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

[Ray Brownrigg]

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=".") }
> }
> }