similar to: multiple secondary axes

I would like to try a likelihood ratio test in place of waldtest. Ideally I'd like to provide two glm models, the second a submodel of the first, in the style of lrt (http://www.pik-potsdam.de/~hrust/tools/farismahelp/lrt.html). [lrt takes farimsa objects] Does anyone know of such a likelihood ratio test? Chris Elsaesser, PhD Principal Scientist, Machine Learning SPADAC Inc. 7921

Dear R-help members, I'm processing a large amount of MODIS data where quality assessment information is stored as an integer value for each pixel. I have to converted this number to an 8 digit binary flag to get access to the stored quality code (e.g. in2bin(165,8) = 1 0 1 0 0 1 0 1). Unfortunately, I did not manage to find a package providing a fast function to do so. I need to run

Dear R-user, I am a R beginner, and therefore my questions are very basic. I have a simple problem: I would like to plot 100 time series each containing 55 steps. The data are stored in a matrix of 100 columns and 55 rows. The first problem is to load the data from a file: I tried the read.table(), the scan() and the matrix(scan()) options, but I have problems to allocate the single columns.

Hello, I have a problem with using replace() to convert a vector of dates from yyyy-mm-dd to julian date. For example, I type replace(x,2004-05-14,134) and I receive an error: Error in `[<-.data.frame`(`*tmp*`, list, value = 134) : new columns would leave holes after existing columns If I can successfully convert, I have a script that will convert all of the dates in

I have a series of nertCDF files containing global data for a particular variable, e.g. tmin/tmax/precipiation/windspeed/relative humuidity/radiation etc. I get the following information when using *nc_open* function in R: datafile: https://www.dropbox.com/s/xpo7zklcmtm3g5r/gfdl_preci.nc?dl=0 File gfdl_preci.nc (NC_FORMAT_NETCDF4_CLASSIC): 1 variables (excluding dimension variables):

Hi Jim, Thanks for your help, I really appreciate it. Perhaps I'm misunderstanding, but does this formula run different ajustment values for this function? logit(p = doc$value, adjust = 0.025) I'm looking to plot the p-values of different adjustment values. Thanks so much, Kirsten On Wed, Jul 12, 2017 at 8:49 PM, Jim Lemon <drjimlemon at gmail.com> wrote: > Hi Kirsten,

Hello everyone, I have a .csv file with the following format: uniqueID SubjectID Distance_miles Tag 1 1001 5.5 3 2 1001 7 1 3 1001 6.5 1 4 1001 5 1 5 1002

Hi Ben, That's exactly right! Except for each set it's the sample population that is 400, 800 or 300. I want to take 3 samples, each of 100, where only the population differs. I can do this separately, but I'm having trouble putting them all on the same graph. I'd like to have sample on the x axis (1-300) and estimate on the y axis. I want to show how population affects the

Hi Kirsten, Perhaps this will help: set.seed(3) kmdf<-data.frame(group=rep(1:4,each=20), prop=c(runif(20,0.25,1),runif(20,0.2,0.92), runif(20,0.15,0.84),runif(20,0.1,0.77))) km.glm<-glm(prop~group,kmdf,family=quasibinomial(link="logit")) summary(km.glm) pval<-0.00845 padjs<-NA npadj<-1 # assume you have five comparisons in this family for(method in p.adjust.methods) {

I have a researcher who is consistently get the warning message: In max(i) : no non-missing arguments to max; returning -Inf Best as I can tell the code is working properly and the output is as expected. I would like some help in understanding why he is getting this error message and what its implications are. I have his code. Sincerely, Kirsten Miles Support Specialist Research Computing Lab

Hi Kirsten, I can run your example code but I can't quite follow your division of sampling. Can you restate the the task? Below is what I think you are asking for, but I have the feeling I may be off the mark. Set A: 400 samples, draw 100 in range of 5 to 15 Set B: 800 samples, draw 100 in range of 5 to 15 Set C: 300 samples, draw 100 in range of 5 to 15 Ben > On Aug 5, 2017, at

Hello all, I have a data set where the response variable is the percent cover of a specific plant (represented in cover classes 0,1,2,3,4,5, or 6). This data set has a lot of zeros (plots where the plant was not present). I am trying to model cover class of the plant as a function of both total nitrogen and shrub cover. After quite a bit of research I have come across a conditional approach

Hi All, I'm trying to create two side-by-side contour plots with one legend by modifying the code found here: http://wiki.cbr.washington.edu/qerm/sites/qerm/images/b/bb/Example_4-panel_v1a.R I've been able to set up the other two functions called in the code, but I can't find reference to print.letterTrevor.R anywhere other than the page linked above. I've searched all the help

Not a reproducible example, so a bit of guessing here, but a) don't try to assign results to variables inside the ifelse. That is, remove all the single-equals signs and "test" variables. If you really need to conditionally assign variables then use "if"... but chances are good you don't need that. b) "closure" is effectively another word

Hi everyone, I'm having some trouble with my ifelse statements. I'm trying to put 12 conditions within 3 groups. Here is the code I have so far: dat$cond <- ifelse(test = dat$cond == "cond1" | dat$cond == "cond2" | dat$cond == "cond3" dat$cond == "cond4" yes = "Uniform" no = ifelse(test =

Hello! I have measurements (length and volume) of fish collected in two years. I want to know if the the relationship between length and volume is the same for both years. The number of fish measured is different for each year. I don't know whether lm or aov is more appropriate to use. Here are the two output options: Call: lm(formula = Volume ~ Length * Year) Residuals: Min 1Q

Hey everyone, I'm working on a contour plot depicting asymptomatic prevalence at varying durations of infectiousness and force of infection. I've been able to work everything out except for this one - my legend title keeps getting cut off. Here's what I have: filled.contour(x=seq(2,30,length.out=nrow(asym_matrix)), y=seq(1,2,length.out=ncol(asym_matrix)), asym_matrix, color =

Two questions: 1. Is the order of the dimensions shown what is shown if you look at str(ncin) - I mean shown at the end where it describes the variable and its dimensions? 2. Is you problem how to subset the netcdf file, how to write to the .csv file, or both? -Roy > On Aug 28, 2017, at 2:21 PM, Eeusha Nafi <eshad002 at gmail.com> wrote: > > I have a series of nertCDF files

hi, i'd like to calculate a temperatursum, adding the value of each element. let's say the data looks like this: x<-c(1,2,3,4,5) what i want to do, is ploting not the sum in the end but all the subresults, too, so my vector holds: x[i] [1] 1,3,6,10,15 here is what i tried, which seems to be right to me, bu doesn't work out: x<-c(1,2,3,4,5) i<-1 j<-1

Hi! Thanks for taking the time to read this. The code below creates a graph that takes 100 samples that are between 5% and 15% of the population (400). What I'd like to do, however, is add two other sections to the graph. It would look something like this: from 1-100 samples take 100 samples that are between 5% and 15% of the population (400). From 101-200 take 100 samples that are between