similar to: Polynomial fitting

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 }

is there a way to use the actual index value for plotting zoo objects this is the way that the index is set up and a sample range of what I would like to plot 01/01/06 00:00:00 - 01/01/06 23:45:00 { library(zoo) # chron library(chron) fmt.chron <- function(x) { chron(sub(" .*", "", x), gsub(".* (.*)", "\\1:00", x)) }} x <- structure(c(15.57, 15.5,

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)

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

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

Hi everybody, I wonder if there is a built-in function similar to Matlab's "normfit" which computes 95% CI based on the normality assumption. So, I have a vector of values and I want to calculate 95% normal CI. Of course, I could write my own function, no problem, but I still wonder if built-in functionality exists. (I wish quantile() had this functionality included). Anyone knows?

Dear R-User, Appreciate any helps. It looks simple, but I don't have a clue. Given that I have a dataframe of tree population with three variables: sp=species , d0=initial_size grow=growth increment from initial size per year How can I calculate the future growth increment of each tree for the next 3 years. The following Rscript was written, #---------- a0 <-

I have a series of numbers I'm wanting to plot. They come from a nanodrop machine, which graphs with a specific x and y indices. X goes from 220nm to 350nm, which I can set. But the y axis should go from -5 to 65, but I'm finding it impossible to hardcode that. I've looked. I've typed ?plot at the R prompt. Google has not been my friend. _R Graphics_, if it holds the key, has not

The aggregate function for some reason will now work for me. The error I'm getting is: "Error in sort.list(y) : 'x' must be atomic for 'sort.list' Have you called 'sort' on a list?" agPriceList=aggregate(PriceList$Size, list(PriceList$bandNum),sum) *Price list dataframe:* dput(PriceList) structure(list(Price = c(0, 8.18, 8.27, 10.42, 10.5, 10.6, 11.13,

On Sat, 2011-10-29 at 15:16 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > > Ralf, et al., > > > > > > Attached is the latest version of my autovectorization patch. llvmdev > > > has been CC'd (as had been suggested to me); this e-mail contains > >

Hello, It's an alternative to use SAS by function in R? I want to plot d histograms by plot.from example bellow: Thank you! plot d 1 1 16.3 2 1 25.0 3 1 57.8 4 1 17.0 5 2 10.8 13 2 96.4 17 3 76.0 18 3 32.0 19 3 11.0 20 3 11.0 24 3 106.0 25 3 12.5 21 4 19.3 22 4 12.0 26 4 15.0 27 5 99.3 32 7 11.0 36

On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > Ralf, et al., > > > > Attached is the latest version of my autovectorization patch. llvmdev > > has been CC'd (as had been suggested to me); this e-mail contains > > additional benchmark results. > > > > First, these are preliminary

Hi everybody! I'm looking some way to do in R a polynomial fit, say like polyfit function of Octave/MATLAB. For who don't know, c = polyfit(x,y,m) finds the coefficients of a polynomial p(x) of degree m that fits the data, p(x[i]) to y[i], in a least squares sense. The result c is a vector of length m+1 containing the polynomial coefficients in descending powers: p(x) = c[1]*x^n +

Hi all, starting from a vector "v[1:n]" I would like to compute the coefficients of the polynomial (1+x^v[1])*(1+x^v[2])*...*(1+x^v[n]). The following code works but is extremely slow for a large "n" due to, I believe, the polynomial being factorized. I wanted to try the package "polynom" command "unclass" but I could not figure out how to input a

Hello, i'm fairly familiar with R and use it every now and then for math related tasks. I have a simple non polynomial function that i would like to approximate with a polynomial. I already looked into poly, but was unable to understand what to do with it. So my problem is this. I can generate virtually any number of datapoints and would like to find the coeffs a1, a2, ... up to a given