[R] strange PREDICTIONS from a PIECEWISE LINEAR (mixed) MODEL

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,c

[R] PREDICTIONS from a PIECEWISE LINEAR (mixed) MODEL: THEY AIN'T LINEAR BETWEEN BREAK POINTS!!

int is not straight at all, instead behaves like if it was a hig order polynomial or something similar I attach the codes below, hoping someone can point me the mistake I sincerely express many thanks in advance .. Federico Bonofiglio frame3<-data.frame(id,chol,cd4,rt,sex,age, nadir

[R] intervals {nlme} lower CI greater than upper CI !!!????

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

[R] glht{multcomp} : use with lme {nlme}

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

[R] random interaction effect in lmer

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 syn

[R] TRADUCING lmer() syntax into lme()

tion) grouped by by a general nesting structure that sets factorA1 and factorA2 as same level effects (hence non nested) and factorB as nested in both. I also must express my momentaneous sheer ignorange on the pdMat objects, thing that prabably is not helping me in the process Kindly Regards

Re: [R] piecewise regression

immediate > by > > > "regression" > > > summary(p.r) # 15 matches in 13 packages > > > p.4 # to view in a web browser > > > > > > > > > Beyond this, the "structchange" package supports looking for > changes &

[R] piecewise regression

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]] __ R-help

[R] graphics: 3D regression plane

t;-lm(y~rd*k) newax<- expand.grid( days = giorni<-seq(min(rd),max(rd), length=100), expl= esplic<- seq(min(k), max(k), length=100) ) fit <- predict(mod,data.frame(rd=giorni,k=esplic)) graph <- persp(x=giorni, y=esplic,fit, expand=0.5, ticktype="detailed", the

[R] logistic regression with response 0,1

ies: - are the estimates correct? - degrees of freedom exponentiate dramatically (one per cell) , so may I risk to never obtain a significant result? I also take the chance to ask wheater u know any implemented method to plot logistic curves directly out of a glm() model I would like to thank u a

[R] anomalies with the loess() function

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---

[R] lowess() won't handle NAs

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 valu

[R] dpois().......bizarre warning messages

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 sain

[R] the sample() function

ters, attach me the algorithm (or source code? is it right?) behind the *sample()* function, so i can inspect in detail the mechanism of this so gossiped "radomization"? thank you, sincerely federico bonofiglio, student of statistics at milano bicocca university, italia [[alterna

[R] cycling k times a realization of a random walk.....problems..

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 WORD