similar to: P values in non linear regression and singular gradients using nls

Is there a way to create a 'bubble plot' in R? For example, if we define the following data frame containing the level of y observed for 5 patients at three time points: time<-c(rep('time 1',5),rep('time 2',5),rep('time 3',5))

I've just uploaded gregmisc_0.8.2.tar.gz to CRAN. It should show up in the package repository in a day or two. This version of the gregmisc package provides an enhanced 'balloonplot' function with 'rowsrt', 'colsrt' arguments to control rotation of the labels, and 'rowmar', 'colmar' to control the amount of space reserved for the labels. Here's

Hi Everyone, I'd omitted the non-optional 'parameters' argument to selfStart. Making this change to SSbatch gives the same (successful) result from the two calls to nls. SSbatch<-selfStart( model=function(Batch, Coeffs) { Coeffs[Batch] } ,initial=function(mCall, data, LHS) { # Estimate coefficients as mean of each batch xy <- sortedXyData(mCall[["Batch"]],

Hi everyone, I thought that for a selfStart function, these two should be exactly equivalent > nls(Aform, DF) > nls(Aform, DF, start=getInitial(Aform, DF)) but in this example that is not the case in R (although it is in S-plus V6.2) ------------------------------ SSbatch<-selfStart( model=function(Batch, Coeffs) { Coeffs[Batch] } ,initial=function(mCall, data, LHS) { # Estimate

I'm not understanding something. I'm trying to add xlab & ylab to a balloon plot of a table object. From docs I thought following should work: require(gplots) # From balloonplot example: # Create an example using table xnames <- sample( letters[1:3], 50, replace=2) ynames <- sample( 1:5, 50, replace=2) tab <- table(xnames, ynames) balloonplot(tab)

I am looking for R code to be able to fit a linear-linear piecewise model with person-specific changepoint. I have searched the web, but have not been able to locate any code. Below is my attempt at some code: chgpt = function(a1,a2,a3,gam,wave){ yht=numeric(10) y1=(wave <= gam)*(a1+(a2*wave)) y2=(wave > gam)*((a1+(a2-a3)*gam)+a3*wave) yhat=y1+y2 return(yht) } nl.dat <- nlme(y ~

Dear R-team There seems to be a bug in glm.fit - I got the following error message: > > > + Error in names<-.default(*tmp*, value = ynames) : names attribute must be the same length as the vector In addition: Warning messages: 1: fitted probabilities of 0 or 1 occurred in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, 2: fitted probabilities of 0 or 1 occurred

Hi, I just updated to R-2.1.1 and updated packages acordingly However, after the update, routines that use polar.plot did not function as correctly. In plotrix 1.3.3 the polar.plot function does scale label.pos to radians prior to calling radial.plot Hence, the command polar.plot(c(5,10,5,0),c(-10,0,10,20),rp.type='P',

Dear R users: I am not entirely convinced that clogit gives me the correct result when I use pspline() and maybe you could help correct me here. When I add a constant to my covariate I expect only the intercept to change, but not the coefficients. This is true (in clogit) when I assume a linear in the logit model, but the same does not happen when I use pspline(). If I did something similar

At 06:09 AM 5/1/00 +0100, Prof Brian D Ripley wrote: >On Sun, 30 Apr 2000, Stephen R. Laniel wrote: > >> I was just now surprised to note that functions to go generalized additive >> models don't appear to exist under R 1.000. In particular, the gam() and >> loess() functions aren't there. Are they hidden somewhere and I just >> haven't noticed? >

Hi. I am new here, and I am writing this Winbugs code with BRugs. n=length(bi.bmi) Lagegp=13 Lgen=2 Lrace=5 Lstra=15 Lpsu=2 #model gen x race bi.bmi.model=function(){ # likelihood for (i in 1:n){ bi.bmi[i]~ dbern(p[i]) logit(p[i])<- a0 + a1[agegp[i]]+a2[gen[i]]+a3[race[i]] + a12[agegp[i], gen[i]] + gam[stra[i]]+ u[psu[i],stra[i]] } # constraints for a1, a2, a3, a12 a1[1]<-0.0

Amigos de la erre. He creado mi primer bucle con for para entrenar unos modelos con GAM. La respuesta es quasipoisson porque estoy trabajando con densidades de peces. Sin embargo, tengo un problema, no se muy bien como añadir el offset a la formula siguiente cuando creo el bucle. GAM.A1 <-gam ((DYO)~s(DMA,k=4)+ s(WOD,k=4)+s(CIN,k=4)+s(DRA,k=4)+s(DBR,k=4)

Hi list, I'm working with gamm models of this sort, using Simon Wood's mgcv library: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group in order to test whether tensor product smooths vary across factor levels. I was wondering if comparing those two

Hi all, I try to interpolate a data set in the form: time Erg 0.000000 48.650000 1.500000 56.080000 3.000000 38.330000 4.500000 49.650000 6.000000 61.390000 7.500000 51.250000 9.000000 50.450000 10.500000 55.110000 12.000000 61.120000 18.000000 61.260000 24.000000 62.670000 36.000000 63.670000 48.000000 74.880000 I want to get smoothed splines by using the class gam The first way I tried , was

Amigos de la erre. He creado mi primer bucle con for para entrenar unos modelos con GAM. La respuesta es quasipoisson porque estoy trabajando con densidades de peces. Sin embargo, tengo un problema, no se muy bien como añadir el offset a la formula siguiente cuando creo el bucle. GAM.A1 <-gam ((DYO)~s(DMA,k=4)+ s(WOD,k=4)+s(CIN,k=4)+s(DRA,k=4)+s(DBR,k=4)

Sorry if this is an obvious question... I'm estimating a simple binomial generalized additive model using the gam function in the package mgcv. The model makes sense given my data, and the predicted values also make sense given what I know about the data. However, I'm having trouble interpreting the y-axis of the plot of the gam object. The y-axis is labeled "s(x,2.52)"

Hi all, until now I have generally used mgcv for gams, however, I decided to experiment with the package gam, and have ran into the previously outlined problem (see below), for which I have not yet found a solution in the archives. If anyone has any suggestions, please let me know. The only difference for my case is that I am running R 2.9.0 under windows Thank you! I'm running R

Hello, I realize this is not a new problem, but I'm hoping somebody can point me in the right direction. The function silly() below calls gam() (from package gam) with different values of the span parameter for lo(), which is used inside the formula of gam. The values for span are stored in a vector that resides in the function silly(), and that lo(), for some reason, needs to see. A hack

Dear useRs, I have a question with respect to fitting a non-linearity using gam (mgcv package, version 1.7-16). In a study I'm currently conducting, I'd like to find out if there is a breakpoint after which the effect of Age of Acquisition (AOA) of the second language changes. I.e. if the slope of AOA before the breakpoint (at a certain AOA) is different from the slope past the

Hello, I am using the function step.gam() from the 'gam' package (header info from library(help=gam) included below) and have come across some behavior that I cannot understand. In short, I have written a function that 1) creates a dataframe, 2) calls gam() to create a gam object, then 3) calls step.gam() to run stepwise selection on the output from gam(). When I do this, gam()