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The University of Miami's Rosenstiel School of Marine and Atmospheric Sciences (RSMAS) is seeking a research associate to work with hydrographic data. The objective of the project is to develop statistical models for estimating salinity and density from observations of temperature together with knowledge of location and time. Because the relationship between salinity and temperature can be

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

I am exploring the use of loess for oceanographic applications and would like to plot the locations (longitude and latitude) points where the models (salinity~temperature*longitude*latitude,parametric="temperature") are fitted. Chambers and Hastie(1993) explains the locations are nodes of a k-d tree. but I cannot find anything about accessing this information. It would be useful to

Can someone point me to the appropriate functions for fitting multiple statistical models that are coupled to each other. The data are measurements of salinity s and temperature t at stations id and pressures at p as well as surface elevations h at stations id. The problem is, for any new station, to estimate s at all p, given t at all p and given h. If h is ignored, the for each p, there

Hello, I've been unsuccessfully trying to reproduce a sem from Grace et al. (2010) published in Ecological Monographs: http://www.esajournals.org/doi/pdf/10.1890/09-0464.1 The model in question is presented in Figure 8, page 81. The errors that I've been getting are: 1. Using a correlation matrix: res.grace <- sem(grace.model, S = grace, N = 190) Warning message: In sem.default(ram

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",

Background: OS: Linux Mandrake 10.1 release: R 2.0.0 editor: GNU Emacs 21.3.2 front-end: ESS 5.2.3 --------------------------------- Colleagues I'm plotting fish egg densities in temperature-salinity space (i.e. mean temperature ~ mean salinity | fish egg densities) for two survey years. The bivariate plot is overlaid on a temperature ~ salinity plot (not means) showing the distribution of

I have Samba 2.2.3a running on Tru64 Unix v4.0f (?) I have Win NT4.0 (sp6a) as our local PC OS. I have created a Samba password file for users. I've tried several permutations, but currently I have security = user encrypt passwords = yes unix password sync = yes username map = /../../../usermap.txt I also have standard password encryption on the NT side, i.e. not plaintext. If I sync

I forgot to add that I first gave a starting value for K. Nonlinear least squares won't work because my errors are not normally distributed. Any advide on my maximum likelihood function would be greatly appreciated. ---------------------------- Original Message ---------------------------- Subject: RE: [R] maximum likelihood problem From: "Ravi Varadhan" <rvaradhan at

It turns out I didn't have to filter out the 1-20 values. The code of x[is.finite(x)] did the trick. Thanks!!! Mike ---------------------------- Original Message ---------------------------- Subject: Re: [R] cleaning up a vector From: "Henrique Dallazuanna" <wwwhsd at gmail.com> Date: Fri, October 1, 2010 1:55 pm To: mlarkin at rsmas.miami.edu Cc: r-help

Background: OS: Linux Mandrake 10.1 release: R 2.0.0 editor: GNU Emacs 21.3.2 front-end: ESS 5.2.3 --------------------------------- Colleagues I have solved the miscoding with subscript and groups in panel.superpose. I was neglecting to pass in the variables correctly. "sardine.egg.T.S.space.2001.and.2002.exp" <- function() { library(lattice) library(grid) ##

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

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, 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

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

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 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

I have a data assimilation problem that might be amenable to the use of GAMS, but I am not sure how feasible it is to implement. I was told the R mailing list was a great resource. My observations are spatiotemporal salinity in the San Francisco Bay at a number of instruments over a few days. The thing that I want to fit is the initial condition for a salt transport model at the beginning of this

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