similar to: density estimation with weighted sample

Hello everybody-- I have been playing with my data in spatstat, and what I'd like to present is a basic exploratory spatial analysis. I have used the following code, using a ppp.object called tsdspoints. The code develops the simulations and the envelopes I want, but I don't understand my first plot here, the [tsds.ghat.short$r, tsds.ghat.short$raw]...I cobbled together this code

A Likert scale may have produced counts of answers per category. According to theory I may expect equality over the categories. A statistical test shall reveal the actual equality in my sample. When applying a chi square test with increasing number of repetitions (simulate.p.value) over a fixed sample, the p-value decreases dramatically (looks as if converge to zero). (1) Why? (2) (If

Dear wizaRds, I use the MASS::kde2d function to estimate density of the two first principal components. I do that to have a graphic visualisation of a "group structure" in my dataset. So far, no problem. But i would like to estimate that density using weights according to the COS?? values that tells me if my observation is well represented on the factorial plan 1-2. I would like to

Hi there, I have a question regarding model comparisons that seems simple enough but to which I cannot find an answer. I am interested in developing a predictive model relating some measure of a tree's stem to the total leaf area (TLA) of the tree. Predictor variables might include, for example, the total cross-sectional area of the tree (commonly referred to as basal area) or the amount

Estimate region of highest probabilty density Dear R-community I have data consisting of x and y. To each pair (x,y) a z value (weight) is assigned. With kde2d I can estimate the densities on a regular grid and based on this make a contour plot (not considering the z-values). According to an earlier post in the list I adjusted the kde2d to kde2d.weighted (see code below) to estimate the

Is there code for bivariate kernel density estimation? For bivariate kernels there is kde2d in MASS kde2d.g in GRASS KernSur in GenKern (list probably incomplete) but none of them seems to accept a weight parameter (like density does since R 2.2.0) -- Erich Neuwirth, University of Vienna Faculty of Computer Science Computer Supported Didactics Working Group Visit our SunSITE at

Dear All, Apologies for not providing a reproducible example, but if I could, then I would be able to answer myself my question. Essentially, I am trying to fit a very complicated custom probability distribution to some data. Fitdistrplus does in principle everything which I need, but if require me to specify not only the density function d, but also the cumulative p and and inverse cumulative

Dear All, I am a novice when it comes to time-series analysis and at the moment I am actually interested in calculating the Hurst exponent of a time series. This question has already been asked quite some time ago http://bit.ly/98dZsi and I trust some progress has been made ever since. I was able to find some functions in the packages http://cran.r-project.org/web/packages/Rwave/index.html

Why not define your own functions based on d? e.g. myCumDist <- function(x) { integrate(d, lower=-Inf, upper=x)$value } myQuantile <- function(x) { uniroot(f=function(y) { h(y) - x }, interval=c(-5,5)) } # limits -5,5 should be replaced by your own which might require some fiddling e.g. d <- function(x) { exp(-x^2/2)/(sqrt(2*pi)) } # just an example for you to test with; use your own

Hi, I wanted to plot the histogram of a vector and then, plot the density function of subsets of the vector on the histogram. So I use truehist in MASS package and lines(density) as follows: length(b) = 1000 truehist(b) lines(density(b[1:100])) however the density plot of the first 100 points exceeds the max of y axis (see attached). how is it possible to make a graphics so that the density plot

Dear R-users, I intend to do a spatial analysis on the genetic structuring within a population. For this I had thought to prepare a kernel density estimate map showing the spatial distribution of individuals, while incorporating the genetic distances among individuals. I have a dataset of locations of N unique individuals (XY-coordinates) and an NxN matrix with the genetic distances given as a

I'm trying to create a density plot using census data, where the weights don't sum to 1. >plot(density(oh$FINCP,weights=oh$PWGTP)) Warning message: In density.default(oh$FINCP, weights = oh$PWGTP) : sum(weights) != 1 -- will not get true density How would I go about doing this? Thanks!

Hi All, I've put together some script which gives me the parameters of a modified NIG distribution. Now I'd like to include a weighted vector within the maximization function. The code below gives me parameter estimates which are implicitly equally weighted. nig.par.fit <- try(optim(vega, negllh, hessian = se, pdf = density.nig, tmp.data = data, transf =

Hi there, I'm trying to fit a growth curve to some data and need to use a weighted least squares estimator to account for heteroscedasticity in the data. A weights argument is available in nls that would appear to be appropriate for this purpose, but it is listed as 'not yet implemented'. Is there another package which could implement this procedure? Regards, Robert Brown

Hi, I'd like to use a three-dimensional dataset to build a kernel density and then sample from the distribution. I already used the npudens function in the np package to estimate the density and plot it: fit<-npudens(~x+y+z) plot(fit) It takes some time but appears to work well. How can I use this to evaluate the fitted function at a certain point, e.g. (x=1, y=1, z=1)?

Dear all, I'm struggling with weighted least squares, where something that I had assumed to be true appears not to be the case. Take the following data set as an example: df <- data.frame(x = runif(100, 0, 100)) df$y <- df$x + 1 + rnorm(100, sd=15) I had expected that: summary(lm(y ~ x, data=df, weights=rep(2, 100))) summary(lm(y ~ x, data=rbind(df,df))) would be equivalent, but

Hi, I am trying to estiamte parameters for gamma distribution using mle for below data using fitdist & fitdistr functions which are from "fitdistrplus" & "MASS"packages . I am getting errors for both functions. Can someone please let me know how to overcome this issue?? data y1<- c(256656, 76376, 6467673, 46446, 3400, 3100, 5760, 4562, 8000, 512, 4545, 4562,

I've figured out how to use optim (barely) to estimate 2 parameter = weibull distributions. I can't get over how easy this is. What I need to = do is use a weight in the observations..... For example,=20 the tree diameters and weights are are=20 4.70 , 100 6.00, 98 7.10, 75.0 8.10, 86.3 8.60, 80.456 8.90, 20.5 9.50, 16.6 11.40, 12.657 11.80, 12.47 14.50,

I am a physician examining an NHANES dataset available at the NCHS website: http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/demo_d.xpt http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/hdl_d.xpt http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/tchol_d.xpt Thank you to the R authors and the foreign package authors in particular. Importing from the SAS export

Hi I'm new to R and most things I want to do I can do but I'm stuck on how to weight a sample. I have had a look through the post but I can't find anything that addresses my specific problem. I am wanting to scale up a sample which has been taken based on a single variable (perf) which has 4 attributes H,I, J and K. The make up of the sample is shown below:- Perf Factored