我正在尝试对某些网格化气候数据(观测和模拟预测)应用偏差校正函数。
qmap包具有对气候数据进行偏差校正的功能, 我正在尝试运行以下
library(raster)
library(qmap)
#Create a rasterStack with observed and modelled data
r <- raster(ncol=20, nrow=20)
obs <- stack(lapply(1:100, function(x) setValues(r, runif(ncell(r))))) #observed data
mod <- stack(lapply(1:100, function(x) setValues(r, runif(ncell(r)))))*2 #modelled data (i want this unbiased)
#bias-correction function
f <- function(obs, mod, ...) {
obs <- t(obs)
qm.fit <- fitQmap(obs, t(mod), method="QUANT",qstep=0.01)
t(doQmap(mod, qm.fit, type="linear") )
}
x <- overlay(obs, mod, fun=f)
我收到以下错误
Error in (function (x, fun, filename = "", recycle = TRUE, forcefun = FALSE, :
cannot use this formula, probably because it is not vectorized
anyboby能帮忙吗?
万分感谢
答案 0 :(得分:2)
使用一些光矩阵整形,您可以使用矩阵上的函数而不是栅格,这应该可以工作:
library(qmap)
library(raster)
r <- raster(ncol=20, nrow=20)
obs <- stack(lapply(1:100, function(x) setValues(r, runif(ncell(r))))) #observed data
mod <- stack(lapply(1:100, function(x) setValues(r, runif(ncell(r)))))*2 #modelled data (i want this unbiased)
qm.fit <- fitQmap(t(obs[]), t(mod[]), method="QUANT",qstep=0.01)
bias_corrected <- doQmap(t(mod[]), qm.fit, type="linear")
bias_corrected_arr <- as.array(t(bias_corrected))
#reshape array
dim(bias_corrected_arr) <- c(20,20,100)
# convert to rasterbrick
mod_Bcorrected <-setValues(brick(mod,values=FALSE),bias_corrected_arr)
# check output
> mod_Bcorrected
# class : RasterBrick
# dimensions : 20, 20, 400, 100 (nrow, ncol, ncell, nlayers)
# resolution : 18, 9 (x, y)
# extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
# coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
# data source : in memory
# names : layer.1, layer.2, layer.3, layer.4, layer.5, layer.6, layer.7, layer.8, layer.9, layer.10, layer.11, layer.12, layer.13, layer.14, layer.15, ...
# min values : 2.136529e-03, 1.316528e-02, 4.380183e-03, 4.305932e-03, 5.293415e-03, 5.505609e-04, 9.494019e-05, 8.768495e-05, 8.171266e-04, 6.158992e-04, 3.248452e-03, 8.445294e-04, 2.856641e-03, 1.135400e-03, 3.255120e-03, ...
# max values : 0.9981889, 0.9995128, 0.9931242, 0.9975992, 0.9998942, 0.9986233, 0.9989456, 0.9952701, 0.9989686, 0.9958882, 0.9968628, 0.9975774, 0.9984780, 0.9910168, 0.9983078, ...