763, 0.842871188459787, 
1.3812699305909, 1.41386414196727, 1.16059409444905, 1.76201853912225, 
1.44222009921562, 1.64997144444006, 1.38376159924388, 1.61003199654573, 
1.57499790571938, 1.15201686470987, 1.76676689613511, 1.8096908759832, 
1.20486311868743, 1.68728039392101, 1.42500419002418, 1.44665607038376, 
1.58123291895195, 1.14005656747451, 1.48678222431433, 1.44661082228836, 
1.61171967442807, 1.30729777317031, 1.42286763782785, 1.40553596608522, 
1.55064563919909, 1.70051648043645, 1.69303294109564, 1.5515686820675, 
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1.34223556479203, 1.15246311383752, 1.68485931618636, 1.40568909806569, 
0.784724015738059, 1.49886040116948, 1.8272016947891, 1.76469931390133, 
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1.68797176508759, 1.46051637830131, 1.49869552936372, 1.14441765225327, 
1.57745957616188, 1.28307917058032, 1.74574595151118, 1.35334508600179, 
1.59272719674702, 1.19935222312585, 1.35880915261704, 1.15239419992794, 
1.4044441578425, 1.39219305326424, 1.44629956051605, 2.01162723040148, 
1.4711194451495, 1.62984928328262, 1.29120536667572, 1.7437705740116, 
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1.46992801989381, 1.51568282463528, 1.56295971478745, 1.43171059023851, 
1.48486531481598)), row.names = c("4", "5", "8", "9", "10", "13", 
"15", "16", "17", "18", "19", "20", "21", "22", "23", "26", "27", 
"28", "29", "31", "34", "35", "37", "39", "40", "41", "42", "43", 
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Answer 1

这是使用terra （替代raster ）的方法

library(terra)
logo <- rast(system.file("ex/logo.tif", package="terra"))   
names(logo) <- c("red", "green", "blue")
p <- matrix(c(48, 48, 48, 53, 50, 46, 54, 70, 84, 85, 74, 84, 95, 85, 
   66, 42, 26, 4, 19, 17, 7, 14, 26, 29, 39, 45, 51, 56, 46, 38, 31, 
   22, 34, 60, 70, 73, 63, 46, 43, 28), ncol=2)

a <- matrix(c(22, 33, 64, 85, 92, 94, 59, 27, 30, 64, 60, 33, 31, 9,
   99, 67, 15, 5, 4, 30, 8, 37, 42, 27, 19, 69, 60, 73, 3, 5, 21,
   37, 52, 70, 74, 9, 13, 4, 17, 47), ncol=2)

xy <- rbind(cbind(1, p), cbind(0, a))
e <- extract(logo, xy[,2:3])
v <- data.frame(cbind(pa=xy[,1], e[,-1]))

library(randomForest)
rfm <- randomForest(formula=pa~., data=v)
p <- predict(logo, rfm)

我从帮助文件?terra::predict中复制了这个。 raster::predict中提供了相同的 raster 示例。

请注意， predict的第一个参数应该是SpatRaster （或 raster 包的 Raster*）。 你做什么：

l <- raster::predict(rf_h_count_km,...)

是错误的，因为这只会为您提供 model 训练数据的预测，因为您没有提供要预测的栅格数据。

（（和raster::predict是一个通用的 function ，它将 select 基于第一个参数的适当实现。在这种情况下，它将只是 Z34D1F91FB2E514B8576FAB1A75A89A6B randomForest::predict ）：

除非您使用所有栅格数据训练 model，否则您可以使用类似

 rfma <- randomForest(formula=red~., data=as.data.frame(logo))
 r <- rast(logo, nlyr=1)
 values(r) <- predict(rfma)