similar to: Quantile regression: Discrepencies Between optimizer and rq()

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 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

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 <-

Dear all, I am having v.4.36 of Quantreg package and I noticed strange behaviour when weights were added. Could anyone please explain me what if the results are really strange or the behavioiur is normal. As an example I am using dataset Engel from the package and my own weights. x<-engel[1:50,1] y<-engel[1:50,2] w<-c(0.00123, 0.00050, 0.00126, 0.00183, 0.00036, 0.00100, 0.00122,

I've been trying to draw quantile linear regression lines across a scatterplot of my data using attach(forrq) plot(PREGNANT,DAY8,xlab="pregnant EPDS",ylab="postnatal EPDS",cex=.5) taus <- c(.05,.1,.25,.75,.9,.95) xx <- seq(min(PREGNANT),max(PREGNANT),100) for(tau in taus){ f <- coef(rq(DAY8~PREGNANT,tau=tau)) yy <-

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:

Hello, I have a question regarding shading regions under curves to display 95% confidence intervals. I generated bootstrap results for the slope and intercept of a simple linear regression model using the following code (borrowed from JJ Faraway 2005): > attach(allposs.nine.d) > x<-model.matrix(~log(d.dist,10))[,-1] > bcoef<-matrix(0,1000,2) > for(i in 1:1000){ +

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

Hi, how is it possible to retrieve the corresponding tau value for each observed data pair (x(t) y(t), t=1,...,n) when doing a quantile regression like rq.fit <- rq(y~x,tau=-1). Thank you for your help. Jaci --

Dear List, I am writing a custom panel function and xyplot method to plot the results of a procrustes analysis from the vegan package. I am having trouble getting the call to panel.arrows to work as I wish when conditioning. The attached file contains the function definitions for the xyplot method and the custom panel and prepanel functions I am using. This example, using data and functions from

I have a simple doubt: I have a function, say: test <- function (theta) { return (theta^2) } I can use: integrate (test,0,1) to obtain the area under de function. Can I do the opposite? I`d like to give the lower limit and the area I need as arguments, in order to get the upper limit. In other words, I`d like to obtain the quantile of the function (the lower limit could be 0, for example).

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

I sent a message a couple days ago about doing calculations for power of the ANOVA. Several people got back to me very quickly which I really appreciated. I'm working now on a similar problem, but instead of a balanced ANOVA, I have an unbalanced one. The first part of the question was: You assume that the within-population standard deviations all equal 9. You set the Type 1 error rate at á

I am a new to using quantile regressions in R. I have estimated a set of coefficients using the method="br" algorithm with the rq command at various quantiles along the entire distribution. My data set contains approximately 2,500 observations and I have 7 predictor variables. I receive the following warning message: Solution may be nonunique in: rq.fit.br(x, y, tau = tau, ...)

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R While this code run perfectly, it does not work for my data providing a warning message: In rq.fit.sfn(D, y, rhs = a) : tiny

I am new to R and am interested in using the program to fit quantile regression models to data collected from a multi-stage probability sample of the US population. The quantile regression package, rq, can accommodate person weights. However, it is not clear to me that boot.rq is appropriate for use with multi-stage samples (i.e., is capable of sampling primary sampling units instead of survey

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 =

Thanks to David's help I subset my large data set and produced a smaller one for a single stream and 7 factors of interest. The structure of this data frame is: str(burns.tds.anal) 'data.frame': 718 obs. of 4 variables: $ site : Factor w/ 143 levels "BC-0.5","BC-1",..: 1 1 4 6 4 4 4 5 5 5 $ sampdate: Date, format: "1996-06-02"

Hi guys im having a problem getting R to numerically integrate for some function, say f(bhat)*optimize(G(bhat)), over bhat. Where id like to integrate this over some finite range, so that here as we integrate over bhat optimize would return a different optimum. For instance consider this simple example for which I cannot get R to return the desired result: f <- function(bhat) exp(bhat) g

Hello, What is the best way to get ranks for a vector of values, limit the range of rank values and create equal count in each group? I call this uniform ranking...uniform count/number in each group. Here is an example using three groups: Say I have values: x = c(3, 2, -3, 1, 0, 5, 10, 30, -1, 4) names(x) = letters[1:10] > x a b c d e f g h i j 3 2 -3 1 0 5 10 30 -1 4 I