similar to: r: functions

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

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

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

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

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 there, I don't have much experience with fitting at all and I'd like to get some advice how to use the "weights"-argument with nls correctly. I have created some data with a sigmoidal curve shape. Each y-Value was generated by the mean of three values. A standard deviation was calculated too. Now, I'd like to weight the data points respective to its standard

I've been playing around with some ways to tighted up our AVX specification and have hit upon a nice way to reduce a bunch of code. Unfortunately, right now TableGen can't handle it. Here's a simple example of what I want to do: class Data<string n, int v> { string Name = n; int Value = v; } // Define some objects usable as arguments. def X : Data<"X", 1>;

I am using the lm function in R to fit several linear models to a fair-sized dataset (~160 collections of ~1000 data points each). My data have intrinsic, systematic uncertainty much greater than the measurement errors on any individual point. My thought is to use the residuals of my linear fits to quantify this intrinsic uncertainty, but I am puzzled over the correct interpretation of R's

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 <-

Hi R-experts, I want to use the jackknife function from the bootstrap package onto a linear model. I can't figure out how to do that. The manual says the following: # To jackknife functions of more complex data structures, # write theta so that its argument x # is the set of observation numbers # and simply pass as data to jackknife the vector 1,2,..n. # For example, to jackknife #

Hola, buenas tardes estoy intentando hacer un bootstrap de un modelo, pero me da el siguiente error: "Error in FUN(newX[, i], ...) : unused argument(s) (list(age = c(33, 47, 49, 56, 60, 64, 64, 66, 68, 69, 71, 71, 72, 73, 74, 75, 75, 76, 78, 81, 83, 83, 36, 43, 46, 47, 49, 49, 51, 51, 52, 52, 53, 54, 54, 54, 55, 56, 56, 57, 57, 58, 58, 58, 58, 59, 59, 60, 61, 62, 63, 64, 65, 65, 66, 66,

Hi all, I try to write a simple function in a script. The script is as follows yo<-function(Xdata) { n<-length(Xdata[,1]) Lgm<-nls(formula=LgmFormula, data=Xdata, start=list(a=1500,b=0.1),weights=Xdata$Qe) return(Lgm) } After the execution of the script, when I call the function yo on data called NC60.DATA I get an error. #yo(NC60.DATA) Erreur dans eval(expr, envir, enclos)

Hi, I am parsing a C++ file using cindex.py and want to get the template parameters to a specific node. However, the tree seems to be different depending on if the template parameter is a struct/class or a simple type such as int or float. In the first case the template type is appended as a child to the VAR_DECL node (the TYPE_REF node seen in the example below), but this is not the case with

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

I get this error while computing partial correlation. *Error in solve.default(Szz) : system is computationally singular: reciprocal condition number = 4.90109e-18* Why is it?Can anyone give me some idea ,how do i get rid it it? This is the function i use for calculating partial correlation. pcor.mat <- function(x,y,z,method="p",na.rm=T){ x <- c(x) y <- c(y)

Hi, I have been using R for over a year now. I am a very happy user. Thank you for making this happen. This is my first question to this list. I trying to add some functions to quantmod that would enable me to draw arbitrary lines and text and make sure they are redrawn. I have created following function: require(quantmod) # Add horizontal line to graph produced by quantmod::chart_Series()

Hello R-Users,   I have one binary dependent variable and a set of independent variables (glm(formula,…,family=”binomial”) ) and I am using the function stepAIC (“MASS”) for choosing an optimal model. However I am not sure if stepAIC considers significance properties like Likelihood ratio test and Wald test (see example below).     > y <- rbinom(30,1,0.4) > x1 <- rnorm(30) > x2