search for: bonoricus

hello, ladyes and gentlemans. check this: means<-c(4,6,8) stand.error<-c(0.1,0.3,0.5) now i've strongly tryed to scatterplot the means(y-axis),by showing their sd with the arrow(..,code=3,angle=90) function. The problem is that my x-axis has categorical values (say, factor(x)), and the arrows() can't recognize them as right coordinates. ????? thank you all in advance B.F. insubria

Hello everybody!!!! Quick question, if you'd like to throw a little tip: does anyone knows a function that runs piecewise regression models with coefficients estimation and inferences ? Thank you [[alternative HTML version deleted]]

Dear Masters, I have a question to submit consider the following script m<-4.95 obs<-rpois(36,m) # i generate 36 realization from a poisson(m) hist(obs,freq=F) curve(dpois(x,m),add=T,col="red") #i wish to overlay on the histogram the theorical poisson density function errors are returned saing the x vector doesn't contain integers.... really bizarre i can't give

Hi folks... check this out.. > GLU<-lme(gluc~rt*cd4+sex+age+rf+nadir+pharmac+factor(hcv)+factor(hbs)+ + haartd+hivdur+factor(arv), + random= ~rt|id, na.action=na.omit) > intervals(GLU)$fixed lower est. upper (Intercept) 67.3467070345 7.362307e+01 7.989944e+01 rt *0.0148050160* 6.249304e-02 1.101811e-01 cd4

Hi Dears, When I introduce an interaciton in a piecewise model I obtain some quite unusual results. If that would't take u such a problem I'd really appreciate an advise from you. I've reproduced an example below... Many thanks x<-rnorm(1000) y<-exp(-x)+rnorm(1000) plot(x,y) abline(v=-1,col=2,lty=2) mod<-lm(y~x+x*(x>-1)) summary(mod) yy<-predict(mod)

Hello Masters, I run the loess() function to obtain local weighted regressions, given lowess() can't handle NAs, but I don't improve significantly my situation......, actually loess() performance leave me much puzzled.... I attach my easy experiment below #------SCRIPT---------------------------------------------- #I explore the functionalities of lowess() & loess() #because I have

Dear Masters, first I'd like to wish u all a great 2011 and happy holydays by now, second (here it come the boring stuff) I have a question to which I hope u would answer: I run a logistic regression by glm(), on the following data type (y1=1,x1=x1); (y2=0,x2=x2);......(yn=0,xn=xn), where the response (y) is abinary outcome on 0,1 amd x is any explanatory variable (continuous or not)

hi ladyes and gentlemans of the R comunity. i have the following graphical issue: # say y is a matrix or a dataframe with dim(51,nrow=17),hence 3 columns; l<-51 # now the 3 colums represents 3 different aspects of my continuous variable (3 factors),plotted against a categorical one; n<-levels(factor(x)) # i want to plot those 3 factors adjacently using the barplot(), and possibly show

hello R-Wizards!!!!! again i'm invoking your presence!! since all the fuss about the paradoxes on a computer algorithm generating "caos" or un-determinancy, i recently grew quite curious about the mechanism underlying the procedure of NUMBER RADOMIZATION. could anyone of you, masters, attach me the algorithm (or source code? is it right?) behind the *sample()* function, so i can

Hi dears, I do > CHOL<-lme(chol~rt*cd4+sex+age+rf+nadir+pharmac+factor(hcv)+factor(hbs)+ haartd+hivdur+factor(arv), random= ~rt|id, na.action=na.omit) ...runs sweet,..then ....try a multicomparisons approach for the categorical rf > summary(glht(CHOL, linfct=mcp(rf="Tukey"))) * Error in model.frame.default(object, data, xlev = xlev) : l'oggetto non è una matrice

hi ladies and gentlemen of the R community. i'm federico an italian student of statistics and i've got an issue to submit to you: i've got a huge file.txt replenished of blank values (over a milion of them). by importing it into R the read.table() function higlights missing objects per row and declares an error. there's any way i can detail into the read.table() function to

Dear Masters, I'm driving crazy with the lowess() function.... my intent is smoothing confidence intervals for predicted y values in a linear model lm() setting since in the predict() function there exists an option for predicting NA values, I instead encounter problems when I fit a missing values x variable to the predicted terms.....,impossible task!!! I've been banging on my head

Hi dears while modeling an interaction random effect in lmer i receive the instantaneous error message > ldlM4<-lmer(ldl~rt*cd4+age+rf+pharmac+factor(hcv)+ + hivdur+(rt:cd4|id),na.action=na.omit,REML=F) *Warning message: In mer_finalize(ans) : false convergence (8) * I think the matter lies in syntax, 'cause i sistematically receive the same message even when changing response... PS:

I Dears, if that wouldn't take u too much effort I'd like to ask for a swift opinion: I have a alinear (mixed effect) model that I wish to run as a piecewise one. When I predict the values Iget quite some odds and disturbing results: The predicted stright line after the break point is not straight at all, instead behaves like if it was a hig order polynomial or something similar!!!! I

hello R-masters. i have an R-issue here that i don't know if you'd wish to help me? about it: briefly i'd like to generate many (say hundred) realizations of a random walk, execute a few operations on each of them (mean time of return), and graph each realization on the same plot. IN OTHER WORDS I'D LIKE TO IMPOSE A LOOPING CYCLE TO THE COMMAND NOT THE ARGUMENT OF THE COMMAND.

Hello Masters, wishing you all a great 2011 I was also going to ask if anyone knows a quick and efficient way to plot a regression plane (z~x*y). I have tried the regr2.plot{HH} function but it is only an educational tool and has poor graphical properties. I also tried to run the following script on a fictitious longitudinal problem, with poor results set.seed(1234)