similar to: Confidence Intervals on Standard Curve

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

Dear All: I would any appreciate any help with this plot I am struggling with. I have 4 estimates (95% CIs) I want to plot. I want the CI lines to be horizontal on each plotted point. I was trying to tweak some old codes (was for a vertical CI lines) into horizontal but not much dice. Many thanks in advance for your help. YA My working codes: x=c(1,1,1,1.1,1.1,1.1,2,2,2,2.1,2.1,2.1)

When concatenating mbox files like described here https://xaizek.github.io/2013-03-30/merge-mbox-mailboxes/. You will end up with an 'unsorted' mbox file. Is this going to be a problem esspecially when they are large >2GB's and new emails will be written to it? The email client nicely sorts the message from folder A "foldera 5 last" as last, but of course the mbox is

Hello, I'm trying to develop a box plot of time series data to look at the range in the data values over the entire period of record. My data initially starts out as a list of hourly data, and then I've been using this code to make this data into the final ts array. # Read in the station list stn.list <- read.csv("/home/kbennett/fews/stnlist3", as.is=T, header=F) # Read in

Hello, As I am relatively new to the R environment this question may be either a) Really simple to answer b) Or I am overlooking something relatively simple. I am trying to add a VarIdent structure to my gam model which is fitting smoothing functions to the time variables year and month for a particular species. When I try to add the varIdent weights to variable Month I get this error returned.

I would like to use lattice graphics to plot multiple functions (or groups or subpopulations) on the same plot region, using different line types "lty" or colors "col" to distinguish the functions (or groups). In traditional graphics, this seems straightforward: First plot all the data using 'type="n"', and subsequently execute a series of "points"

Dear list, I would like to plot points with two types of labels, one at the data point (the name of the point) and another offset a bit with another factor which is either of the two greek characters alpha or beta. I have tried to get the routine to plot a greek character with expression() or with substitute() and have not yet had any success. The following only plots the word in english in

Hi, I got the same problem like http://tolstoy.newcastle.edu.au/R/devel/04/11/1204.html R crashes when I use the em function from the mclust package on univariate data and on a special case on bivariate data (when the matrix is not provided as written in the manual). It seems as if the problem is the format of the data to be analyzed. Operating System: Windows XP (SP2) R version: R-2.0.1 The

Hi, We got a question about interpretating R-suqared. The actual outputs for a test dataset is X=(x1,x2, ..., xn). model 1 predicted the outputs as Y1=(y11,y12,..., y1n) model n predicted the outputs as Y2=(y21,y22,..., y2n) ... model m predicted the outputs as Ym=(ym1,ym2,..., ymn) Now we have two ways to calculate R squared to evaluate the average performance of committee model. (a)

Hi, I have a problem regarding matrix handeling. I am working with a serie of matrixes containing several columns. Now I would like to delete those rows of the matrixes,that in one of the columns contain values less than 50 or greater than 1000. How would this be possible, I have tried to create a simple function for this, but I just don't seem to get it right.Thank you so much for your help,

I'm quite new to R. I have a time series of annual state population estimates from census.gov, and I'd like to get a time series of monthly estimates, by a nonlinear interpolation. How can I do this in R? Thanks! [[alternative HTML version deleted]]

Hi, I am using R to fit statistical models to data were the observations are means of the original data. R is used to calculate the mean before fitting the model. My problem is: When R calculates the means using tapply, the class of the means differs from the class of the original data, which gives me trouble when I want to use the original data to calculate model predictions. Here is a simple

Hi, I'm trying to obtain confidence intervals for auto and cross correlation estimates. I've adapted code made available by Stock and Watson that uses the Bartlett Kernel and the delta method. In R it runs really, really slow because of the loops it uses and I have 9 series that I'd like to examine (81 total combinations). It was easy enough to replace one of the while loops with a

Greetings UseRs, Pls advise if there is a way to write a func that can be supplied to aggregate to compute weighted MeanAbsolute Dev (MAD). I am having trouble passing the correct weights from each group level and cannot see the code behind aggregate. But maybe 'aggregate' is not the best way to do that. > m1 <-

Hello R-Users, I am new to R and trying my best however I need help with this simple task. I have a dataset, YM1207. X.Symbol Date Time Exchange TickType ReferenceNumber Price Size 12491 3:YMZ7.EC 12/03/2007 08:32:50 EC B 85985770 13379 7 12492 3:YMZ7.EC 12/03/2007 08:32:50 EC A 85985771 13380 4 12493

Dear Gabor, Thanks for your reply. however: > tail(DJd) ^DJI.Close 2010-04-01 10927.07 2010-04-05 10973.55 2010-04-06 10969.99 2010-04-07 10897.52 2010-04-08 10927.07 *2010-04-09 10997.35* > tail(ag) 2009-11-30 10344.84 2009-12-31 10428.05 2010-01-31 10067.33 2010-02-28 10325.26 2010-03-31 10856.63 *2010-04-30 10997.35 * It seems the script "makes up"

[[this bounced first, because it has 'help' in the Subject line ... -- Martin Maechler ]] I am a complete novice R programmer. (Though I know C quite well) I am trying to write some R code to do the following simulation. There is a 2-frame "movie" of noise and signal dots. the noise dots have random positions in each frame. The signal dots are placed randomly in frame 1,

Hi all. I have defined a plot thus: par(mar=c(5,5,4,5),las=1, xpd=NA) plot(Day, Ym1Imp, ylim=c(0,100), type="b", bty="l", main="Ym1 Expression", cex=1.3, xaxt="n", yaxt="n") #plot implant data axis(side=1, at=c(0,1,3,5,7,10,14,21), labels=c(0,1,3,5,7,10,14,21)) # label x axis mtext("Day", side =1, at=10, line=3, cex=1.2) # title x

Dear R-list I have been trying to make survreg fit a normal regression model with left truncated data, but unfortunately I am not able to figure out how to do it. The following survreg-call seems to work just fine when the observations are right censored: library(survival) n<-100000 #censored observations x<-rnorm(n) y<-rnorm(n,mean=x) d<-data.frame(x,y) d$ym<-pmin(y,0.5)

Hi, I would like to know about graphics for response surface in R. What are arguments for a best graphics? thanks, Bruno y <- c(66,39,43,49,58,17,-5,-40,65,7,43,-22,-31,-35,-26) x1 <- c(-1,1,-1,1,-1,1,-1,1,rep(0,7)) x2 <- c(-1,-1,1,1,0,0,0,0,-1,1,-1,1,0,0,0) wireframe( y ~ x1*x2 , scales = list(arrows = FALSE), drape = TRUE, colorkey = TRUE, screen = list(z = 30, x = -60),