[R] Lots of huge matrices, for-loops, speed from Zarza on 2008-07-07 (R help archive)

[R] Lots of huge matrices, for-loops, speed

From: Zarza <s.schmidtlein_at_uni-bonn.de>
Date: Sun, 06 Jul 2008 08:39:52 -0700 (PDT)

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)  
          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) 

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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