similar to: Modifying the design matrix X in GAMS to suit data assimilation

Hello, I'm trying to find out if something has been written in R regarding data assimilation and inverse modeling. These searches do not return anything that look like Kalman filter variations (EK, SEEK, ROEK, etc.) help.search("assimilation") help.search("inverse model") Regards, ************************************************** AVIS DE NON-RESPONSABILITE: Ce

This is my first time running GAMs in R. My csv file has these column headings: "X" "Y" "Sound" "Atlantic" "Blacktip" "Bonnet" "Bull" "Finetooth" "Lemon" "Scalloped" "Sandbar" "Spinner" "Abundance" "Diversity"

My x axis is a series of daily dates (e.g., 01/01/2000, 01/02/2000, etc.) from 2000 to end of 2008. The default only gives me 4 ticks. I want more. Why doesn't this work? sdate<-as.POSIXct(strptime(date,format="%m/%d/%Y")) plot(ppt~sdate,type="l",ylim=c(0,47),col=1,lwd=1,pch=16,ylab="Salinity, psu",xlab="Year",las=1,main="Duck Key

Hello, I'm doing the following: library(ncdf) library(fields) library(animation) saline <- open.ncdf("salinity_1990.nc") salt = get.var.ncdf(nc=saline, varid="Salinity") # create an animation of the complete temporal domain in the ncdf file. saveHTML({ for (i in 1:364) { image.plot(salt[, , i]) } }, img.name = "salinity.img",

Dear Mr. Simon Wood, dear list members, I am trying to fit a similar model with gam from mgcv compared to what I did with BayesX, and have discovered the relatively new possibility of incorporating user-defined matrices for quadratic penalties on parametric terms using the "paraPen" argument. This was really a very good idea! However, I would like to constraint the coefficients

> library(SpatioTemporal) > library(plotrix) > library(maps) > palay.cov<-read.csv("C:/Users/BEDANA-PC/Desktop/STThesisWD/Thesis Data/palay.covar.csv") > palay.o<-read.table("C:/Users/BEDANA-PC/Desktop/STThesisWD/Thesis Data/palay.obs.txt") > palay.obs<-as.matrix(palay.o) > palay.stc<-read.table("C:/Users/BEDANA-PC/Desktop/STThesisWD/Thesis

Hi list. I'm plotting pluviometric (Rain) data as a barplot, and then adding the salinity variable to this plot as lines. Obviously as these Y scales are completely different the salinity appears at the lower part of the graph extremely compacted. I need to plot the line at the exactly same area of the barplot but with its own Y axis (at the right), so the salinity can use the plot area

Dear R Wizards, After much frustration and days of confusion I have finally broken down and am asking for help, which I don’t like doing, but I just can’t figure this one out on my own. I’ve conducted a laboratory experiment testing the effects of temperature and salinity on whether or not a biological event will occur (Go or NoGo). I’ve coded the factors temperature and salinity as factors for

I have a dataset relating the effects of engineering works on the level of salinity in a river before and after the works. I have modelled this using linear mixed effects models to determine if the significance and level of the response to the works. I am wanting to calculate the se of difference at several points. I am not sure of how to calculate the difference and which estimate of se I

Colleagues, I am analyzing data collected during oceanographic cruises. We have conducted many cruises over the last decade. On each cruise we visit ~50 stations. At each station (termed EventNum)we lower an instrument that measures depth, temperature, salinity and oxygen every few seconds as it is lowered through the water. Data from all EventNums on all cruises are stacked, generating a data

Hi, We are analizing the relationship between the abundance of groupers in line transects and some variables. We are using the quasipoisson distribution. Do we need to include the length of the transects as an offset if they all have the same length?? Also, can we include in the gam models variables that are measured at different spatial scales? We have done an analysis to see what variables

Hi Everyone, Recently I got puzzled by the function read.table, even though I have used it for a long time. I have such a file (tmp.txt, 2 rows and 3 columns, with a space among columns): 1 2'-PDE 4 2 3'-PDE 5 if I do: a = read.table("tmp.txt", header = F, quote = "") a V1 V2 V3 1 1 2'-PDE 4 2 2 3'-PDE 5 Everything is fine. However, if I do: a

Hi, I have used R for some time, but managed to avoid writing loops. But this time I am afraid there is no way around it. I have a dataframe with time and salinity (see below). I would like to extract the time intervals where salinity changes by less than 0.05. So using the values below this would mean that a subset was made from 2003-07-19 to 2003-07-24, where the change in salinity exceeded

Hello all, I have a huge data set that I'm cleaning up a bit. I am extracted the means per condition, but also need to get the n. Strangely enough I am unable to find a function that could actually pull this off. I am unable to use replications or count the rows using nrow. What I am trying to obtain is the number of observations fitting something along the lines of

I am very new to R and programming and thank you in advance for your patience and help with a complete novice! I am working with a large multivariate data set that has 10 explanatory environmental variables (e.g. temp, depth) and over 60 response variables (each is a separate species). My data frame is set up like the simplified version below: JulianDay Temperature Salinity Depth Copepod

Oh sorry. An example say lots more than words. The data below, when submitted to twoord.plot return the mentioned error. Rain are bars and Salt lines, the bars appear and the error occurs with the salt data. Point Rain Salt Fev/03 365.6 13 Mar/03 235 18 Abr/03 115.1 18 Mai/03 47.4 18.75 Jun/03 112 15 Jul/03 156.8 17 Ago/03 66.1 15 Set/03 149.8 14 Out/03 167,1 11.5 Nov/03 269.3 17.5 Dez/03 283.7

Dear Mr. and Mrs. I'm very grateful for these software and this list. My question is: when a use linear multiple regression (lm()) for my data, abundance ichthyoplankton ~ salinity + temperature + month of the year(f1 is a factor: 1 for january, 2 for february, ..., 12 for december), the summary() of results is ... Coefficients: Estimate Std. Error t value Pr(>|t|)

Dear R listers, When using smooth.spline to interpolate data, results are generally good. However, some cases produce totally unreasonable results. The data are values of pressure, temperature, and salinity from a probe that is lowered into the ocean, and the objective is to interpolate temperature and salinity to specified pressures. While smooth.spline provides excellent values at the

summary: Specifically, how does one do stack/FIFO operations in R? Generally, how does one code functions with side effects in R? details: I have been a coder for years, mostly using C-like semantics (e.g., Java). I am now trying to become a scientist, and to use R, but I don't yet have the sense of "good R" and R idiom (i.e., expressions that are to R what (e.g.) the Schwartzian

Dear all, Does any know how to solve PDE with R? The archive list refers to the use of ODE if PDE are parabolic. I am not a mathematician and this does not mean anything for me! help would be very appreciated. Many thanks -- ___________________________________________________ Christophe NGUYEN UMR 1220 INRA-ENITAB Transfert sol-plante et cycle des ?l?ments min?raux dans les ?cosyst?mes