similar to: Quantile of a function

Dear all, I am trying to optimize a logistic function using optim, inside the following functions: #Estimating a and b from thetas and outcomes by ML IRT.estimate.abFromThetaX <- function(t, X, inits, lw=c(-Inf,-Inf), up=rep(Inf,2)){ optRes <- optim(inits, method="L-BFGS-B", fn=IRT.llZetaLambdaCorrNan, gr=IRT.gradZL, lower=lw, upper=up, t=t, X=X)

Hello expeRts, I need generate symbolize the autocovariances matrix of a Gaussian ARMA(1,1), for derivate it and evaluate. I try this codes, but whitout sucess vacv<-NULL vacv[1]<-1-2*phi*theta-theta^2 vacv[2]<-(1-phi*theta)*(phi-theta) vacv[3:n]<-acv[2]*(phi^(1:(n-2))) facv<-list() for(i in 1:2)

Could I have some suggestions as to how (various ways) I can display my confidence interval results? rm(list = ls()) set.seed(1) func <- function(d,t,beta,lambda,alpha,p.gamma,delta,B){ d <- c(5,1,5,14,3,19,1,1,4,22) t <- c(94.32,15.72,62.88,125.76,5.24,31.44,1.048,1.048,2.096,10.48) post <- matrix(0, nrow = 11, ncol = B) theta <- c(lambda,beta) beta.hat <- 2.471546 for(j

Hello Everyone, I'm currently learning about quantile regressions. I've been using an optimizer to compare with the rq() command for quantile regression. When I run the code, the results show that my coefficients are consistent with rq(), but the intercept term can vary by a lot. I don't think my optimizer code is wrong and suspects it has something to do with the starting

Hi r-users, I have a matrix B and a list of 3x3 matrices (mylist). I want to calculate the quantiles in the list using each of the value of B as probabilities. The codes I wrote are: B <- matrix (runif(12, 0, 1), 3, 4) mylist <- lapply(mylist, function(x) {matrix (rnorm(9), 3, 3)}) for (i in 1:length(B)) { quant <- lapply (mylist, quantile, probs=B[i]) } But quant

Can anyone please tell me if there is a function to calculate confidence intervals for the results of the quantile function. Some of my data is normally distributed but some is also a squewed distribution or a capped normal distribution. Some of the data sets contain about 700 values whereas others are smaller with about 100-150 values, so I would like to see how the confidence intervals change

Dear all. I have a question on plotting the coefficients from a series of mutivariate quantile regressions. The following code plots the coefficients for each RHS variable x1 and x2. Is there a way to plot only the coefficients on x1? In the data I am using, I have a large number of fixed effects and do want to plot the coefficients on these fixed effects. quant.plot <-

Hi, I want to run a quantile regression (Y=a+bX+e) using normal and t copula for my dissertation. I 've read the documentation of "copula" and "copBasic". However, I still have difficulty to deal with my data. Details are as following: I've already loaded xls data into r using "XLConnect" package. excel.file<-file.path("Q:/dailyvstoxx.xls")

Dear, I want to make an application to calculate quantile within a For() I tried the following without success: ej. date p_val <- matrix(sample(10, 1000, replace=TRUE), 200,5) test 1 rr <- paste("p_val$",names(p_val[1]), sep="") quant <- quantile(rr, probs = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100)/100, na.rm=FALSE, type=1) test 2 rr <-

Hi, I am trying to find a simple way to numerically solve a system of two equations equal to zero with two unknowns (x_loc and y_loc). Here is a mock data set and below it, the equations I need to solve. theta<-c(180,135,90)/(2*pi) x<-c(0,0,15) y<-c(20,0,0) 0 = -sum((y_loc-y)*(sin(theta)*(x_loc-x)-cos(theta)*(y_loc-y))/(((x_loc-x)^2+(y_loc-y)^2)^0.5)^3) 0 =

I am trying to calculate quantiles of a data frame column split up by two factors: # Calculate the quantiles quarts = tapply(gdf$tt, list(gdf$Runway, gdf$OnHour), FUN=quantile, na.rm = TRUE) This does not work: > quarts 04L 04R 15R 22L 22R 27 32 33L 33R 0 NULL Numeric,5 NULL Numeric,5 NULL Numeric,5 NULL Numeric,5 NULL 1 NULL

Hello everyone! I am having some trouble supplying the gradient function to nlm in R for windows version 2.2.1. What follows are the R-code I use: fredcs39<-function(a1,b1,b2,x){return(a1+exp(b1+b2*x))} loglikcs39<-function(theta,len){ value<-sum(mcs39[1:len]*fredcs39(theta[1],theta[2],theta[3],c(8:(7+len))) - pcs39[1:len] * log(fredcs39(theta[1],theta[2],theta[3],c(8:(7+len)))))

All, I have been trying to get the following code to work: A.quantiles <- quantile(foo.frame$A, probs = seq(from = 0.05, to = 0.95, by = 0.05)) base.quantiles <- quantile(Efficacy205$BASELINE_RANK, probs = seq(from = 0.05, to = 0.95, by = 0.05)) gender <- levels(Efficacy205$GENDER) contrast.1 <- contrast(Model.1, list(TPCODE= 'A', AGE =

Dear readers I would like to make General Linear Model (GLM) for the following data set http://louise.hoffman.googlepages.com/fuel.csv The code I have written is fuelData<-read.table('fuel.csv',header=TRUE, sep=',') n<-dim(fuelData)[1] xOnes<- matrix(1,nrow=n,ncol=1) x<-cbind(xOnes,fuelData[,3]) y<-fuelData[,4] theta<-((t(x)%*%x)^(-1))%*%t(x)%*%y which gives

Hi all! I'm new to this forum so please excuse me if I don't conform perfectly to the protocols on this board! I'm trying to get an estimate of true prevalence based upon results from an imperfect test. I have various estimates of se/sp which could inform my priors (at least upper and lower limits even if with a uniform distribution) and found the following code on this website..

I'm making some illustrations and it would be convenient to automatically shade the overlapping portions of circles. These illustrations are for Social Choice theory, a field in political science and economics. I've wrestled together some examples so you can see what I mean, but have not mastered the "color overlapping sections" problem (as you will see):

Dear all, I have a function to optimize for a set of parameters and want to set a constraint on only one parameter. Here is my function. What I want to do is estimate the parameters of a bivariate normal distribution where the correlation has to be between -1 and 1. Would you please advise how to revise it? ex=function(s,prob,theta1,theta,xa,xb,xc,xd,t,delta) { expo1=

https://bugzilla.netfilter.org/show_bug.cgi?id=1206 Bug ID: 1206 Summary: segfault when snat map rule has been added Product: nftables Version: unspecified Hardware: x86_64 OS: Ubuntu Status: NEW Severity: normal Priority: P5 Component: nft Assignee: pablo at netfilter.org

I got some R code that I don't understand. Question as comment in code //where is t comming from, what is phi inverse rAC <- function(name, n, d, theta){ #generic function for Archimedean copula simulation illegalpar <- switch(name, clayton = (theta < 0), gumbel = (theta < 1), frank = (theta < 0), BB9 = ((theta[1] < 1) | (theta[2] < 0)), GIG = ((theta[2] < 0) |

Hello, I am using Dr. Harrell's design package to make a nomogram. I was able to make a beautiful one without stratifying, however, I will need to stratify to meet PH assumptions. This is where I go wrong, but I'm not sure where. Non-Stratified Nomogram: