similar to: unbalanced mixed effects models for fully factorial designs

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

Is there anyway to change any y[i] value (i=2,...6) to make following NLS workable? x <- c(0,5,10,15,20,25,30) y <- c(1.00000,0.82000,0.68000,0.64000,0.66667,0.68667,0.64000) lm(1/y ~~ x) nls(1/y ~~ a+b*x^c, start=list(a=1.16122,b=0.01565,c=1), trace=TRUE) #0.0920573 : 1.16122 0.01565 1.00000 #Error in numericDeriv(form[[3]], names(ind), env) : # Missing value or

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

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

Dear list members, Where can I find code for computing the p*p variance-covariance matrix given a vector of p variances (ordered varA, varB, ..., varp) and a vector of all possible correlations (ordered corAB, corAC, ..., corp-1,p)? I know that the covariance between 2 variables is equal to the product of their correlation and their standard deviations: corAB * varA^.5 * varB^.5 and so:

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)

I think one needs an LU decomposition rather than QR. However, I couldn't find anything off the shelf to do an LU, other than learning that determinant() now uses LU instead of QR or SVD, so the code to do it must be in there for those that want it. You'll probably need to divide rows of U by the first entry if you insist on the unique reduced REF. However, I can't see any reason

I am faced with a situation wherein I have to use multiple lapply's. The pseudo-code could be approximated to something as below: For each X from i=1 to n For each Y based on j=1 to m For each F from 1 to f Do some calculation based on Fij Store Xi,Yj = Fij End For F End for Y End for X Is there anyway to optimize the processing logic further? I *guess* using the multiple lapply

Hi, The following url is being played with incorrect a/v sync in Firefox: http://195.10.10.216/flumotion/videodemo/med.ogg?token=c965dab73e5ed982821e01174e87925a4bb3134e4bb9aaec (it is time limited, but should work a few days, I could provide) Tested with 3.6.3pre on Linux (with and without PulseAudio) and Firefox 3.6 on Windows XP. Looking at firefox source code, I found the library that is

Hello My dataset is as follows: jobno recruits 1100 18 1200 1 1850 5 2100 190 2789 25 3000 1 . . . . the dataset has 130 rows I want to plot barplot from left side and cumulative curve from right side in one graph itself using layers in ggplot2. I sorted the recruits 1st in decreasing order

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

Hello all, I'm having trouble creating an adjacency matrix. Basically, I need to turn the following distance matrix into an adjacency matrix based on whether values are >1.5 or not. If they are >1.5, then the returned value should be 0. If they are =<1.5, then the returned value should be 1. DistanceMatrix: A B C D E [1,]

I could not figure out what was the default when I ran hclust() without specifying the method. For example: I just have a code like: hclust(dist(data)) Any input would be appreciated:) [[alternative HTML version deleted]]

Hello all, I am brand new to R and doing an exercise for a class. I need to find the nearest neighbour for points in the following matrix: > DistanceMatrix x1 x2 x3 x4 x5 [1,] 0.000000 2.828427 1.581139 2.236068 2.000000 [2,] 2.828427 0.000000 1.581139 4.123106 2.000000 [3,] 1.581139 1.581139 0.000000 2.549510 2.121320 [4,] 2.236068 4.123106 2.549510

Ladies and Gentlemen, using > A <- structure(c(1, 0, 0, 3, 2, 1, 4, 5, -3, -2, 1, 0), .Dim = as.integer(c(3,4))) I get > dim(A) [1] 3 4 > qr.R(qr(A),complete=TRUE) [,1] [,2] [,3] [,4] [1,] -1 -3.000000 -4.000000 2.0000000 [2,] 0 -2.236068 -3.130495 -0.8944272 [3,] 0 0.000000 -4.919350 -0.4472136 > qr.R(qr(A),complete=FALSE) [,1]

Looking to the wonderful statistical advice that this group can offer. In behavioral science applications of stats, we are often introduced to coefficients for orthogonal polynomials that are nice integers. For instance, Kirk's experimental design book presents the following coefficients for p=4: Linear -3 -1 1 3 Quadratic 1 -1 -1 1 Cubic -1 3 -3 1 In R orthogonal

Hi there, Given a positive definite symmetric matrix, I can use chol(x) to obtain U where U is upper triangular and x=U'U. For example, x=matrix(c(5,1,2,1,3,1,2,1,4),3,3) U=chol(x) U # [,1] [,2] [,3] #[1,] 2.236068 0.4472136 0.8944272 #[2,] 0.000000 1.6733201 0.3585686 #[3,] 0.000000 0.0000000 1.7525492 t(U)%*%U # this is exactly x Does anyone know how to obtain L such

Dear R Gurus - Please help me to make a multiple-panel plot using xYplot in which the y-axis of each panel is dependent only on the data of that plot. Here is the dataset: response date mean Lower Upper 1 density 1 271.0000000 249.2500000 289.0000000 2 density 4 296.7500000 280.5000000 313.0000000 3 recruits 1 271.0000000 249.2500000

Hi All, I have a data.frame: >pop consumed ind recruits gonad cell 1 516.74 1 0.02 20.21 0.25 2 1143.20 1 0.02 20.21 0.50 3 250.00 1 0.02 20.21 0.25 4 251.98 1 0.02 18.69 0.25 5 598.08 1 0.02 18.69 0.25 6 437.38 1 0.02 18.69 0.25 7 250.00 1 0.02 17.39 0.25 8 250.00 1 0.02 17.39 0.25 9