similar to: nls with weights

hi all i have a function that uses two inputs, say xdata and ydata. An example is the following, simple1<-function(xdata,ydata) { ofit<-lm(ydata~xdata) list(ofit) } say i use arbitray number for xdata and ydata such that D = x1 x2 y 1 1 10 2 6 6 3 10 7 x<-D[,1:2] and y<-D[,3] if one uses these inputs and rund the program we get the following: >simple(xdata=x,ydata=y)

I have a set of data in the form x1, y1, z1 x1, y2, z2 ... x1, yN, zN x2, y1, z(N+1) x2, y2, z(N+2) ... x2, yN, z(2N) ...and so on... xM, yN, val(M*N) I have been trying to figure out how to get R to use this data in a persp plot. So far the only thing that I can figure out to do is to break the data file into three different files. The first file contains the x-coordinate data: x1 x2

Hello all, I have been using R for about 3 weeks and I am frustrated by a problem. I have read R in a nutshell, scoured the internet for help but I either am not understanding examples or am missing something completely basic. Here is the problem: I want to plot data that contains dates on the x axis. Then I want to fit a line to the data. I have been unable to do it. This is an example of

Airon, I used R2.4.0 on a Windows XP (SP2) (not Chinese :-)) and it still works: > data = read.csv("u:/airon.csv") > xdata = data$Adj..Close > modwt.la8 = modwt(xdata, "la8", n.level=6) > summary(modwt.la8) Length Class Mode d1 1467 -none- numeric d2 1467 -none- numeric d3 1467 -none- numeric d4 1467 -none- numeric d5 1467 -none- numeric d6 1467

I'm working with 3 different data sets and applying this non-linear regression formula to each of them. nls(Y ~ (upper)/(1+10^(X-LOGEC50)), data=std_no_outliers, start=list(upper=max(std_no_outliers$Y),LOGEC50=-8.5)) Previously, all of the regressions were calculated in Prism, but I'd like to be able to automate the calculation process in a script, which is why I'm trying to move to

Dear R-users and experts I want to create to analyse my data which looks like follows:( I have show only 8 variables but original variables much more number >1000) *sub* *ydata* *X1* *X2* *X3* *X4* *X5* *X6* *X7* *X8* 1 12 1 1 1 2 1 1 1 1 2 13 2 2 1 2 2 1 1 1 3 11 1 1 1 2 1 2 1 2 4 12 1 1 2 1 1 2 2 2 5 14 1 2 2 1 1 2 2 2 6 12 2

Dear all: I have the following codes: Xdata<-c(2,3,8,9,10) Ydata<-1:5 gap.plot(Xdata, Ydata,gap=c(5,6),gap.axis="x",type="o") However, the type='o' seems only work on the first part of gap plot, the second half of the plot always just points, you can not add lines on that part, any help will be highly appreciated. I would like to have these two parts of

Good Day, I fit a multivariate linear regression model with 3 dependent variables and several predictors using the lm function. I would like to use stepwise variable selection to produce a set of candidate models. However, when I pass the fitted lm object to step() I get the following error: Error from R: Error in drop1.mlm(fit, scope$drop, scale = scale, trace = trace, k = k, : no

Dear All, I have been trying to simulate data from a fitted glm using the simulate() function (version details at the bottom). This works for lm() fits and even for lmer() fits (in lme4). However, for glm() fits its output does not make sense to me -- am I missing something or is this a bug? Consider the following count data, modelled as gaussian, poisson and binomial responses: counts

Hi: I have encountered problems with imodwt and universal.thresh.modwt and cannot find any reference in R Search. Hope someone can give me some ideas: Starting with modwt.la8 <- modwt(xdata, "la8", n.level=6) <-- this seems to work fine (1) ydata <- imodwt(modwt.la8) will always give ydata as numeric(0) (no values) instead of being a time series data with

Hi! When running the following code I get the message: Error in eval(expr, envir, enclos) : object "A" not found fm <- function(p,ydata, env = parent.frame()) { #fit model y ~ (b0+b1x1+...+bpxp)*exp(g2plus*z2plus) where bi and g2plus are parameters #create design matrix for linear part fo00 <-

Greg, Two thoughts: 1. It might be possible that 'vars' is a reserved word of sorts and if you change the name of your vector RF might be happier 2. A way that works for me is to call importance as follows: sel.imp <- importance(sel.rf, class=NULL, scale=TRUE, type=NULL) and then use the 'names' of the imp data frame to be absolutely clear to RF you are talking about the

Airon, I don't think you have to find an English computer 'cause the following must work in your Chinese one :-) Let me explain. First of all, change your lines to xdata <- ckhdat$Adj..Close[1:1447] #names(ckhdwt.la8) <- c("w1", "w2", "w3", "w4", "w5","w6", "v6") note the # sign, i.e., DO NOT change the names

Hi all! I am working with GLMM using the binomial family I use the following codes I dropped no significant terms, refitting the model and comparing the changes with likelihood: G.1<-lmer(data$Ymat~stu+spi+stu*sp1+(1|ber),data=data,family="binomial") G.1b<-lmer(data$Ymat~stu+spi+(1|ber),data=data,family="binomial") anova (G.1,G.2) But, when I want to evaluate the

Hi, all I am trying to make a plot with a axis break and I want the whole plot to be line, not points. However, when I execute the following command half of the graph is points and the other lines. gap.plot(Xdata, Ydata,gap=c(5,6),gap.axis="x",type="l") I think it might be a bug in plotrix. I would greatly appreciate your input. If there is another way to do it, I

Good afternoon My name is Joanne Lenehan, I am a post grad at UNE using R version 2.9.0 I came across the Tweedie package in old R help posts and was interested in giving it a go for some data. The data is below and also attached as BaregroundLitterLogs Site Treatment Graze Dam Plot Time Bare Litter Logs 1 C remote yes 1 A 0 2 0 1 C remote yes 2 A 0 15 0 1 HE

Hi I am doing this sort of thing: POLY: > > obj = best.tune(svm, similarity ~., data = training, kernel = "polynomial") > summary(obj) Call: best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") Parameters: SVM-Type: eps-regression SVM-Kernel: polynomial cost: 1 degree: 3 gamma: 0.04545455 coef.0: 0

Hi list, I was wondering how I should go about fitting a sigmoid curve to a dataset. More specifically how I estimate parameters a and b in the following equation: 1 / 1+exp(-(x-a)*b) with b the steepness of the sigmoid curve and a the shift of the center of the sigmoid curve relative to the center of your dataframe. The fit is in function of x, the location within the input vector and y, an

This concerns the contributed package "concord". Sorry to bother you with it, but my attempt to contact the author/maintainer failed (see below). Perhaps you can forward it, or let me know where to send it. Regards, Rob Kushler ------------------------------------------------------ This is the Postfix program at host tak.itd.uts.edu.au. I'm sorry to have to inform you that

Dear list members. I have problems with the usage of cv.glm from the boot package. Here are some parts of the script I wanted to use: data <- read.table("selected_2D.csv", header=TRUE, sep=",") ? glm.fitted <- glm("ydata$ y ~ 1 + density + vsurf_ID6 + vsurf_S ", data=data) error <- cv.glm(data=data, glm.fitted, K=6) ydata$y is a separate data set, where