search for: nyear

I've ported somebody else's rather cumbersome Matlab model to R for colleagues that want a free build of the model with the same type of I/O. The Matlab model reads a text file with the initial parameters specified as: C:\Data\Carluc\Rport>more Params.R # Number of years to simulate nYears = 50; # Initial year for graphing purposes year0 = 1970; # NPP/GPP ratio (cpp0 unitless) fnr = 0.30; # Quantum efficency qe = 0.040; That is, there are four input variables (for this run - there can be many more) written in a way that R can understand them. In R, I can have the model source the...

Hi, I am new to R community and I have a question on panel configurations in the Trellis package. Particularly, I have the following code: require(lattice) plotTable <- NULL Date <- seq(as.Date("2006-11-01"), as.Date("2009-12-01"), by = 1) nYear <- length(unique(format(Date,"%Y"))) plotTable$Date <- as.Date(paste(unique(format(Date, "%Y-%m")), "-01",sep = "")) nDate <- length(plotTable$Date) plotTable$Date <- rep(plotTable$Date, each = 10) plotTable$Date <- factor(format(plotTable$Date...

...'m admitting defeat. Can anybody help? Object example3 has been read in using read.table. The statement works fine in S-Plus. When I run the same statement in R the column Prefix, which is read in using the as.character coercion, is wacky. It reads in as numeric. > example3 nRows nWaves nYears Noise Amp Freq AmpTwo FreqTwo Prefix 1 1 1 1000 0.02 0.01 2 NA NA "test" 2 2 1 1000 0.02 0.02 2 NA NA "test2" 3 3 1 1000 0.02 0.03 2 NA NA NA 4 4 1 1000 0.02 0.04 2 NA NA...

Hi, I'm trying to draw a 2D plot using multiple tints of red. The (simplified) setup is the following: || year | x | y || My idea is that each year is plotted with a different tint of red. Older year (lightest) -> Later year (darkest). I've managed to plot this with different scales of grays simply by doing: palette(gray(length(years):0/length(years))) before the plot and for each

...r a way around it that would be most appreciated! Thank you! -------------- Function requires the commands already listed above: FMAT <- matrx(0, nrow=4, ncol=4, byrow=TRUE) #This is the matrix that will store the frequency of each possible transition occurring over the 4 years nboxes <- 4 nyears <- 4 for(row in 1:nboxes) { for(col in 1:(nyears-1)) { FMAT[boxes[row,col+1], boxes[row,col]] <- boxes[boxes[row, col+1], boxes[row,col]] #This is the line of code I have been struggling with an am unsure about. I have tried #various versions of this and keep getting an assortment of error...

Hey all, I feel like the solution to this problem should be relatively simple, but for some reason I can't find answers or come up with my own solution. Given the dataframe: (SpA and SpB not important, want to look at distribution of cooccurance for each year) Year SpA SpB Coocc 2000 0 2000 2 2000 1 2001 8 2001 2 2001 0 2001 0 2002 1 2002 2 How can I apply different functions to

...s sequence. for(j in 1:(varsize[4]-1)) temp <- get.var.ncdf(nc=input, varid="p_foraging",c(1,1,j),c(varsize[1],varsize[2],1)) filled.contour(x, y, temp, color = terrain.colors, plot.title = title(main = paste("Everglades Wood Stork Foraging Potential \nYear", (2000+j)), xlab = "UTM East", ylab = "UTM North") , plot.axes = { contour(temp, add=T) axis(1, seq(450000 , 580000, by = 10000)) axis(2, seq(2800000,4000000, by = 10000)) }, key.title = title(main=&quo...

...sp,] dmnd <- tsrSUB[is.na(tsrSUB$cDeviceClass),] if(nrow(dmnd) != 1) warning(paste("cSpillingPointID", sp, "has more than one demand time Series. Used first.")) tsrValCount <- getTimeSeries(chn, tsrSUB[!is.na(tsrSUB$cDeviceClass),]$cTimeSeriesID, nDaysBack = 366*nyears) tsrValDmnd <- getTimeSeries(chn, dmnd$cTimeSeriesID[1], nDaysBack = 366*nyears) sequence <- timeSequence(from = format(start(tsrValDmnd), '%Y-%m-%d'), to = format(end(tsrValDmnd), '%Y-%m-%d'), by = 'day') if(format(start(tsrValDmnd), '%H') != '00&...

Hi there I'm working with ncdf data. I have different dataset for 4 runs, 4 seasons and 3 timeslices (48 datasets in total). The datasets have the following dimensions: 96 longitudes, 48 latitudes and 30 time steps. To read all of them in, I wrote the following loop: runs <- c("03","04","05","06") years <- c(1851,1961,2061) seasons <-

Hi there, I would like to make a 'bumps chart' like the ones described e.g. here: http://junkcharts.typepad.com/junk_charts/bumps_chart/ Purpose: I'd like to plot the proportion of people in select countries living for less then one USD pr day in 1994 and 2004 respectively. I have already constructed a barplot - but I think a bumps chart would be better # The barplot and data

I am trying to compare Vbert growth curves from several years of fish data. I am following the code provided by: http://www.ncfaculty.net/dogle/fishR/gnrlex/VonBertalanffy/VonBertalanffy.pdf. Specifically the section on VBGM Comparisons between groups. ? This code is pretty cut and dry. I am able to run it perfectly with the "fake" data that is provided. But when I run it with my own

Hi ggplot experts, I need to plot two time series of stacked data: a barchart with bars for each month. To compare the data of two years I need to combine both time series with in a single graph via position=doge. How should I do that? I tried the following scenario: I added two layers with the time series of the stacked data for both years. That worked well exept the bars are obscuring each

Dear all, I'm looking for primary help at aggregating table() results and at writing a loop (if useful) My dataset ( http://goo.gl/gEPKW ) is composed of 23k rows, each one representing a point in the space of which we know the land cover over 10 years (column y01 to y10). I need to analyse it with a temporal sliding window of 5 years (y01 to y05, y02 to y06 and so forth) For each period