similar to: Binning the data based on a value

Hi there, I would like to solve a simple equation in R a^2 - a = 8.313 There is no real solution to this problem but I would like to get an approximate numerical solution. Can someone suggest how I can set this up? Thanks in advance, Diviya [[alternative HTML version deleted]]

Hello there, Can anyone point me to the code for logLik of an nls object? I found the code for logLik of an lm but could not find exactly what function is used for calculating the logLik of nls function? I am using the nls to fit the following model to data - Model 1: y ~ Ae^(-mx) + Be^(-nx) +c and want to understand what is the likelihood function used by nls. Presumably it is using -

Hi there, I have a complex math equation which does not have a closed form solution. It is - y <- (p*exp(-a*d)*(1-exp((d-p)*(a-x[1]))))/((p-d)*(1-exp(-p*(a-x[1])))) For this equation, I have all the values except for x[1]. So I need to solve this problem numerically. Can anyone suggest an optimization package that I can use to estimate the value for x[1]? Thanks in advance, Diviya

Hello there, I have a question regarding bar plots. I am trying to plot the data from the following matrix as a barplot - # input data mdat <- matrix(c(0.1,0.9,0.9,0.1,0.5,0.5,0.45,1-0.45,0.6,0.4,0.8,0.2), nrow = 6, ncol=2, byrow=TRUE, + dimnames = list(c("Mon", "Mon", "Tues", "Tues", "Thurs", "Friday"), +

Hello there, I am new to using regression in R. I wanted to solve a simple regression problem where I have 2 equations and 2 unknowns. So lets say - y1 = alpha1*A + beta1*B y2 = alpha2*A + beta2*B y1 <- runif(100000, 0,1) y2 <- runif(100000,0,1) alpha1 <- 0.6 alpha2 <- 0.75 beta1 <- 1-alpha1 beta2 <- 1-apha2 I now want this equation to estimate the values of A and B. Both A

Hi there, I was wondering if there is any R package that one can use for plotting that has more legend symbols - the standard pch has 18 symbols but I need ~30 for my application- and just using different colors is not an option. Thank you in advance, Diviya [[alternative HTML version deleted]]

Hello there, I recently wrote some code to perform pairwise correlations between all samples in a large dataset. So we are talking about performing pairwise correlations between 400K vectors. Since R has a very rich library of functions, it was very easy to code this in R. However, R was probably not the best choice as it is super slow for this large job. So my plan is to recode it in C. I was

Hi, I'm looking for a super-duper fast mean/sum binning implementation available in R, and before implementing z = binnedMeans(x y) in native code myself, does any one know of an existing function/package for this? I'm sure it already exists. So, given data (x,y) and B bins bx[1] < bx[2] < ... < bx[B] < bx[B+1], I'd like to calculate the binned means (or sums)

Newbie here. Many apologies in advance for using the incorrect lingo. I'm new to statistics and VERY new to R. I'm attempting to "group" or "bin" data together in order to analyze them as a combined group rather than as discrete set. I'll provide a simple example of the data for illustrative purposes. Patient ID | Charges | Age | Race 1 |

Hello, I am having problem with binning the data. I have a 50X3 matrix and I binned the data for all the 3 columns. Using table command I got the total no. of elements in a particular bin. Could you please tell me how to see that what all elements are there in a particular bin and then create a different matrix for each bin? Thanks. Regards, Sumit [[alternative HTML version deleted]]

Hi there, I would like to solve the following equation in R to estimate 'a'. I have the amp, d, x and y. amp*y^2 = 2*a*(1-a)*(-a*d+(1-a)*x)^2 test data: amp = 0.2370 y= 0.0233 d= 0.002 x= 0.091 Can anyone suggest how I can set this up? Thanks, Diviya [[alternative HTML version deleted]]

I am trying to produce frequencies in defined intervals however I can't seem to figure out how to get R to bin my data the way I want it to. I have several thousand lengths of fish that I want to be binned as follows: Ex. Length Bin 209 200 219 210 431 430 727 720 That is, bins with any length equal to or greater than the lower

Hello there, I am using NLS for fitting a complex model to some data to estimate a couple of the missing parameters. The model is - y ~ (C+((log(1-r))*exp(-A*d)*(-1+r+exp(d*(A-B)))/(r*(-A*d+d*B+log(1-r))))) where A, B and C are unknown. In order to test the model, I generate data by setting values for all parameters and add some noise (C). A <- 20 B <- 500 r <- 0.6881 d <-

Hello there, I am trying to fit an exponential fit using Least squares to some data. #data x <- c(1 ,10, 20, 30, 40, 50, 60, 70, 80, 90, 100) y <- c(0.033823, 0.014779, 0.004698, 0.001584, -0.002017, -0.003436, -0.000006, -0.004626, -0.004626, -0.004626, -0.004626) sub <- data.frame(x,y) #If model is y = a*exp(-x) + b then fit <- nls(y ~ a*exp(-x) + b, data = sub, start

Hello there, I am trying to use R function NLS to analyze my data and one of the examples in the documentation is - ## the nls() internal cheap guess for starting values can be sufficient: x <- -(1:100)/10 y <- 100 + 10 * exp(x / 2) + rnorm(x)/10 nlmod <- nls(y ~ Const + A * exp(B * x), trace=TRUE) plot(x,y, main = "nls(*), data, true function and fit, n=100") curve(100 +

Hi there, I am trying to compute the autocorrelation in a dataset using R's acf function. ACF automatically plots the results. This works well except in some cases xlim doesnt work data <- rnorm(2000,0,1) acf(data,xlim=c(1,10)) # works - the plot starts at 1 acf(data,lag=100,xlim=c(1,100)) # this does not work and the plot still starts at 0 Is there another way to specify the xlim or

Hi there I have an Rscript and I am looking for a way to install a package non-interactively. In Rscript {Utils}, I saw an example which does something like this, however this does not seem to work for my particular example. I am trying to install the following package in an Rscript (without switching to interactive mode). res <- try(install.packages("DEoptim")) if(inherits(res,

I have a simulated matrix of dates that I generated from a probability function. Each column represents a single iteration. I would like to bin each run _separately_ by decades and dump them into a new matrix where each column is the length of all decades a single run with the number dates binned by decade. I have successfully done this for a single vector of dates, but not for a matrix:

How to use ggplot to do the binned quantile plots(one type of scatter plot)? Hi all, I have done scatter plot: plot(x, y). Now I wanted to do binned quantile plots... can ggplot2 help me? For example, we bin x data into 10 bins. For each bin, we draw the 10 deciles of the corresponding y data in that bin as points/dots. And then accross all bins, we would like to connect the corresponding

Hi, Standard correlations (Pearson's, Spearman's, Kendall's Tau) do not accurately reflect how closely the model (GAM) fits the data. I was told that the accuracy of the correlation can be improved using a root mean square deviation (RMSD) calculation on binned data. For example, let 'o' be the real, observed data and 'm' be the model data. I believe I can calculate