search for: logscale

...R, so any pointers would be very helpful. I've read many definitions of what dropterm() does, but none seem to stick in my mind or click with me. I've coded everything fine for an interaction that runs as follows: two sets of data (one for North aspect, one for Southern Aspect) and have a logscale on the x axis, with survival on the y. After calculating my anova results i have all significant results (ie aspect = sig, logscale of sunlight = sig, and aspect:llight = sig). When i have all significant results in my ANOVA table, do i need dropterm(), or is that just to remove insignificant term...

...e2    -0.34    0.1 subgroupB    site1    -0.28    0.06 subgroupB    site2    -0.29    0.07 subgroupC    site1    0.34    0.03 subgroupC    site2    0.36    0.04 #meta-analysis A<-meta[c(1,2),] B<-meta[c(3,4),] C<-meta[c(5,6),] Ameta<-meta.summaries(A$beta,A$se,method="fixed",logscale=TRUE,names=A$study) Bmeta<-meta.summaries(B$beta,A$se,method="fixed",logscale=TRUE,names=B$study) Cmeta<-meta.summaries(C$beta,A$se,method="fixed",logscale=TRUE,names=C$study) list(summary(Ameta),summary(Bmeta),summary(Cmeta)) I get my output, but I'm not able to p...

Dear all, I have a problem with gamma regression (glm - family = Gamma) and zeros in R. The problem is the following when I try to estimate a dataset without zeros (endogenous variable) there is no problem. However, if I try to do the same with zeros I always get an error message. In STATA and MATLAB I can do a gamma regression with zeros. What could be the problem? If someone could help me I

...1:10 b <-11:20 j <- rnorm(100) grid<-expand.grid(a = a, b = b) levelplot(j~a*b, grid) (from a previous mail) and it does not work. Can anybody tell me what I'm doing wrong? Furthermore as you ight notice the data in a is in log-scale so I want the y-axis of the plot in logscale. All the best Tom ------------------------------------------------------------------------ ------------------------ Tom Lenaerts (tlenaert at ulb.ac.be) http://www.tomlenaerts.tk/ Postdoc Researcher @ IRIDIA-Universite Libre de Bruxelles-Belgium Guest Professor @ DINF-Vrije U...

...htplot() is found in the 'gap' package which I installed in R 2.11.1 on Windows. I thought I'd test out the function first with the examples they give in the documentation: # foo example test <- matrix(c(1,1,4,1,1,6,1,10,3,2,1,5,2,2,6,2,4,8),byrow=TRUE,6) mhtplot(test) mhtplot(test,logscale=F) # fake example with Affy500k data affy <-c(40220, 41400, 33801, 32334, 32056, 31470, 25835, 27457, 22864, 28501, 26273, 24954, 19188, 15721, 14356, 15309, 11281, 14881, 6399, 12400, 7125, 6207) CM <- cumsum(affy) n.markers <- sum(affy) n.chr <- length(affy) test <- data...

Dear R Users, I am trying to obtain a best-fit analytic distribution for a dataset with 11535459 entries. The data range in value from 1 to 300000000. I use: fitdistr(data, "gamma") to obtain mle's for the parameters. I get the following error: Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : non-finite finite-difference value [1] And the following warnings:

...ctor(sample(c("A","B"), 5000, replace = T)) counts = sample(c(1, 10, 100, 1000, 10000), 5000, replace = T)) -stop-cut-and-paste-here What i am wanting to do is to do split violin plots for splits 1 and 2 within groups A and B for each count (which would be in the logscale, but that is not important for this example). We have four groups for each setting but there is a nested aspect to it. Here is what I have tried: -start-cut-and-paste-again--- GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin, draw_group = function(self, data, ..., draw_q...

I am reading some documentation about Cross Spectrum Analysis as a technique to compare spectra. My understanding is that it estimates the correlation strength between quasi-periodic structures embedded in two signals. I believe it may be useful for my signals analysis. I was referred to the R functions that implement this type of analysis. I tried all the examples which generated a series of

Hey Gang, Here is the situation. First of all I am a newbie, in the arena of R, so please bare with me. I am trying to make a plot with a series of plot lines that uses logs on the x axis, and when I plot it I am having a few odd things go on that I do not know how to correct. 1. the tick marks are not where I want them to be. I want them at .1, 1, 10, and 100, but I'm get them at 0.1,

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

...R, so any pointers would be very helpful. I've read many definitions of what dropterm() does, but none seem to stick in my mind or click with me. I've coded everything fine for an interaction that runs as follows: two sets of data (one for North aspect, one for Southern Aspect) and have a logscale on the x axis, with survival on the y. After calculating my anova results i have all significant results (ie aspect = sig, logscale of sunlight = sig, and aspect:llight = sig). When i have all significant results in my ANOVA table, do i need dropterm(), or is that just to remove insignificant term...