similar to: Specification: Bi variate minimization problem

Dear all, I'm working on the following problem: Assume two datasets: Y, Y that represent the same physical quantity Q. Dataset X contains values of Q after an event A while dataset Y contains values of Q after an event B. In R X, Y are vectors of the same length, containing effectivelly a number of observations of Q in each state. Q is a continous variable. Now, the two datasets should

Hi all, ok I have this issue: I want to change my graphs legends to custom text, often requiring the use of superscripts/subscripts I tried to use this instruction I found on stack overflow: labs(x = "R(Ohm)", y= "CDF", aesthetic= " Content (%)" ) but it wont' seem to work. Also tried bquote for super/ subscripts xlab(bquote(~x~/(~x~ + ~MO[2]~)*

Hi Petr and thanks for your reply, That's the problem I don't want to modify the labels of my legends but the title of the legend in itself inserting my custom text :) Take for example the 1st graph in this tutorial http://www.sthda.com/english/wiki/ggpubr-r-package-ggplot2-based-publication-ready-plots I want to change the "sex" in "sex subscript 2" or "

Hi There are plenty of examples https://stackoverflow.com/questions/6202667/how-to-use-subscripts-in-ggplot2-legends-r https://stackoverflow.com/questions/19507742/using-expressionpaste-to-insert-math-notation-into-a-ggplot-legend which you can modify. If you say "but it wont' seem to work" how can we know what does it mean? Plotmath expressions are rather tricky, especially if

I am running my code in a loop and it does not work but when I run it outside the loop I get the values I want. n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta <- 0 ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit))

I am new to R and I am trying to do a monte carlo simulation where I generate data and interject error then test various cut points; however, my output was garbage (at x equal zero, I did not get .50) I am basically testing the performance of classifiers. Here is the code: n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta

Hello, I'm trying to fminimize the following problem: You have a data frame with 2 columns. data.input= data.frame(state1 = (1:500), state2 = (201:700) ) with data that partially overlap in terms of values. I want to minimize the assessment error of each state by using this function: err.th.scalar <- function(threshold, data){ state1 <- data$state1 state2 <- data$state2

R-users E-mail: r-help@r-project.org Hi! R-users. http://finzi.psych.upenn.edu/R/library/mvpart/html/xpred.rpart.html says: data(car.test.frame) fit <- rpart(Mileage ~ Weight, car.test.frame) xmat <- xpred.rpart(fit) xerr <- (xmat - car.test.frame$Mileage)^2 apply(xerr, 2, sum) # cross-validated error estimate # approx same result as rel. error from printcp(fit) apply(xerr, 2,

Hi folks I wonder if anyone can help me. I want to run some simulations to see how big a sample size might be necessary to distinguish a curved bivariate relationship (e.g. something that might be best described by a quadratic model) from a relationship that is two straight lines with a sudden change in slope (e.g. something best described by a breakpoint regression). I am using

Dear all, I would like to generate bi-variate normal data given that the first column of the data is known. for example: I first generate a set of data using the command, x <- rmvnorm(10, c(0, 0), matrix(c(1, 0, 0, 1), 2)) then I would like to sum up the two columns of x: x.sum <- apply(x, 1, sum) now with x.sum I would like to generate another column of data, say y, that makes

Updating gnulib has caused -Wformat-signedness to be enabled. This has revealed many problems in C format strings. The fixes here fall into the following main categories: - Using %d with an unsigned parameter. - %x and %o expect an unsigned argument. - uid_t and gid_t are unsigned on Linux. The safe way to print these is to cast them to uintmax_t and then print then using the %ju

Updating gnulib has caused -Wformat-signedness to be enabled. This has revealed many problems in C format strings. The fixes here fall into the following main categories: - Using %d with an unsigned parameter. - %x and %o expect an unsigned argument. - uid_t and gid_t are unsigned on Linux. The safe way to print these is to cast them to uintmax_t and then print them using the %ju

library(ROCR) n <- 1000 fitglm <- function(iteration,intercept,sigma,tau,beta){ x <- rnorm(n,0,sigma) ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit)) *int*<-coef(model)[1] *slope*<-coef(model)[2] # when add error you are suppose to get slightly bias slope. However when I change

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Hello everyone Is there a function/command to simulate from "matrix variate normal distribution" in R. A follow up question would be is there a function/command to obtain the density, distribution and quantile function of "matrix variate normal distribution" in R. Wikipedia has a good description of "matrix variate normal distribution" which is also alternatively

Hi I have estimated a hazard function using a kernel based method. Now i have to generate a random variate that follows this hazard function. Does any one know method I should use to generate the variate. Thanks Samiul -- View this message in context: http://www.nabble.com/Generate-random-variate-from-a-non-parametric-hazard-function-tp26034880p26034880.html Sent from the R help mailing list

Does anyone know how to get a meaningful estimate of the covariance matrix for the first canonical variate coefficients for the right-hand (X) side of a canonical regression? cancor returns coefficients but not precisions. This would have to be subject to some constraint because of identifiability problems (arbitrary scaling). I think that SPSS attempts to do this but seems to condition on

Folks, I wonder if anything could be said about the distribution of a random variate x, where x = N(0,1)/N(0,1) Obviously x is pathological because it could be 0/0. If we exclude this point, so the set is {x/(0/0)}, does x have a well defined distribution? or does it exist a distribution that approximates x. (The case could be generalized of course to N(mu1, sigma1)/N(mu2, sigma2) and one

Hi, I plot no: of bytes against time and find the distribution curve using R. These bytes are sent from the client to the server. Is there a way to generate bytes randomly using R according to a distribution ? I would like to send these bytes to the server. Hope I am not misguided here. My goal is to simulate a certain distribution of bytes. Thanks, Mohan DISCLAIMER:\

Hello, I have built a multi-variate probit model using the package "bayesm", which requires that the X data is constructed using the function "CreateX". I've gone through the documentation and run my model, but wanted to be sure about my interpretation of the results for the coefficients - beta. Steps: 1) I have 5 choices for the dependent variable Y, so p=5 2) I have 8