search for: jointly

>From my book on corpus linguistics with R: # (10) Imagine you have two vectors a and b such that a<-c("d", "d", "j", "f", "e", "g", "f", "f", "i", "g") b<-c("a", "g", "d", "f", "g", "a", "f", "a",

Suppose I make two discrete variables -- > D <- data.frame(f1=sample(1:5,100,replace=T), f2=sample(1:5,100,replace=T)) I know I can do: > table(D$f1, D$f2) 0 1 2 3 4 0 5 5 5 5 4 1 4 2 6 7 3 2 5 3 5 3 6 3 3 1 3 1 2 4 6 4 3 3 6 > table(D$f1) 0 1 2 3 4 24 22 22 10 22 > table(D$f2) 0 1 2 3 4 23 15 22 19 21 which is all great. But how do I produce the

Hello, I've been postponing some of my encoding for when joint stereo gets implemented. The reason I've been doing this, is that I am under the impression this is the largest step in the quality/bitrate ratio that's left. Now I'm wondering if I am correct in thinking this. lame's documentation seems to imply it doesn't make much of a difference at higher than 128kbps (I

Hi, I've just read this on Vorbis Xtreme site: "11. YOU SAY THAT OGG VORBIS IS PATENT-FREE, BUT I SAW A PATENT NAMED 'JOINT-STEREO' ON FRAUNHOFER'S PATENT LIST? SO OGG VORBIS ACTUALLY ISN'T PATENT-FREE SINCE IT ALSO USES JOINT-STEREO? No. You can't judge on a patent just by looking at its name - what's 'inside' is what matters. So if the name of the

Hello, I am using sm.density() to find the bivariate joint PDFof events: For eg, x<-cbind(rnorm(30),rnorm(30)) den<-sm.density(x) Then I get the joint pdf from den$estimate in order to constructthe joint cumulative PDF. However, summing up all the values from den$estimateisnot equal to 1(have multipliedby the grid size). Anyone could help? Thanks. mc [[alternative HTML version

Hi, I conduct a panel data estimation and obtain estimators for two of the coefficients beta1 and beta2. R tells me the mean and covariance of the distribution of (beta1, beta2). Now I would like to find the distribution of the quotient beta1/beta2, and one way to do it is to simulate via the joint distribution (beta1, beta2), where both beta1 and beta2 follow t distribution. How could we

Dear all, Please, look at the following code: attach(geyser) f1 <- kde2d(duration, waiting, n = 5) a <- 0 for (i in 1:5){ for (j in 1:5){ a <- a + f1$z[i,j] } } As far as I understood from Help kde2d returns matrix elements of which are values of joint probability mass function Pr(X=x,Y=y) therefore, sum of its elements should sum to 1. Which is not the case from my check. Where is

Dear all, Given rr<-data.frame(r1<-rnorm(1000,10,5),r2<-rnorm(1000,220,5)) How can I add a column (rr$p) for the joint probability of each r1 & r2 pair? I know how to add the column.. I just dont know how to compute the p value for joint probabilities given the two samples. //M

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 all, I'm fairly new to R, so I'm still trying to feel out what is available to me. I would like to be able to plot joint density in a two dimensional plot where density is indicated by color or darkness gradients, like a 2d color coded topographic map. Ideally, the output would be something I could then plot other points or lines on. Currently, I'm calculating joint density with

...tate names and years, so Connecticut and 2010, Maryland and 2010 and Vermont and 2010. I'm trying the following subset code: newdata<- subset(bpa, (!State=="Connecticut" & year<"2010")) It appears that it's only evaluating both criteria independently and not jointly, so this is returning all cases in 2008 and 2009, leaving out connecticut for those years as well. How do I get subset to return a dataset based on the joint occurrence of values of two variables? Yours, Simon Kiss str(bpa) 'data.frame': 150 obs. of 5 variables: $ State : Factor w/ 5...

Dear R users, For a uni-variable distribution represented in a numerical vector, we can obtain a distribution function using 'ecdf', and then calculate corresponding p-values. But if I have a 2-column dataframe representing a bi-variable joint distribution, given a pair of values, how can I get the p-value? And how can I plot out the density of the joint distribution? Best

Hello, I am interested in fitting a segmented model with unknown joint points in nls and perhaps eventually in nlme. I can fit this model in sas (see below, joint points to be estimated are a41 and a41), but am unsure how to specify this in the nlm function. I would really appreciate any suggestions or example code. Thanks a lot. -andy proc nlin data=Stems.Trees; params b41=-3 b42=1.5

  Dear R helpers   Suppose I have two sets of ranges (interest rates) as   Range 1 : (7 – 7.50, 7.50 – 8.50, 8.50 – 10.00) with respective probabilities 0.42, 0.22 and 0.36.     Range II : (11-12, 12-14, 14-21) with respective probabilities 0.14, 0.56 and 0.30 respectively.     My problem is to form the combinations of these ranges in a decreasing order of joint probabilities. It is assumed that

When I was computing some joint probabilities, I found that R reported most of the results to to -Inf and thus didn't record the value. I guess it is b/c the joint log(probability) can be extremely small. Is there a way in R to keep the values even if they are small?

For a homework question. I was wondering if rcmdr has a function to plot a graph of a bivariate function of X and Y. I have a function with joint pdf fX,Y(x,y) = x+y for 0<x<1 , 0<y<1 I've tried > x <- seq(0,1,.001) > y <- seq(0,1,.001) > r = x+y > plot(r) but it seems to just add them together say .2+.2 .3+.3 not other possibilities like .9 + .1 Thanks

I've got an idea for joint multichannel coding. Here is my idea: My idea is a process for coding 2,3,4 or 5 combined channels, in a compatible way for decoders unable to deal with combined channels. part1: All channels to be coded are summed, each one effected by a coefficient related to its sound power importance for the listener. The sum of all channels is devided by the square root of the

Dear All, I am estimating a Cox proportional hazard model, with several interactions of the type a*z + a*y + a*x + b*z + b*y + b*x. I need to know if the first three (the "a"s) are jointly significantly different from the last three (the "b"s). I have tried several approaches, but have been unsuccessful. Here's the model, and the code I came up with, with the obvious shortcomings. modelPG2 <- coxph(Surv(t0, t, d) ~ civilian + monarch + txmonarch...

Hi, Can someone set me straight as to how to write formulas in R to indicate: complete independence [A][B][C] joint independence [AB][C] conditional independence [AC][BC] nway interaction [AB][AC][BC] ? For example, if I have 4 factors: hair colour, eye colour, age, sex does > mosaicplot( frequency ~ hair + eye + age + sex) mean that the model fitted is of complete independence of

X and Y are independently distributed random variables. I would like to study the contours of the joint density of these two variables. Any function to call? Thank you very much! -- View this message in context: http://www.nabble.com/How-to-plot-contours-for-joint-density-of-2-independently-distributed-r.v.--tp19493126p19493126.html Sent from the R help mailing list archive at Nabble.com.