similar to: a priori power analysis for glm, family = poisson

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

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",

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

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

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

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

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

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|)

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

I have a spatial salinity field s and a model g(s) ~ Xb where the X comes from slightly modified GAM basis functions. I am trying to deal with the following set of requirements: 1. The underlying physics are linear, and plain salinity (the identity link) is the correct response to my covariates. 2. Dispersion (variance or sd) is almost certainly proportional to the mean. 3. The data s(x,y)

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 R-helpers, I would like to compare the fit of two models, one of which I fit using lm() and the other using glm(family=poisson). The latter doesn't provide r-squared, so I wonder how to go about comparing these models (they have the same formula). Thanks very much, Mark Na [[alternative HTML version deleted]]

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

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

Dear R-help list, I am trying to construct a lattice histogram using 3 factors. My dataframe looks like this: (simulating a waterbalance over groundwater with different salinities) s days model EC EC_max 0.4 1 "A" 10 9 0.42 2 "A" 10 9 0.44 3 "A" 10 9 : : : :

Hi, I have done the interpolation for my data and I was able to create the contours in multipanel with the help of Pascal. Now, I want to clip the contour with the shapefile. I want only the portion of contour to be displayed which falls inside the boundary of the shapefile. The data mydata.csv can be found on https://www.dropbox.com/s/khi7nv0160hi68p/mydata.csv The data for shapefile can be

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"

R-help, I have the following file I want to import to R (some lines removed) Calibrated CTD data for station:00280001 Calibrated:23/8 2001, Salinity Unsmoothed, Fluorescence Uncalibrated Maximum observed depth: 36 m QUAL has one digit for each of pressure, temp., sal. and fluor. QUAL=1:Uncal., QUAL=2:OK, QUAL=6:Interp., QUAL=9:No data