错误：nrow(ref) 和 nrow(target) 必须 > 0？

Question

基于Unable to writeRaster for signature "rasterPCA", "character"，我得到了一组气候变量的两个栅格，分别是PC1和PC2。但是，无论具有相同的范围和分辨率，当加载到 R 中时，我的全局环境中的单元格数量都会有所不同。

以下是我正在使用的代码，来自 Hamann et al., 2015 的附录，我收到此错误：

library(SDMTools)     # install package to read and write ESRI ASCII grids
library(yaImpute)     # install package for k-nearest neighbour (kNN) search

lg1 <- asc2dataframe("C:\\Users\\rameshv\\LGM\\4_PCAforR\\PC_1.asc") # principal component grids
lg2 <- asc2dataframe("C:\\Users\\rameshv\\LGM\\4_PCAforR\\PC_2.asc")
present1  <-asc2dataframe("C:\\Users\\rameshv\\Present\\4_PCAforR\\PC_1.asc")
present2  <- asc2dataframe("C:\\Users\\rameshv\\Present\\4_PCAforR\\PC_2.asc")

idxy <- cbind(id=1:nrow(lg1),lg1[,1:2])   # data frame of IDs and XY coords
b <- (max(lg1$var.1)-min(lg1$var.1))/120  # bin size for 120 PC1 bins

l1 <- round(lg1$var.1/b)              # convert PC1 to 120 bins via rounding
l2 <- round(lg2$var.1/b)              # convert PC2 to <120 bins via rounding
p1 <- round(present1$var.1/b)               # same for present PC1
p2 <- round(present2$var.1/b)               # same for present PC2
l  <- paste(l1,l2)                         # PC1/PC2 combinations in LGM climate
p  <- paste(p1,p2)                         # PC1/PC2 combinations in present climate
u  <- unique(p)[order(unique(p))]          # list of unique PC1/PC2 combinations

sid <- c()                                 # empty vector for source IDs
tid <- c()                                 # empty vector for target IDs
d   <- c()                                 # empty vector for distances

for(i in u){                          # loop for each unique PC1/PC2 combination
lxy <- idxy[which(l==i),]           # coordinates of i-th combination in LGM
pxy <- idxy[which(p==i),]           # coordinates of i-th combination in present
sid <- c(sid, lxy$id)               # append i-th PC1/PC2 combination to previous 

if(nrow(pxy)>0){                    # kNN search unless no-analogue climate
  knn <- data.frame(ann(as.matrix(pxy[,-1]), as.matrix(lxy[,-1]), k=1)$knnIndexDist)      
  tid <- c(tid, pxy[knn[,1],"id"]) # the IDs of the closest matches  
  d <- c(d, sqrt(knn[,2]))         # their corresponding geographic distances
}
 else {                              # else statement for no-analogue climates
 tid <- c(tid, rep(NA,nrow(lxy))) # flag destinations as missing for no analogues
 d <- c(d, rep(Inf,nrow(lxy)))    # flag distances as infinity for no analogues
 }
}

在 for 循环结束时，我收到以下错误：

Error in ann(as.matrix(pxy[, -1]), as.matrix(lxy[, -1]), k = 1) : 
error: nrow(ref) and nrow(target) must be > 0

我不确定这个错误是否与单元格数量的差异有关？有什么建议吗？

编辑：

根据 Bastien 的评论，我调查了结构并得到以下信息：

> str(as.matrix(pxy[,-1]))
  num [1:27, 1:2] 8.1 8.14 8.22 8.97 9.01 ...
  - attr(*, "dimnames")=List of 2
  ..$ : chr [1:27] "1" "8" "33" "583" ...
  ..$ : chr [1:2] "y" "x"

> str(as.matrix(lxy[,-1]))
  logi[0 , 1:2] 
  - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:2] "y" "x"

建议？

Answer 1

LoBu 是正确的，您的 lxy 数组为空。目前还不清楚哪里出了问题 - 您的 PCA 可能失败，b 的 bin 大小计算可能失败。如果您尝试匹配像 Hamann 等人那样的分箱气候值，您的 pxy 数据框也太短了。 2015 年。如果不近距离查看您的数据，就无法说出如何解决此问题 - 我建议检查您的 PCA 输出和基础气候栅格。

你的学习区域有多大？附录 3 是迄今为止最复杂的气候模拟方法。我建议尝试附录 2，它速度较慢，但​​如果您不是在高分辨率下工作，它就可以正常工作。这是代码，以供快速参考。

library(SDMTools)       # install package to read and write ESRI ASCII grids
present <- asc2dataframe("C:\Your Path\MAT6190.asc")
future  <- asc2dataframe("C:\Your Path\MAT2020s.asc")

t <- 0.25               # plus/minus threshold to define climate match
t <- 1/(t*2)            # inverse for rounding, double for plus/minus

x <- present$x                    # vector of grid cell x coordinates
y <- present$y                    # vector of grid cell y coordinates
p <- round(present$var.1*t)/t     # vector of rounded present climate values 
f <- round(future$var.1*t)/t      # vector of rounded future climate values 
d <- vector(length=length(p))     # empty vector to write distance to climate match

u     <- unique(p)[order(unique(p))]    # list of unique climate values in p
match <- function(u){c(which(u==f))}    # function finding climate matches of u with f
m     <- sapply(u, match)               # list of climate matches for unique values

for(i in 1:length(p)){                  # loop for all grid cells of p
   mi   <- m[[which(u==p[i])]]          # recalls list of climate matches for p[i]
   d[i] <- sqrt(min((x[i]-x[mi])^2 + (y[i]-y[mi])^2))    # distance to closest match
   }

# writes out log10 speed and distance multiplied by 100 in ESRI ASCII format
# conversion: -200=0.01km, -100=0.1km, 0=1km, 100=10km, 200=100km etc.
d[d==Inf] <- 100000  # sets no analogue to 10,000km
out=cbind(y,x,logDist=round(log10(d)*100),logSpeed=round(log10(d/50)*100))
dataframe2asc(out)