similar to: Introducing empirical: Probability Distributions as Models of Data

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

Dear useRs, I have an empirical distribution (not normal etc) and I want to draw random samples from it. One solution I can think of is to compute let's say 100 quantiles, then use runif() to draw a random number Q between 1 and 100, and finally run runif() again to pull a random value from the quantile Q. Is there perhaps a better/more elegant way of doing this? Thank you, b.

Hi, I am going to sound mean here, however I don't feel the document is "very comprehensive". Maybe concise is a better word. I quickly looked through the document. The biggest problem is that there is very little discussion on multivariate distributions. Noting that multivariate distributions play a critical role in statistical theory, plus are gaining an increasing number of

I found you online....... Can you help with empirical probability? Hi Partha. I really liked your email that you sent me, it really inspired me. I have been breezing through the chapters, and doing quite well, You should be a teacher. After all the time my college instructor spent with the class on the slopes etc.... There were very few of us who really understood it. However, after reading

I am not sure if I should call this a bug or just a design imperfection. I have noticed that the data.frame function does not preserve the integrity of multivariate components. Here is a simple illustration: > x <- matrix(1:6,3,2) > y <- 1:3 > z <- data.frame(x=x,y=y) > z$x # NULL > z$y # [1] 1 2 3 One can however get by that by using model.frame: > zz <-

I found this a few months ago, but for the life of me I can't remember what the function or package was, and I have had no luck finding it this week. I have found, again, the functions for working with distributions like Cauchy, F, normal, &c., and ks.test, but I have not found the functions for estimating the distribution parameters given a vector of values. What I need to do is

Hello, I am not sure how to do this in R. Any suggestion would be appreciated. I have a vector of values from where I build an empirical CDF. For example: > x <- seq(1,100) > x <- sample(x,1000,replace=T) > quantile(x,probs=seq(0,1,.05)) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 1.00 5.00 10.00 16.00 20.00 25.00 31.00 36.00 41.00

Readers, Using version 251 I tried the following command: lm(y~a+b,data=datafile) Resulting in, inter alia: ... coefficients (intercept) a 1.2 3.4 Packages installed: acepack ace() and avas() for selecting regression transformations adlift An adaptive lifting scheme algorithm akima Interpolation of irregularly spaced

Hi All, Does anybody know if there is a simple way to draw numbers from an empirical distribution? I know that I can plot the empirical cumulative distribution function this easy: plot(ecdf(x)) Now I want to pick a number between 0 and 1 and go back to domain of x. Sounds simple to me. Any suggestion? Thank you, Your culprit (everybody needs a culprit) -- View this message in context:

Dear List, I have some discrete data and want to calculate the percentiles and the percentile ranks for each of the unique scores. I can calculate the percentiles with quantile(). I know that "ecdf" can be used to calculate the empirical cumulative distribution. However, I don't know how to exact the cumulative probabilities for each unique element. The requirement is similar

Dear R users, A new package titled `np' is now available from CRAN. The package implements recently developed kernel methods that seamlessly handle the mix of continuous, unordered, and ordered factor datatypes often found in applied settings. The package also allows users to create their own nonparametric/semiparametric routines using high-level function calls (via the function npksum())

Dear R users, A new package titled `np' is now available from CRAN. The package implements recently developed kernel methods that seamlessly handle the mix of continuous, unordered, and ordered factor datatypes often found in applied settings. The package also allows users to create their own nonparametric/semiparametric routines using high-level function calls (via the function npksum())

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as

Hi, I have just started using R. Now I have the following problem: I want to create an Empirical Cumulative Distribution Function and I only came so far: F10 <- ecdf(x) plot(F10, verticals= TRUE, do.p = TRUE, lwd=3) x=c(1.6,1.8,2.4,2.7,2.9,3.3,3.4,3.4,4,5.2) Now I'd like to use arguments such as xlabs and main but I don't know how to integrate them. I hope someone can help me, I am

I'm following up on a question I posted 8/10/2010, but my newsreader has lost this thread. > Barrett & Ling, JASA, 1992, v.87(417), pp184-191 define general > classes of influence measures for multivariate > regression models, including analogs of Cook's D, Andrews & Pregibon > COVRATIO, etc. As in univariate > response models, these are based on leverage and

Hello all: I'm sure this is a trivial request, but I'm still a beginner at this, and haven't been able to find it. I need to create simulated data based on some empirical distributions of a single variable. I've found R functions to help me simulate data based on analytical distributions, or to make simulations based on correlation matrices, but nothing so simple as what I need.

Barrett & Ling, JASA, 1992, v.87(417), pp184-191 define general classes of influence measures for multivariate regression models, including analogs of Cook's D, Andrews & Pregibon COVRATIO, etc. As in univariate response models, these are based on leverage and residuals based on omitting one (or more) observations at a time and refitting, although, in the univariate case, the

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and

In message <Pine.GSO.4.31.0107010731110.7616-100000@auk.stats>, Prof Brian D Ripley <ripley@stats.ox.ac.uk> writes > >You do realize that the Kolmogorov tests (and the Kolmogorov-Smirnov >extension) assume continuous distributions, so the distribution theory >is not valid in this case? > >S-PLUS does stop you doing this: > >> ks.gof(o,