similar to: nls

Hello. I have the following distance matrix between 8 points: [1,] 0.000000 3.162278 7.280110 8.544004 7.071068 9.899495 6.403124 8.062258 [2,] 3.162278 0.000000 5.000000 6.403124 4.472136 8.944272 6.082763 8.062258 [3,] 7.280110 5.000000 0.000000 1.414214 1.000000 5.000000 4.242641 5.830952 [4,] 8.544004 6.403124 1.414214 0.000000 2.236068 4.123106 4.472136 5.656854 [5,] 7.071068 4.472136

When I try to select only those rows from the following data frame, called "data", in which X > Y X Y V3 2 2 1 8.062258 3 3 1 2.236068 4 4 1 6.324555 5 5 1 5.000000 6 1 2 8.062258 8 3 2 9.486833 9 4 2 2.236068 10 5 2 5.656854 11 1 3 2.236068 12 2 3 9.486833 14 4 3 8.062258 15 5 3 5.099020 16 1 4 6.324555 17 2 4 2.236068 18 3 4 8.062258 20 5 4 5.385165 21 1 5 5.000000

Does anyone know of a way of dealing with unbalanced mixed effects (fixed and random factors) for fully factorial designs. An example of such data is given below; The response variable is SQRTRECRUITS SEASON is a random factor DENSITY is a fixed factor Thus DENSITY:SEASON is a fixed factor. Therefore, whereas the effects of SEASON and DENSITY:SEASON should be tested against the overall

Dear R-help list, I'm trying to overlay a number of data objects in a plot. Following an earlier example on the list I've created an empty plot as follows > xlim <- range(as.numeric(c("0","10000"))) > ylim <- range(as.numeric(c("0","25")) ) > plot(NA, xlim=xlim, ylim=ylim, xlab="distance", ylab="semivariance")

Dear all, I have the following dataset: each row corresponds to count of forest floor small mammal captured in a plot and vegetation characteristics measured at that plot > sotr plot cnt herbc herbht 1 1A1 0 37.08 53.54 2 1A3 1 36.27 26.67 3 1A5 0 32.50 30.62 4 1A7 0 56.54 45.63 5 1B2 0 41.66 38.13 6 1B4 0 32.08 37.79 7 1B6 0 33.71 30.62

Dear All, I have been trying to simulate data from a fitted glm using the simulate() function (version details at the bottom). This works for lm() fits and even for lmer() fits (in lme4). However, for glm() fits its output does not make sense to me -- am I missing something or is this a bug? Consider the following count data, modelled as gaussian, poisson and binomial responses: counts

https://bugs.freedesktop.org/show_bug.cgi?id=70511 Priority: medium Bug ID: 70511 Assignee: nouveau at lists.freedesktop.org Summary: nouveau_bo_name_get segmentation fault while running root tutorials/gl/glbox.C QA Contact: xorg-team at lists.x.org Severity: normal Classification: Unclassified

Hello, I'm using the gstat package within R for an automated procedure that uses ordinary kriging. I can see that there is a logical ("singular") atrtibute of some adjusted model semivariograms: .- attr(*, "singular")= logi TRUE I cannot find documentation about the exact meaning and the implications of this attribute, and I dont know anything about the inner calculations

# I need to generate some data. I'm modeling some time series that follow a # negative exponential decay (mostly). I have 20 samples that can easily be fit with cubic splines. # What I want to do is generate many thousands of similar samples using the parameters from the splines # For instance one data sample looks not unlike this: foo.curve <- 1 * exp(-0.01 * 1:500) + 0.5

Hi, I tried to pass the function dist() as an argument, but got an error message. However, almost the same code with mean() as the function to be passed, it works ok. foo<-function (x, xfun = dist) { xfun(x) } foo(matrix(1:100,nrow=5)) Error in foo(matrix(1:100, nrow = 5)) : could not find function "xfun" foo<-function (x, xfun = mean) { xfun(x) } foo(1:10)

Hi, I'm not sure how use curve(dexGAUS(… None of the following four works: rt<- rexGAUS(100, mu=300, nu=100, sigma=35) m1<-gamlss(rt~1, family=exGAUS) curve(dexGAUS(rt=x, mu=300 ,sigma=35,nu=100), 100, 600, main = "The ex- GAUS density mu=300 ,sigma=35,nu=100") curve(dexGAUS(x=rt, mu=300 ,sigma=35,nu=100), 100, 600, main = "The ex- GAUS density mu=300

What is the difference of using 2L versus 2 as the margin argument in the apply() function? Where can I find detailed information on all of the possible margin arguments? > x [,1] [,2] [1,] 1 2 [2,] 3 4 [3,] 5 6 [4,] 7 8 [5,] 9 10 > sqrt(apply(x, *2L*, function(r.st) var(r.st[!is.na(r.st)]))) [1] 3.162278 3.162278 > sqrt(apply(x,* 2*, function(r.st)

Dear R users, I'm estimating a mixed effects model in which the spatial correlation is controlled for by the "corGaus" structure. I'm wondering if there is a document or paper that explains how the spatial correlation structure (such as "corExp" or "corGaus") works. Let me use the example and data posted on UCLA's R FAQ webpage to explain my problems.

Hello folks, I am on learning phase of R. I have developed Regression Model over six predictor variables. while development, i found my all data are not very linear. So, may because of this the prediction of my model is not exact. Here is the summary of model : Call: lm(formula = y ~ x_1 + x_2 + x_3 + x_4 + x_5 + x_6) Residuals: Min 1Q Median 3Q Max -125.302

I've never noticed this before, though it's probably not new. In 1.2.3 for Windows, if I print a short vector the formatting is different than if I print a long one, whether or not they fit on one line. The short/long split appears to be between 9 and 10 elements: > 1:9 [1] 1 2 3 4 5 6 7 8 9 > 1:10 [1] 1 2 3 4 5 6 7 8 9 10 > sqrt(1:9) [1] 1.000000 1.414214

Dear List, I have been trying to extract associations from a matrix whereby individual locations are within a certain distance threshold from one another. I have been able to extract those individuals where there is 'no interaction' (i.e. where these individuals are not within a specified distance threshold from another individual) and give these individuals a unique Group ID containing

To improve the efficiency of a process I am writing I would like to cache results. So I would like a data structure like a hash table. So if I call Z <- f(Y) I can cache Z associated with Y: CACHE[Y] <- Z I am stumped. I expected to be able to use a list for this but I cannot figure how.... cheers Worik [[alternative HTML version deleted]]

Full_Name: Cam Webb Version: 1.6.0 (fink X11 compile) OS: Mac OS X (Jaguar) Submission from: (NULL) (64.168.28.87) This is an unpredictable, intermittant hang during cmdscale of the mva library. Some data never cause a problem, other data always do, abut I can't track down the difference in the structure of the data. Sometimes the function will work for `difficult' data after it has

Hello, in scilab /Matlab there are functions that can create linear sequences (like R's seq()) as well as logarithmic sequences. Is there a logarithmic aequivalent of seq()? Or maybe this would be an idea for newer R-releases, maybe a type-option with "linear" and "logarithmic" as parameters....?! Ciao, Oliver

In trying to streamline various optimization functions, I would like to have a scratch pad of working data that is shared across a number of functions. These can be called from different levels within some wrapper functions for maximum likelihood and other such computations. I'm sure there are other applications that could benefit from this. Below are two approaches. One uses the <<-