search for: nmodel

...(idata,idata$DVID==j) for (k in 1:nPer) { #loop on Period # k=1 ijkdata<-subset(ijdata,ijdata$OCC==k) for (l in 1:nGRP) { #loop on Group # l=1 subdata<-subset(ijkdata,ijkdata$GRP==l) nModel<-nlevels(factor(subdata$MODEL)) #number of models to be plotted in this loop mylegend<-c("Raw data",levels(factor(subdata$MODEL))) subID<-nlevels(factor(subdata$ID)) #number of ID in the new dataset myplot<-xyplot(Y...

...couple logistic regressions, what making a distribution of some event. Currently, i store it like this: o1 <- lrm(...) o2 <- lrm(...) o3 <- lrm(...) ... Then, i have made a function to peak required regression object from this variables by it number: get_object <- function(obj_name, nModel) { eval (parse(text=paste("o <- ", obj_name, nModel, sep=""))) o } Is there a better way to do it? I have try to store it in the matrix using data.frame(), but object become destroyed after that and predict() function do not recognize it. Regards, Dmitrij Kudriavcev...

...the same problem. If I remove the parameter "a," then it can find a solution. Does anyone know what I can do to fit this model? vardata<- rnorm(73,mean=0,sd=5) x<-0:72 a<-1 b<-50 k<-0.05 l<-5 startt<-c(a,b,k,l) yf<-a+b*(1-exp(-k*(x-l))) y<-yf+vardata nmodel<-nls(y~a+b*(1-exp(-k*(x-l))), start=list(a=1,b=50,k=0.05,l=5)) -- View this message in context: http://r.789695.n4.nabble.com/nls-not-solving-tp3328647p3328647.html Sent from the R help mailing list archive at Nabble.com.

...I did not try to implement the changes proposed by > Hadley, hoping that a solution can be found using the original dataset. > > ######### My code > > # Creating dataset > > nPts<-10 # number of time points > nInd<-6 # number of individuals > nModel<-3 # number of models > > TimePts<-rep(1:nPts,nInd*nModel) # > creates the "Time" column > Coef<-rep(rnorm(6,0.1,0.01),each=nPts,nModel) # Creates a > vector of coefficients for generating the observations >...

...v[1L] <- NA nas <- !is.na(dev) P <- dev P[nas] <- safe_pchisq(dev[nas], dfs[nas], lower.tail = FALSE) aod[, c("LRT", "Pr(Chi)")] <- list(dev, P) } aod <- aod[o, ] head <- c("Single term deletions", "\nModel:", deparse(as.vector(formula(object)))) if (scale > 0) head <- c(head, paste("\nscale: ", format(scale), "\n")) class(aod) <- c("anova", "data.frame") attr(aod, "heading") <- head aod } ## addterm.AICc=func...

hi, I would like to find the x value (independent variable) for a certain dependent value using the fitted model with nls. with (predict) I can find y that corresponds to a list of x. I need the other way around. can it be done? thanks, afadda

...= seq(0.1, 1.6,.5), mu = seq(0.01, .41,.1), l=1, b =seq(0,.6,.3)) lower.bound<-list(A=.01,mu=0,l=0,b=0) try( invisible(capture.output(mod1<-nls2(Yvals~modeltest(A,mu,l,b,thour), #start = list(b =5, k = 2, l=0), start = st2, lower = lower.bound, algorithm = "brute-force" ))) ) try(nmodel<-nls(Yvals~modeltest(A,mu,l,b,thour), start=coef(mod1), #start=list(A=1.8,mu=.2,l=.5,b=.15), lower=lower.bound, algorithm="port") ) My problem seems to be with initial parameter estimates. I am running through a couple hundred treatments, so I used nls2 to pick the best starting value...

...e)\t",k,"\t",vlr,"\n") vlr <- NULL k <- 0 for(j in 1:length(delta)) if(delta[j]>10){ k <- k+1 vlr[k] <- delta[j] } cat("> 10 (very strong evidence)\t",k,"\t",vlr,"\n") cat("\nMODELS\n") vlr <- NULL k <- 0 for(j in 1:length(delta)) if(delta[j] <= 2){ k <- k+1 vlr[k] <- j } cat("<= 2 (no credible evidence)\t",k,"\t",vlr,"\n") vlr <- NULL k <- 0 for(j in 1:length(...

...rate=rates[ii]) } uy <- unlist(y) ylim <- if (any(uy == Inf)) { c(0, 2*max(ho$intensities)) } else { c(0, max(c(ho$intensities, uy))) } plot(ho, col='lightgray', freq=FALSE, ylim=ylim, main=paste(KK, 'component(s) in model\nmodel prior(s) =', paste(round(pis, 2), collapse=', '))) cols <- rainbow(KK) for (ii in 1:KK) { lines(x, y[[ii]], col=cols[ii]) } } ## ## Model dat as a mixture of Gammas then plot. ## modelGammas <- function(dat, which='BIC') { set.seed(939458) fmo <...

...? ? 1 > str(hoyt.add1) Classes ?anova? and 'data.frame': 4 obs. of 4 variables: $ Df : num NA 1 2 6 $ AIC : num 2166 2130 1430 842 $ LRT : num NA 38.8 740.3 1336.5 $ Pr(Chi): num NA 4.66e-10 1.74e-161 1.36e-285 - attr(*, "heading")= chr "Single term additions" "\nModel:" "~Status + (Sex * Rank * Occupation)" So, all I get is a data frame containing the results for added terms. I'm also not sure which add1 function to look at since I don't find an add1.loglm > methods(add1) [1] add1.default* add1.glm* add1.gnm* add1.lm* add1.mlm* add...

...e)\t",k,"\t",vlr,"\n") vlr <- NULL k <- 0 for(j in 1:length(delta)) if(delta[j]>10){ k <- k+1 vlr[k] <- delta[j] } cat("> 10 (very strong evidence)\t",k,"\t",vlr,"\n") cat("\nMODELS\n") vlr <- NULL k <- 0 for(j in 1:length(delta)) if(delta[j] <= 2){ k <- k+1 vlr[k] <- j } cat("<= 2 (no credible evidence)\t",k,"\t",vlr,"\n") vlr <- NULL k <- 0 for(j in 1:length(...

...####################################################################### # Print method for harmodel: print.harModel = function(x, digits = max(3, getOption("digits") - 3), ...){ formula = getHarmodelformula(x); modeldescription = formula[[1]]; betas = formula[[2]]; cat("\nModel:\n", paste(modeldescription, sep = "\n", collapse = "\n"), "\n\n", sep = "") coefs = coef(x); names(coefs) = c("beta0",betas) if (length(coef(x))){ cat("Coefficients:\n") print.default(format(coefs, di...

...######################################################################### # Print method for harmodel: print.harModel = function(x, digits = max(3, getOption("digits") - 3), ...){ formula = getHarmodelformula(x); modeldescription = formula[[1]]; betas = formula[[2]]; cat("\nModel:\n", paste(modeldescription, sep = "\n", collapse = "\n"), "\n\n", sep = "") coefs = coef(x); names(coefs) = c("beta0",betas) if (length(coef(x))){ cat("Coefficients:\n") print.default(format(coefs, digits...