You are doing a lot of unnecessary work inside each loop. For example the
function
regression() is defined as many as rows*cols times, but only needs to be
defined once.
Similarly getgrid() and getslice() (which does need to be defined inside the j
loop, but
not inside the k loop).
HTH
Ray Brownrigg
On Mon, 07 Jul 2008, Zarza wrote:
> 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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