Question

我正在尝试使用RasterStack或focal {raster}在localFun {raster}中使用不同图层运行多元回归。在similar post和raster reference manual的帮助下，我的代码可以正常使用单RasterLayers作为输入（请参阅可重复的，尽管可能是'笨重'，下面的示例）。但是，我想使用RasterStack中的不同图层执行此操作，如下面代码的{SECTION2}中所述。我非常感谢任何建议。

谢谢

CODE：

library(raster)

#%%%%%%%%%%%%%%%%%%%%%
## SECTION1
#%%%%%%%%%%%%%%%%%%%%%

# create test data
set.seed(0)
resp = expl = raster(nrow=10, ncol=10)
# response variable
resp = setValues(resp,runif(100,min=15,max=45))
# explanatory variable
expl = setValues(expl,runif(100,min=2,max=6))
expl = expl * resp
resp[1:5] = NA; expl[1:5] = NA # add some NA values
par(mfrow=c(1,2))
plot(resp); plot(expl)
#..............................................................

# check global lm() results
data1.df = na.omit(as.data.frame(stack(list(resp=resp,expl=expl))))
head(data1.df)
data1.lm = lm(resp ~ expl, data=data1.df)
(data1.lmSum = summary(data1.lm))
data1.lmSum$coefficients[1];data1.lmSum$coefficients[2];data1.lmSum$coefficients[8]
data1.lmSum$r.squared
data1.lmSum$sigma
# pf(data1.lmSum$fstatistic[1], data1.lmSum$fstatistic[2], data1.lmSum$fstatistic[3],lower.tail = FALSE)      
#..............................................................

# lm function for focal {raster} with RasterLayers
# output coefficients, r-squared, residual standard error and p-value(F stat)

# Calculate focal ("moving window") weight
fw = focalWeight(resp, 2, "Gauss")

# focal regression:
lm.focal = function(x, y, ...) {
  if(all(is.na(x) & is.na(y))) {NA}
  else {
    m = lm(y~x)
    summary(m)$r.squared #r-squared
    # summary(m)$coefficients #intercept and slope together
    #---> Error in setValues(x, value) : cannot use a matrix with these dimensions
    # summary(m)$coefficients[1] #intercept
    # summary(m)$coefficients[2] #slope
    # summary(m)$coefficients[8] #p-value
    # summary(m)$sigma #residual standard error
  }
}
#---> How to output all at once?

lm.focal.out1 = localFun(resp, expl, w=fw, fun=lm.focal, na.rm=TRUE)
plot(lm.focal.out1)

#%%%%%%%%%%%%%%%%%%%%%
## SECTION2
#%%%%%%%%%%%%%%%%%%%%%

# create test data
set.seed(1)
resp = expl1 = expl2 = expl3 = expl4 = raster(nrow=10, ncol=10)
# x1 response variable
resp = setValues(resp,runif(100,min=15,max=45))
# x3 explanatory variables
expl1 = setValues(expl,runif(100,min=2,max=6))
expl1 = expl1 * resp
expl2 = expl1 * resp/runif(100,min=1,max=4)
expl3 = ((expl1 * resp) / 1.5 )/10
expl4 = ((expl1 * resp) / runif(100,min=0.5,max=2))/100
# add some NA values
resp[1:5] = NA; expl1[1:5] = NA; expl2[1:5] = NA; expl3[1:5] = NA; expl4[1:5] = NA 

#stack RasterLayers
stack1 = stack(list(resp=resp,expl1=expl1,expl2=expl2,expl3=expl3,expl4=expl4))
# par(mfrow=c(1,1))
plot(stack1)
#..............................................................

# check global lm() results
stack1.df = na.omit(as.data.frame(stack1))
head(stack1.df)
stack1.lm = lm(resp ~ expl1+expl2+expl3+expl4, data=stack1.df)
(stack1.lmSum = summary(stack1.lm))
stack1.lmSum$coefficients[1]
stack1.lmSum$coefficients[2];stack1.lmSum$coefficients[3];stack1.lmSum$coefficients[4];stack1.lmSum$coefficients[5]
stack1.lmSum$r.squared
stack1.lmSum$sigma
pf(stack1.lmSum$fstatistic[1], stack1.lmSum$fstatistic[2], stack1.lmSum$fstatistic[3],lower.tail = FALSE) 
#..............................................................

# lm function for focal {raster} with RasterStack
# output coefficients, r-squared, residual standard error and p-value(F stat)

# Calculate focal ("moving window") weight
fw.s = focalWeight(stack1, 2, "Gauss")

# focal regression with raster stack:
lm.focal.stack = function(x, ...) {
  if(all(is.na(x) )) {NA}
  else {
    m = lm(x[1]~x[2]+x[3]+x[4]+x[5])
    summary(m)$r.squared #r-squared
    # summary(m)$coefficients #intercept and slope together
    #---> Error in setValues(x, value) : cannot use a matrix with these dimensions
    # summary(m)$coefficients[1] #intercept
    # summary(m)$coefficients[2] #slope
    # pf(summary(m)$fstatistic[1], summary(m)$fstatistic[2], summary(m)$fstatistic[3],lower.tail = FALSE) #p-value
    # summary(m)$sigma #residual standard error
  }
}
#---> How to output all at once?

lm.focal.stack.out1 <- focal(stack1, w=fw.s, fun=lm.focal.stack, na.rm=TRUE)
#---> unable to find an inherited method for function ‘focal’ for signature ‘"RasterStack"’
#plot(lm.focal.stack.out1)

#-----------------------------------------------------------
> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] raster_2.5-8 sp_1.2-3    

loaded via a namespace (and not attached):
[1] rgdal_1.1-10    tools_3.3.1     Rcpp_0.12.5     grid_3.3.1      lattice_0.20-33

Answer 1

不确定你是否仍然需要这个答案，但是我遇到了同样的问题并且创建了一个名为localFunStack的函数来完成从本地函数作为rasterStack对象的向量输出的工作，有一点点黑客来获得正确的图层名称：

# localFun modified to write out a layer stack
localFunStack <- function(x, y, ngb=5, fun, ...) {

  compareRaster(x,y)
  rasterList <- list()
  nc1 <- 1:(ngb*ngb)
  nc2 <- ((ngb*ngb)+1):(2*(ngb*ngb))

  if (canProcessInMemory(x, n=2*ngb)) {
    vx <- getValuesFocal(x, 1, nrow(x), ngb=ngb)
    vy <- getValuesFocal(y, 1, nrow(y), ngb=ngb)
    v <- apply(cbind(vx, vy), 1, function(x, ...) fun(x[nc1], x[nc2], ...))

    for (j in 1:nrow(v)) {
      if (length(rasterList) < j) { 
        rasterList[[j]] <- raster(x)
      }
      values(rasterList[[j]]) <- v[j,]
    }
  } 

  else {
    tr <- blockSize(out)
    pb <- pbCreate(tr$n, label='localFun', ...)

    for (i in 1:tr$n) {
      vx <- getValuesFocal(x, tr$row[i], tr$nrows[i], ngb=ngb)
      vy <- getValuesFocal(y, tr$row[i], tr$nrows[i], ngb=ngb)
      v <- apply(cbind(vx, vy), 1, function(x, ...) fun(x[nc1], x[nc2], ...))
      for (j in 1:nrow(v)) {
        if (length(rasterList) < j) { 
          rasterList[[j]] <- raster(x)
        }
        rasterList[[j]] <- writeValues(rasterList[[j]], v[j,], tr$row[i]) 
      }
    }
  }
  return(stack(rasterList))
}

# local regression function
lm.focal <- function(x, y, ...) {
  if(all(is.na(x) & is.na(y)) || all(is.na(x)) || all(is.na(y))) {rep(NA, 8)}
  else {
    m <- lm(y~x)
    coef <- summary(m)$coef
    if (nrow(coef) == 1) { # Add NAs for cases where the response is constant
      coef <- rbind(coef, rep(NA, 4))
      rownames(coef) <- rownames(nm)
    }
    coef <- as.vector(coef)
    names(coef) <- c( outer( rownames(nm), colnames(nm) ,FUN=paste ,sep=" "))
    coef
    # summary(m)$r.squared #r-squared
    # summary(m)$sigma #residual standard error
  }
}

lm.focal.out = localFunStack(expl, resp, ngb=5, fun=lm.focal, na.rm=TRUE)

m <- lm(resp ~ expl)
nm <- summary(m)$coefficients    
names(lm.focal.out) <- c(outer(rownames(nm), colnames(nm), FUN=paste, sep=" "))
plot(lm.focal.out)

使用RasterStack进行焦点{raster}回归

1 个答案: