similar to: non-uniform time series embeddings

Thanks for your answer. However, my variable is simulated from the cumulative distribution function of the Poisson distribution. So, the pattern obtained from the function "qpois" is not the same as the observed pattern (i.e., obtained from the function "ppois") set.seed(5) mortality_probability <- round(ppois(seq(0, 7, by = 1), lambda = 0.9), 2)

Hi, Does anyone know a method for adding a linear/polynominal trend to a simulated arima model using the arima.sim function? Any help will be greatly appreciated. Cheers, Sam.

Thanks for your answer. I have attached the plot for representing the variable. I think that I need to draw a Hypercube sample for each age class (i.e., for 0, 1, 2, 3, 4, 5, 6, 7) in a given simulation (i.e., N = 1) and the LHS values for all age classes should be like the observed cumulative distribution (see attached figure). Thus, the output of randomLHS should be a matrix with 100 rows (N =

> How can I draw a Hypercube sample for the variable mortality_probability so > that this variable exhibits the same pattern as the observed distribution? One simple way is to use the uniform random output of randomLHS as input to the quantile function for your desired distribution(s). For example: q <- randomLHS(1000, 3) colnames(q) <- c("A", "B",

> However, my variable is simulated from the cumulative distribution function > of the Poisson distribution. Then I am afraid I don't know what you're trying to achieve. Or why. However, the principle holds; write a function that maps [0,1] to the 'pattern' you want, do that and apply it to the result from randomLHS. It happens that for generating data that follow a given

Hello, I am performing a sensitivity analysis using a Latin Hypercube sampling. However, I have difficulty to draw a Hypercube sample for one variable. I?ve generated this variable from a Poisson distribution as follows: set.seed(5) mortality_probability <- round(ppois(seq(0, 7, by = 1), lambda = 0.9), 2) barplot(mortality_probability, names.arg = seq(0, 7, by = 1), xlab = "Age

Ranjan Maitra writes: > Does anyone have a suggestion (or better still) code for sampling > from the uniform distribution over the convex hull of a set of > points? This is implemented in library 'spatstat'. If x and y are vectors of coordinates of your initial set of points, library(spatstat) W <- convexhull.xy(x, y) P <- runifpoint(42, W) will compute

Hi, Is there a function in R that lets one represent an integer in binary format for a given number of bits? So an example would be.... > binary.function(num=5, num.of.bits=8) > "00000101" Or, is this something I have to write myself? Any help would be appreciated. Cheers, Sam.

Hi, Does anyone know of a method for writing text to the graphics window, where there is *no* plot? Basically, I have developed a 'significance test' and I would like the output on the graphics window to say something about the input parameters and the stats of the significance test. Any help would be appreciated. Cheers, Sam.

Hi, I am trying to use the stl function in the ts package. It requires that the data is a univariant time series at the moment my data is in a vector. I have coerced it to a time series using.... crimets <- ts(crimeData) However, this does not work. Does anyone have any suggestions? Cheers, Sam. p.s. I am fairly new to R so apologies if this is a stupid posting.

Hi, I am considering whether or not to buy an apple mac. I have noticed on one of the R FAQs for Mac OS X that you cannot install packages from other OS's if C++ code is contained. My question is: Is it possible to build the package sources containing C++ code on the Mac and then install them? Thanks in advance, Sam.

Hi, I am trying to run a very computationally expensive procedure in R-2.0.0. and the process always gets killed after approx 8 minutes. This procedure calls some of my own C++ code - in case it was this code causing a memory leak I unload and then reload the .so file every time, however I still get the same problem. The procedure is run 16000 times and always calls the lm() function. My

Hi all, I am REALLY confused with the variance right now. for a discrete uniform distribution on [1,12] the mean is (1+12)/2=6.5 which is ok. y=1:12 mean(y) then var(y) gives me 13 1- on http://en.wikipedia.org/wiki/Uniform_distribution_%28discrete%29 wiki the variance is (12^2-1)/12=143/12 2-

Full_Name: Hsiu-Khuern Tang Version: 2.0.0 OS: Debian GNU/Linux Submission from: (NULL) (156.153.255.236) Hi all, If fit is an rpart object, plot(fit, uniform=TRUE) ignores uniform=TRUE if the graphics device is not already open. To reproduce this: library("rpart") example(plot.rpart) dev.off() # <- works OK without this plot(fit, uniform=TRUE) # <- uniform=TRUE is ignored

> What specific information do you need? All my experiments eventually analyze or transform the data dependence graph, which takes into account both registers and aliasing information. Because there is already a built-in support for graphs, I hoped there is also built-in support for data dependence graphs. That would allow, for example, to display them naturally with dot utility.

> Is there some uniform way to find data dependence between two (sets of) > instructions? There is use/def data, and there is AliasAnalysis > information, but I am looking for something more high-level and uniform. > I guess I am missing then was abandoned. Any ideas? Nope, there currently isn't a uniform interface for doing so, at least not at the LLVM level. As you said,

In the wake of discussions about documentation of R's random number generation (and whether there should be links to .Random.seed in other places), here is a proposed patch to Uniform.Rd from 2.8.1. (Watch out for line breaks.) [R-core may choose not to accept this, but at least it's easy if they want it.] cheers Ben Bolker *** Uniform.Rd.orig 2009-01-04 14:10:47.000000000 -0500

Dear all, I am trying to simulate from discrete uniform distribution. But I could not find any in-built code in R. Could anyone help me please? Thanks in advance for the time and help. Cassie [[alternative HTML version deleted]]

When `length( skewed.probs ) > 200' uniform samples are generated in R-devel. R-3.5.1 behaves as expected. `epsilon` can be a lot bigger than illustrated and still the uniform distribution is produced. Chuck > set.seed(123) > > epsilon <- 1e-10 > > ## uniform to 200 then small > p200 <- prop.table( rep( c(1, epsilon), c(200, 999-200))) > ## uniform to 201

Guys, Is there some uniform way to find data dependence between two (sets of) instructions? There is use/def data, and there is AliasAnalysis information, but I am looking for something more high-level and uniform. I guess I am missing something, because such interface seems to exist once and then was abandoned. Any ideas? Victor -- IBM Research Lab in Haifa, Israel