Hello,
we have 80 text files with matrices. Each matrix represents a map (rows for
latitude and columns for longitude), the 80 maps represent steps in time. In
addition, we have a vector x of length 80. We would like to compute a
regression between matrices (response through time) and x and create maps
representing coefficients, r2 etc. Problem: the 80 matrices are of the size
4000 x 3500 and we were running out of memory. We computed line by line and
the results for each line were appended to output grids. This works. But -
for each line, 80 text files must be scanned and output must be written. And
there are several for-loops involved. This takes a lot of time (about a
week). I read the contributions related to speeding up code and maybe
vectorizing parts of the procedure could help a bit. However, I am a
neophyte (as you may see from the code below) and did not find a way by now.
I would appreciate very much any suggestions for speeding up the procedure.
Thanks, Zarza
The code (running but sloooooow):
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
regrid <- function (infolder, x, outfolder) {
# List of input files
setwd (infolder)
filelist <- dir (pattern=".*.asc$", full.names = F)
# Dimensions (making use of the header information coming with
# the .asc-input files, ESRI-format)
hd <- read.table (filelist [1], nrows = 6)
cols <- hd[1,2]
rows <- hd[2,2]
times <- length (filelist)
items <- 4 + ncol (x)
# Prepare output
out1 <- matrix (numeric (times * cols), ncol = cols)
out2 <- matrix (numeric (items * cols), ncol = items)
out3 <- as.numeric (items)
# Prepare .asc-files
filenames <- c("R2", "adj.R2", "p", "b0", colnames (x))
for (i in 1:items) {
write.table (hd, file = paste (outfolder, filenames [i],".asc",sep =""),
quote=F, row.names=F, col.names=F) }
rm (hd)
# Prepare regression
xnam <- paste ("x[,", 1:(ncol(x)),"]", sep="")
form <- paste("y ~ ", paste(xnam, collapse="+"))
rm (xnam)
# Loop through rows
for (j in 1:rows) {
getgrid <- function (j) {
print (paste ("Row",j,"/",rows),quote = F)
# Read out multi-temporal response values for one grid-row of cells
for (k in 1:times)
{
getslice <- function (k) {
values <- scan (filelist [k], what=0, na.strings = "-9999",
skip = (5 + j), nlines = 1, nmax = cols, quiet=T)
values }
out1[k,] <- getslice (k)
}
# Regression
for (l in 1:cols)
{
y <- as.vector (out1 [,l])
if (length (y) > length (na.omit (y)))
{
setNA <- function (l) {
NAs <- rep (NA, length (out3))
NAs }
out2[l,] <- setNA (l)
}
else
{
regression <- function (l) {
model <- lm (as.formula(form))
out3[1] <- summary (model)$r.squared
out3[2] <- summary (model)$adj.r.squared
f <- summary (model)$fstatistic
out3[3] <- 1-pf(f[1],f[2],f[3])
out3[4:items] <- coef(model)[1:(1 + ncol(x))]
out3 }
out2[l,] <- regression (l)
}
}
out2
}
fillrow <- getgrid (j)
# Append results to output files
for (m in 1:items) {
write.table (t(fillrow [,m]), file = paste (outfolder, filenames [m],
".asc", sep =""), append=T, quote=F, na = as.character (-9999),
row.names = F, col.names = F, dec=".") }
}
}
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