similar to: Computing normal conf.intervals

I wonder how one in R can fit a 3rd degree polynomial to some data? Say the data is: y <- c(15.51, 12.44, 31.5, 21.5, 17.89, 27.09, 15.02, 13.43, 18.18, 11.32) x <- seq(3.75, 6, 0.25) And resulting degrees of polynomial are: 5.8007 -91.6339 472.1726 -774.2584 THanks in advance! -- Jonas Malmros Stockholm University Stockholm, Sweden

Hello everyone. I use double for loops to fill in matrices, but there are surely better (and computationally faster) ways to perform that task. Could someone show me, given the following example of a double for loop, how this could be done? It is much easier to learn by examples. Val <- matrix(0, nrow=n+1, ncol=n+1) for( i in 0:n){ for(j in 0:i){ Val[j+1, i+1] <- u^j*d^(i-j)

Dear R users£¬ I've just started learning R and I'm having a problem with it. I was told as following when I tried to run R: Error in loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'matlab' methods specified for export, but none defined: sum, size, padarray, flipud, fliplr Error: package/namespace load failed for

Hello, I have written a simple Metropolis-Hastings MCMC algorithm for a binomial parameter: MHastings = function(n,p0,d){ theta = c() theta[1] = p0 t =1 while(t<=n){ phi = log(theta[t]/(1-theta[t])) phisim = phi + rnorm(1,0,d) thetasim = exp(phisim)/(1+exp(phisim)) r = (thetasim)^4*(1-thetasim)^8/(theta[t]^4*(1-theta[t])^8) if(runif(1,0,1)<r){ theta[t+1] = thetasim }

I have a data frame named "database" with panel data, a little piece of which looks like this: Symbol Name Trial Factor1 Factor2 External 1 548140 A 1 -3.87 -0.32 0.01 2 547400 B 1 12.11 -0.68 0.40 3 547173 C 1

Bill, Alberto, Gabor, Thank you for answering my question. Now I learned about outer() function. That was a straightforward example. But what if I had a matrix, where the last column was filled with values first (again, a for loop), and the rest was filled by using a double loop? OVal <- matrix(0, n+1, n+1) for(i in 0:n){ OVal[i+1, n+1] <- max(Val[i+1, n+1]-K, 0) } for(i in seq(n,1,

Dear R users, I have a huge database and I need to adjust it somewhat. Here is a very little cut out from database: CODE NAME DATE DATA1 4813 ADVANCED TELECOM 1987 0.013 3845 ADVANCED THERAPEUTIC SYS LTD 1987 10.1 3845 ADVANCED THERAPEUTIC SYS LTD 1989 2.463 3845 ADVANCED THERAPEUTIC SYS LTD 1988 1.563 2836 ADVANCED TISSUE

Dear list members, I have written a function, called say Analysis. I supply an Excel file name as an argument, it does analysis on this file and returns a pdf file with specific plots, and a text file with several statistical models' output (I extract certain values from the output and create my own custom dataframe with output). As of now I have to open the script file and load the function

Hello, I have a vector of names, say : names <- c("Factor 1", "Factor 2", Factor 3") I am creating a dataframe and I want the column names to be mixed like this: "Factor 1" " Sign Factor 1" "Factor 2" "Sign Factor 2" "Factor 3" "Sign Factor 3" How can I automate the creation of such a mixed vector? I tried

Hello, I am using textplot function in gplots package to put some model output inside a PDF file, but it does not seem to work properly with PDF. I am doing follwing: pdf(file="C:/...", paper="a4", width=8, height=12) .model <- lm(.model.formula, data=database) textplot(capture.output(summary(.model)), valign="top", halign="left") I am getting these

Hi all, I post it on both r-help and r-finance since I don't know where is most appropriate for this topic. Sorry if it bothers you. I did garch fitting on S&P500 monthly returns with garchFit from fSeries. I got same coefficients from all cond.dist except normal. I thought that is probabaly usual for the data. But when I play with it, I got another question. I plot skew normal with

Hello, My response variable seems to be distributed according to Student t with df=4. I have 320 observations and about 20 variables. I am wondering whether there is a way to fit glm with Student t for error distribution. Student t is not one of the family choices in glm function. How should I proceed to fit glm with Student t? I know that Student t is the Inverse Gamma with shape parameter

i want to plot the histogram and the curve in the same graph.if i have a set of data ,i plot the histogram and also want to see what distribution it was.So i want to plot the curve to know what distribution it like. -- View this message in context: http://www.nabble.com/how-to-plot-the-histogram-and-the-curve-in--the-same-graph-tp20082506p20082506.html Sent from the R help mailing list archive at

Hi all, I was trying garchFIt() of fSeries to fit volatility of monthly log returns of S&P500. I tried residuals of normal, student t, skew normal, skew t. But all innovations except normal got exaxtly same coefficients, even if I changed their parameters of skew and shape. Is this correct for the data or something wrong? I am attaching the code, thank you. Muster #GARCH analysis of

I am extracting climate data for coastal areas from 5km grid data for specific xy coordinates that relates to individual study sites (SQ). Code so far: setwd("/Users/roblewis/Documents/PhD/Climate Data") fnamestemp=read.table("Path_Directory_Maxt") fnames=paste(fnamestemp[,1],fnamestemp[,2]) nfiles = length(fnames) xy=as.matrix(read.table("xy_.txt"))

Hi all, Are there base functions that do the equivalent of this? fliptb <- function(x) x[nrow(x):1, ] fliplr <- function(x) x[, nrow(x):1] Obviously not hard to implement (although it needs some more checks), just wondering if it had already been implemented. Hadley -- Assistant Professor / Dobelman Family Junior Chair Department of Statistics / Rice University http://had.co.nz/

I'm slowly moving my statistical analysis from Matlab to R, and find myself missing two features: a) How do I mimic Matlab's 'hold on'? (I want to show several plots together, when I type two plots one after the other the second overwrites the first) b) How do I mimic Matlab's 'axis'? (after drawing my plots I want to zoom on specific parts- for example, x=0:5,

Hi all, I'm trying to learn R after years of Matlab's experience. Here is an issue I couldn't solve today. Consider the following piece of code (written by memory): for(i in 1:n){ submat <- data[1:i,] C <- colSums(submat) } The problem is that at the first iteration, data[1:1,] reduces to a vector and colSums returns an error. This sounds really strange to me

Hi, I'm having a problem converting a Matlab program into R. The R code works almost all the time, but about 4% of the time R's optim function gets stuck on a local minimum whereas matlab's fminsearch function does not (or at least fminsearch finds a better minimum than optim). My understanding is that both functions default to Nelder-Mead optimization, but what's different about

I submit the following matrix to both MATLAB and R x= 0.133 0.254 -0.214 0.116 0.254 0.623 -0.674 0.139 -0.214 -0.674 0.910 0.011 0.116 0.139 0.011 0.180 MATLAB's inv(x) provides the following 137.21 -50.68 -4.70 -46.42 -120.71 27.28 -8.94 62.19 -58.15 6.93 -7.89 36.94 8.35 11.17 10.42 -14.82 R's solve(x) provides: 261.94 116.22 150.92 -267.78 116.22 344.30 286.68