similar to: Barchart bar lengths not proportionate

Hi all, I am using barchart() to plot values above the bars. When using groups argument we get bars grouped arround a given x level. By placing values above this bars we need to know the respective x coordinates. How can I get it? require(lattice) da <- expand.grid(x=1:5, z=1:3, w=1:2) da$y <- rpois(da$x, lambda=23) barchart(y~x|w, groups=z, data=da, horizontal=FALSE,

Hi R users, I sent a message yesterday about NA in model estimates ( http://r.789695.n4.nabble.com/How-set-lm-to-don-t-return-NA-in-summary-td3722587.html). If I use aov() instead of lm() I get no NA in model estimates and I use gmodels::estimable() without problems. Ok! Now I'm performing a lot of contrasts and I need correcting for multiplicity. So, I can use multcomp::glht() for this.

Hello, I'm doing some graphics for a paper and a need customize such with filled region above the density curve. My attempts I get something very near what I need, but I don't solve the problem of use semitransparent filled. Below a minimal reproducible code. Someone has any idea? require(lattice) # toy data... dt <- expand.grid(A=1:2, B=1:3, y=1:50) dt$y <- rnorm(nrow(dt), dt$B,

Colin, If your propose is to create a ternary plot with points and vectors, I think easier do this with graphics based plots instead of trellis based plots. Although, with little work you can do with trellis too. I gave you a reproducible code to put an arrow in a ternary plot. I use the function locator() to extract coordinates. The code is the following

Hi all, I want to provide the result from D() to curve(), because I want to plot the k-th derivative of some functions. Actually, I copy from console the result given by D() and paste inside curve(). With a lot of functions and high degree differentiation this process is tedious. Can I provide directly?? # what I actually have done (very simple function) D(expression(x^3), "x") # copy

Hi, I am currently running the following negative binomial GLM: glm89.nb <- glm.nb(AvGUD ~ Year*Trt*Micro + (0 + Micro/Trt/Year)) where Year has 3 levels, Trt has 2 levels, and Micro has 3 levels. >From what I have read the above model has a 3 way interaction (Year*Trt*Micro), and Micro is nest within Trt and Trt is nested with Year (0 + Micro/Trt/Year). I was hoping someone could

I am trying to calculate Poisson regression based adjusted rate ratios in R, but R's default in glm does not code the intercept as the global rate. In SAS I use "cell means" coding so that the intercept is the global rate, but I do not know how to do this in R. If anyone knows a way to make glm use "cell means", or an how to find adjusted rate ratios I would be grateful.

Dear all, I have been working in a plot based on figure 5.6 of the Lattice book (http://lmdvr.r-forge.r-project.org/figures/figures.html). I have already modified it to include the size of the circles as another variable, but I would like to modify the legend to show it (like they do it in http://www.jstatsoft.org/v15/i05/paper). I have divided my variable in intervals: DATA$s_Shape_2 <-

Hi I ran the following sweave file in R2.14.1 and upgraded to R2.15 yesterday with the same setup I got the following error message when I rand the following Sweave file > setwd("D:/Cic/Sweave/Parasite/Comb/12") > Sweave("D:/Cic/Sweave/Parasite/Comb/12/ParasiteComb12.Rnw") Error: c("'ParasiteComb12DS.Rnw'", "'ParasiteComb12.Rnw'")

Hi All, I need a small help with following code: I'm trying to convert "dashed lines" to regular ones; and changing default "blue" border color to say "black"... but I'm doing it wrong and its not working. Can anyone help please. Thanks, Code: require(lattice) ex <- data.frame(x=1:10, y=rep(c("A","B"), 5)) bwplot(y~x, data=ex,

Hello list, I have plots with long strings in main=, ylab= or xlab=. So, in I my script I use break long lines to avoid lines hiden on my monitor and in sweave document pages. I use graphics like this plot(1, main="aaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbb") but I would like a plot result like this plot(1, main="aaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbb") I

Hi, I was wondering: I've got a dataset where I've got student 'project's nested within 'school's, and 'division' (elementary, junior, or senior) at the student project level. (Division is at the student level and not nested within schools because some students are registered as juniors & others as seniors within the same school.) So schools are random,

Dear useRs, How could I obtain the confidence intervals for the means of my treatments, when my data was fitted to a GLM? I need the CI's for the Poisson and Negative Binomial distributions. Here's what I have: mydata1 <- data.frame('treatments'=gl(4,20), 'value'=rpois(80, 1)) model1 <- glm(value ~ treatments, data=mydata1, family=poisson) means1 <-

Hi, How can I find and download a function in R to do the LU decompostion for finding the upper and lower triangular matrix.  Thank you so much. Joe ___________________________________________________ 您的生活即時通 － 溝通、娛樂、生活、工作一次搞定！ [[alternative HTML version deleted]]

Hi, I'm trying to fit nonlinear mixed effects model using nlme function but getting an error message. Here is what I have: fitted_model = nlme(scores~spline(b1,b2,b3,kt,time), fixed = list(b1~1, b2~1, b3~1, kt~1), random = b1+b2+b3~1, groups= ~id, data = sdat, start = c(b1=3.5,b2=2,b3=.60,kt=3.5),verbose=T) Error: Error in

Dear all, I am interested to calculate confidence interval for fitted values in general for non-linear regressions. Lets say we have y=f(x1,x2,..xN) where f() is a non-linear regression. I would like to calculate a confidence interval for new prediction f(a1,..,aN). I am aware of techniques for calculating confidence intervals for coeffiecients in specific non-linear regressions and with them

I am using levelplot() function from pkg:lattice and I want to add some points to the plot generated by levelplot() similarly as in basic R graphic sequence: plot(...); points(...), but it does not work. I was reading documentation for lattice, but so far without much success. I would very much appreciate some tips. Best regards, Ryszard

Hi, I wanted to use the predict.lm() function to compare the empirical data with the predicted values. The problem is that I have NAs in my data. I wanted to cbind my data.frame with the empirical values with the vector I get from predict.lm. But they don't have the same length because predict.lm just skip NA-predictions. Is there a way to get a vector with predicted values of the same

Dear all, I'm attempting to use a piecewise regression to model the trajectory of reproductive traits with age in a longitudinal data set using a mixed model framework. The aim is to find three slopes and two points- the slope from low performance in early age to a point of high performance in middle age, the slope (may be 0) of the plateau from the start of high performance to the

pairwise.t.test is returning NAs when one of the samples only has one entry, while TukeyHSD returns results (maybe not trustworthy or believable, but results). I stumbled on this because I did not realize one of my samples only had one entry while most of the others had several hundred, so I realize this is not a desirable situation. I'm really just curious about the difference between how