similar to: Use of distribution model to estimate probability of an event

Ok, I try it again with plain text, with a simple R code example and just sending it to the r list and you move it to sig finance if it is necessary. I try to be as detailed as possible. I want to fit a distribution to my financial data using a volatility model to estimate the VaR. So in case of a normal distribution, this would be very easy, I assume the returns to follow a normal distribution

Hi, I am using the package "demography" from Rob Hyndman for the Lee-Carter-Model. It is an amazing powerful tool but I am struggling with one issue: I want to compute different percentiles of the survival probability distribution derived from the Lee-Carter-Forecast (e.g. the 50%tile, 60%tile, 75%tile and 99%tile) for each of the next 10 years. Is there any possibility to retrieve

I would like to determine the probability of an event at a specific timepoint given the linear predictor of a given Cox model. For instance, assume that I fit the following model: data(pbc) fit <- coxph(Surv(time, status)~ age, data=pbc) To extract the value of the linear predictor for each patient in the dataset: prd <- predict(fit, newdata=pbc, type="lp")

Dear R-users, I would like to determine the probability of event at specific time using cox model fit. On the development sample data I am able to get the probability of a event at time point(t). I need probability score of a event at specific time, using scoring scoring dataset which will have only covariates and not the response variables. Here is the sample code: n = 1000 beta1 = 2; beta2 =

Hi all, Does anyone have the knowledge to help me identify a package capable of forecasting a MULTIPLE regression model? i have a model with one one dependant variable and 4 independant variables. i would like to forecast confidence intervals for a few steps ahead...(DENSITY forecasting). PS i can forecast a univariate vector, with package 'forecast', however, i want one that can do

Dear R users, This is a KDE beginner's question. I have this distribution: > length(cap) [1] 200 > summary(cap) Min. 1st Qu. Median Mean 3rd Qu. Max. 459.9 802.3 991.6 1066.0 1242.0 2382.0 I need to compute the sum of the values times their probability of occurence. The graph is fine, den <- density(cap, from=min(cap), to=max(cap), give.Rkern=F)

Hi list, I have a group of data. It looks like they follow a exponential distribution. In R, how can I esimate lamda, that is the rate in pexp, of the distribution and can I use Kolmogorov-Smirnov for hypothesis testing in such a situation? I have read the "8.2 Examing the distribution of a set of data" of "An Introduction to R" but I did not find any clues on this issue.

Hallo I would appreciate any help with the following. Problem: Suppose A, B and C are independent U(0,1) random variables. What is the probability that A(x^2) + Bx + C has real roots? I have done the theoretical work and obtained an answer of 1/9 = 0.1111. Now I want to show my students to get this in R with simulation. Below are two attemps, both giving the answer to be about 0.26. Could

R version 2.12.0, 64 bit on Windows. Here is a short script that illustrates the problem: library(tseries) library(xts) setwd('C:\\cygwin\\home\\Ted\\New.Task\\NKs-01-08-12\\NKs\\tests') x = read.table("quotes_h.2.dat", header = FALSE, sep="\t", skip=0) str(x) y <- data.frame(as.POSIXlt(paste(x$V2,substr(x$V4,4,8),sep=" "),format='%Y-%m-%d

Dear all, I stumbled upon what appears to be a troublesome issue when sampling from an ARIMA model (from Rob Hyndman's excellent 'forecast' package) that contains a seasonal AR component. Here's how to reproduce the issue. (I'm using R 2.9.2 with forecast 2.19; see sessionInfo() below). First some data: > x <- c( 0.132475, 0.143119, 0.108104, 0.247291, 0.029510,

(My apologies if this is a repeated posting. I couldn't find any trace of my previous attempt in the archive.) I'm having trouble with forecast() in the dse2 package. It works fine for me on a model without a trend, but gives me NaN output for the forecast values when using a model with a trend. An example: # Set inputs and outputs for the ARMA model fit and test periods

I want to calculate the probability that a group will include a particular point using the squared Mahalanobis distance to the centroid. I understand that the squared Mahalanobis distance is distributed as chi-squared but that for a small number of random samples from a multivariate normal population the Hotellings T2 (T squared) distribution should be used. I cannot find a function for

Hello,   Could someone help me please and to tell how to get the probability from empirical distribution (not parametric) for each data value (R function). For example, for normal distribution there is such a function like:   “pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)”   I need the same function only for the empirical distribution (which does not correspond to any typical

Hello,   Could someone help me please and to tell how to get the probability from empirical distribution (not parametric) for each data value (R function). For example, for normal distribution there is such a function like:   “pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)”   I need the same function only for the empirical distribution (which does not correspond to any typical

I would like to build a model in R to simulate the seed dispersal by one plant. The plant produced 5 seeds and the probability of falling inside the eight closest space was 0.8 and in the next space 0.2 and in the rest space 0: 0 0 0 0 0 0 0.2 0.2 0.2 0.2 0.2 0 0.2 0.8 0.8 0.8 0.2 0 0.2 0.8 1 0.8 0.2 0 0.2 0.8 0.8 0.8 0.2 0 0.2 0.2 0.2 0.2 0.2 0 0 0 0 0 0 0

Dear R family, I want to calculate the joint probability (distribution) of two random continuous variables X and Y. Could to please tell me how to do it?Thanks in advance.. elisa [[alternative HTML version deleted]]

Hi guys Given a exponential curve, is there any function on r that can generate exponential distributed random numbers? in General I want an function that can transform one probability distribution into another?? Regards ****************************************************************** Bander ************************************* [[alternative HTML version deleted]]

hi all I would like to introduce my R package: empirical: Probability Distributions as Models of Data The description is: Computes continuous (not step) empirical (and nonparametric) probability density, cumulative distribution and quantile functions. Supports univariate, multivariate and conditional probability distributions, some kernel smoothing features and weighted data (possibly useful

hi all I would like to introduce my R package: empirical: Probability Distributions as Models of Data The description is: Computes continuous (not step) empirical (and nonparametric) probability density, cumulative distribution and quantile functions. Supports univariate, multivariate and conditional probability distributions, some kernel smoothing features and weighted data (possibly useful

Please, I seek expertise and advice, possibly leads to R packages or stats literature. My data: measurements of economic variables for each county of California over 37 years. My dependent variable is square feet of office floor space permitted to be added in a county. Independent variables include for example change in number of office jobs in same county same year (and lagged years).