错误：nrow（ref）和nrow（target）必须> 0？

Question

基于Unable to writeRaster for signature "rasterPCA", "character"，我获得了两个栅格，PC1和PC2是一组气候变量。但是，无论具有相同的范围和分辨率，当加载到R时，我的全球环境中的单元格数目不同。

下面是我使用的代码，它来自2015年Hamann等人的附录我得到这个错误：

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 
} 
} 

在的结束for循环中，我得到的错误如下：

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

我不知道如果这个错误有事情做与差异的数细胞？有什么建议么？

编辑：

基于巴斯蒂安的评论，我调查了结构，我得到这个：

> 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，你的lxy数组是空的。目前尚不清楚哪里出错了 - 您的PCA可能已经失败，您的箱尺寸计算b可能失败。你的pxy数据帧也太短了，如果你试图匹配像Hamann等人的装箱气候值。如果不仔细观察数据，就无法说明如何解决这个问题 - 我会建议检查您的PCA输出和基础气候栅格。

你的学习区有多大？附录3是迄今为止最复杂的气候模拟方法。我会建议尝试附录2，如果你不是在高分辨率下工作，这会比较慢但工作得很好。这里是代码，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)