similar to: Plot help

Dear List, I am looking to plot error bars on a line using dispersion. I have values for the upper value and for the lower values, however i am unsure how to plot different values for the upper CI and the lower CI? I have been using dispersion(1:35,sim,simCI,col="red") Where there are 35 points, and simCI relates to a vector containing the lower confidence intervals, however i want

Hello, I have a problem... I tried using a function and I get the error: "ERROR: missing value where TRUE/FALSE needed" Here is my code: a<-mctp(Ecoli~Fecha, data=Datos, type = "Tukey", alternative = "two.sided", asy.method = "mult.t", plot.simci = TRUE)summary(a) Ecoli, Fecha and Datos are well defined. I don't know what is the problem... THANKS

hi could you help me to solve this issue Question: Using command rweibull(100,8,15), simulate n = 100 realizations from Weibull(8; 15) distribution. Using the simulated sample, compute the sample mean, variance and standard deviation of these observations. I am trying like this sim<-rweibull(100,8,15) # simulated sample SM<-mean(sim) # simulated sample mean var(sim) # variance

hello, i need to calculate ci's for each of 4 groups within a dataset, to be able to infere about differences in the variable "similarity". the problem is that data within groups is dependent, as assigned by the blocking-factor "site". my guess was to use a block bootstrap but samples within in these blocks / sites are not of same length. i was not able to find a method to

Hello, I have a question regarding shading regions under curves to display 95% confidence intervals. I generated bootstrap results for the slope and intercept of a simple linear regression model using the following code (borrowed from JJ Faraway 2005): > attach(allposs.nine.d) > x<-model.matrix(~log(d.dist,10))[,-1] > bcoef<-matrix(0,1000,2) > for(i in 1:1000){ +

Dear all, I am trying to evaluate the optimisation behaviour of a function. Originally I have optimised a model with real data and got a set of parameters. Now I am creating simulated data sets based on these estimates. With these simulations I am estimating the parameters again to see how variable the estimation is. To this end I have written a loop which should generate a new simulated data

Dear R-users, I would like to show you a simple example that gives an overview of one of my current issue. Although my working setting implies a different parametric model (which cannot be framed in the glm), I guess that what I'll get from the following example it would help for the next steps. Anyway here it is. Firstly I simulated from a series of exposures, a series of deaths (given a

wanfairos wrote: > > Dear members; > > I have one problem to display my simulated data. Basically, I've generated > a set of rnorm (std.norm.mat)and rnbinom (gen.data) data to test on the > performance of FE Poisson model estimates > > ... Long self-contained example shorted > > my.data<-rep(9999,sim.size) > > for(i in 1: sim.size){ > ... >

I have been trying to simulate from a time series with trend but I don't see how to include the trend in the arima.sim() call. The following code illustrates the problem: # Begin demonstration program x <- c(0.168766559, 0.186874000, 0.156710548, 0.151809531, 0.144638812, 0.142106888, 0.140961714, 0.134054659, 0.138722419, 0.134037018, 0.122829846, 0.120188714,

Dear useRs, I want to simulate a time series (stationary; the distribution of values is skewed to the right; quite a few ARMA absolute standardized residuals above 2 - about 8% of them). Is this the right way to do it? #-------------------------------- load("rdtb") #the time series > summary(rdtb) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.11800 -0.65010 -0.09091

Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <- as.data.frame(mvrnorm(N, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are

Dear all, I have a data frame with one factor and four numeric variables and wish to obtain the var-cor matrix separately by factor. I tried by() and sapply() but getting nowhere. I understand this can be done by subsetting the dataframe, but there should have some sleek ways of doing it. Here is a simulated dataframe; s <- rep(c("A","B","C"), c(25,22,18)) d

I'm attempting to write a general function to implement Faraway's bootstrapping algorithm for mixed models with lmer, but have run into a curious problem. I'm comparing two models model.1<-lmer(Response ~ Treatment + (1|Trial), data=exp.data, method="ML") model.2<-lmer(Response ~ 1 + (1|Trial), data=exp.data, method="ML") When I attempt to update

Hi, Does anyone know a method for adding a linear/polynominal trend to a simulated arima model using the arima.sim function? Any help will be greatly appreciated. Cheers, Sam.

Dear R-users I am trying to recompile a CRAN package on Windows 32. Rtools for 2.14 (that is the version I am running) and miktex were sucessfully installed on my machine. Problems: a) hydroGOF is a CRAN package, but R CMD check does not work on it. C:\Users\eduardo\Documents\R_tests2>R CMD check hydroGOF * using log directory 'C:/Users/eduardo/Documents/R_tests2/hydroGOF.Rcheck'

Full_Name: Gang Liang Version: 1.7.1 OS: Debian/Woody Submission from: (NULL) (192.6.19.190) > print(arima.sim(list(ar=.3,order=c(1,1,1)), 30)) [1] 0.00000000 0.10734243 0.02907301 -1.23441659 -0.98819317 -2.82731975 [7] -2.69052512 -4.22884756 -5.02820635 -5.41514613 -6.20486350 -7.01040649 [13] -6.78121289 -5.41111810 -4.96338053 -5.42395408 -6.22741444 -5.75228153 [19] -6.07346580

Dear all, I have a matrix called combine86 which looks as follows: > combine86 Sim Mean Obs Mean Sim Sum Obs Sum AMAZON 1172.0424 1394.44604 553204 659573 NILE 262.4440 164.23921 67973 41881 CONGO 682.8007 722.63971 205523 214624 MISSISSIPPI 363.0758 142.59883 124535 49054 AMUR 143.5857 89.30434 36040 22594 PARANA

Hi. I'm embarking on my first attempt at creating my own panel function for lattice graphics, and despite all of my online research and pouring through the documentation, I cannot figure out how to solve my particular problem. Hopefully, a generous fellow R user can help. I have some data that is split into two groups: some "actual" data, and some simulated data,

Hi, all. In setting up my package for post-processing regression models, I am trying to use standard notation as much as possible: thus, I use coef() to access estimated coefficients. I wrote a function called se.coef() to grab standard errors, and se.fixef() and se.ranef() to grab se's from coefficients estimated from lmer(). I also need a function to access sigma-hat (the residual sd

Dear List, Trying to explain my situation as simply as possible for me: I am running a series of iteration on coxph model on simulated data (newly generated data on each iteration to run under coxph; in my example below- sim.fr is the generated data). However, sometimes i get warning messages like "Ran out of iterations and did not converge" or "Error in var(x, na.rm = na.rm) :