similar to: weights in quantile regression

Hi - I am doing a monte carlo experiment that requires to do a linear regression of a matrix of vectors of dependent variables on a fixed set of covariates (one regression per vector). I am wondering if anyone has any idea of how to speed up the computations in R. The code follows: #regression function #Linear regression code qreg <- function(y,x) { X=cbind(1,x) m<-lm.fit(y=y,x=X)

Hi List, I have a time series of 122 values, actualy it is a time series of daily indian monsoon rainfall. now i want to filter this time series for a particular oscilation say 10 to 20days oscilation. i want to find out what amount of variance is explained by this mode. Which package is available in R for this purpose. and how to calculate nquest frequancy of this series. any help is much

Mike, Do something like: require(rms) dd <- datadist(mydatarame); options(datadist='dd') f <- Rq(y ~ rcs(age,4)*sex, tau=.5) # use rq function in quantreg summary(f) # inter-quartile-range differences in medians of y (b/c tau=.5) plot(Predict(f, age, sex)) # show age effect on median as a continuous variable For more help type ?summary.rms and ?Predict Frank ------------

I am a student doing my MSc Research Methods, i am working on my thesis research on analysing and modelling of crop failure risks due to drought in selected districts in Malawi. The analysis and modelling will focus on two crop stages of development: just after planting and flowering stages. I have rainfall, temperature, Relative humidity, wind speed, coordinate location data for the past 30 years

Hello, I'm looking for a way to plot different data series on one graph. I have a series of hourly rainfall and quarterly flow measurements (i.e. 4 times an hour) of a catchment. The rainfall should be plotted in bars, the flow as a line. Both on the same X axe (time) but with different Y axes. The problem is the plot() function does not support add=TRUE... Furthermore I'm not sure

Hi @all, I am biting my nails with the following problem: I have a data set of daily rainfall measurements for the last 20 years. What I want to do is calculate the daily cumulative sum of rainfall but only for every year which means that the cumulative sum has to be reset each year. After the calculations I want to plot each year of cumulative rainfall as a separate line in one graph preferably

Greetings R users, My goal is to generate quartile groups of each variable in my data set. I would like each experiment to have its designated group added as a subsequent column. I can accomplish this individually with the following code: brks <- with(data_variables, cut2(var2, g=4)) #I don't want the actual numbers, I need a numbered group data$test1=factor(brks,

Hi I think I have been struggling to use replace correctly, I usually work my way around this using a loop, but I think this is in fact inefficient. I have a dataset with runoff from three plots and associated rainfall. However either the datarecording was sloppy, or the rainfall very patchy. So I am trying to remove data from my dataset for which the runoff is larger than the rainfall on the

I am trying to model the observed leaching of wood preservative chemicals from treated wood during an outdoor experiment where leaching is caused by rainfall events. For each rainfall event, the amount of rainfall was recorded as well as the amount of preservative chemical leached. A number of climatic variables were measured, but the most important is the amount of rainfall. I have tried a

Thank you Sarah and Mike for your explanations. My final objective is to produce maps (png image or any kind of extension I can import in LaTeX) where rainfall data are interpolated, using the Inverse Distance method or Kriging. My input file (pointfile.csv in the reported example) reports the station code, lat and long of the meteorological station and the rainfall value (which might be the

Hi, For my master thesis I have 24 micro-plots on which I did measurements during 3 months. The measurements were: - Rainfall and runoff events throughout 3monts (runoff being dependant on the rainfall, a coefficient (%) has been made per rainfall event and per 3 months) - Soil texture (3 different textures were differentiated) - Slope (3 classes of slopes) - Stoniness (one time measurement)

I have data that looks like lake,loglength,logweight 1,2.369215857,1.929418926 1,2.426511261,2.230448921 1,2.434568904,2.298853076 1,2.437750563,2.298853076 1,2.442479769,2.230448921 1,2.445604203,2.356025857 ... 102,2.722633923,3.310268367 102,2.781755375,3.502153893 102,2.836324116,3.683407299 102,2.802773725,3.583312152 102,2.790285164,3.546419267 102,2.806179974,3.599118565

Dear All, I am a research student in environment. I have only little programming knowledge. I am currently doing the last project about rainfall impact on ground water quality in an area. It happens that I have to use R to read rainfall data (3 dimension) from ASC file (*.asc), and then write them into one NCDF file (*.nc). I have been working very hard on study R, but I

Why am I getting a wrong result for quartiles? here is my code: > cbiomass = c(910, 1058, 929, 1103, 1056, 1022, 1255, 1121, 1111, 1192, > 1074, 1415) > summary(cbiomass) > IQR(cbiomass) The result R gives me is: For the summary > Min. 1st Qu. Median Mean 3rd Qu. Max. 910 1048 1088 1104 1139 1415 For IQR > 91.25 ********* The true Q1 is 1039

I have a time series of rainfall in a dataframe. I would like to be able to aggregate this using a sliding window approach- i.e. a new 24 hourly total is calculated for each hours rainfall. I'm struggling to understand how this might be achieved - currently I've tried looping a sum function to re-calculate a new total at every stage of the loop. for (inp[[9]] in

Hello, I'm sorry if this is a repeat, a few choice searches into google didn't help and I found no search for the archives. I can no longer put of moving away from UW-IMAP. Dovecot certainly seems to be the right choice, but I am having problems getting it to do what I need it to for all my mail. The machine is running the dist version, 0.99.12. I have a system mailbox and a large MH

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R How to estimate the panel data quantile regression if the regression contains no constant term? I tried to change the code of

>[Rd] boxplot() defaults {was "boxplot in extreme cases"} >Martin Maechler maechler at stat.math.ethz.ch >Mon Nov 8 10:36:42 CET 2004 > > AndyL> Try: > > AndyL> x <- list(x1=rep(c(0,1,2),c(10,20,40)), > x2=rep(c(0,1,2),c(10,40,20))) > AndyL> boxplot(x, pars=list(medpch=20, medcex=3)) > > AndyL> (Cf ?bxp, pointed to from

Hi, You might get more help from the R-sig-geo list, which is devoted to spatial topics. However. The *.asc file is an ArcGIS raster export format. You should use whatever the appropriate import commands are for your own gridded rainfall data. If you have a different format, you might or might not be able to import it directly with raster. ?raster will tell you more about the kinds of formats

Hi, I'm new to R and tried a search but couldn't find what I was looking for. I have some data as a csv file with columns:- longditude latitude year month rainfall region What I need to do is produce a monthly time series for each region, where region is an integer id and where each time point in the series is the monthly average of rainfall for each location in that region. Basically