similar to: factorial ANOVA for block/split-plot design

Dear R Help - I am analyzing data from an ecological experiment and am having problems with the ANOVA functions I've tried thus far. The experiment consists of a blocked/split-plot design, with plant biomass as the response. The following is an overview of the treatments applied (nitrogen addition, phosphorus addition, and seeded/not seeded) and at what level (block, main-plot, and sub-plot):

Hi all, I'm working on a target whose registers have equal-sized subregisters and all of those subregisters can be named (or the other way round: registers can be grouped into super registers). So for instance we've got 16 registers W (as in wide) W0..W15 and 32 registers N (as in narrow) N0..N31. This way, W0 is made by grouping N0 and N1, W1 is N2 and N3, W2 is N4 and N5, ..., W15 is

Hi Everyone, Working on a REST-based web application, and I need some ammo in dealing with my supervisors and contractors. Is it better to: Method 1: Take in one large XML-based POST request, which contains 1st, 2nd and 3rd level elements, and build the associated child and grandchild objects in the parent controller, Method 2: Instead use a number of smaller XML-based POST requests

Hi all, I have a following problem: - Remus network buffering doesn''t work. It seems to be because of no vif is reported by the function server.xend.domain on line 29 of /usr/local/lib/python2.7/dist-packages/xen/remus/vm.py (seen using pdb) : 27 if domid: 28 try: 29 self.dominfo = server.xend.domain(domid, ''all'') >

Hello all, I'm having difficulty with setting up a mixed model using lme in the nlme package. To summarize my study, I am testing for effects of ornamentation on foraging behavior of wolf spiders. I tested spiders at two different ages (penultimate vs. mature) and of two different phenotypes (one species tested lacks ornamentation throughout life [non-ornamented males] while the other

Hello, I''ve recently setup a Linux Dom0 with a 3.0.17 kernel and Xen 4.1.2, and since the 3.x series doesn''t have blktap support I''m using qdisk to attach raw images. I''ve been playing with small images, something like 1GB, and everything seemed fine, speed was not fantastic but it was ok. Today I''ve set up a bigger machine, with a 20GB raw hdd and the

Hi all, I have some problems with the mle2 function > RogersIIbinom <- function(N0,attackR3_B,Th3_B) {N0-lambertW(attackR3_B*Th3_B*N0*exp(-attackR3_B*(24-Th3_B*N0)))/(attackR3_B*Th3_B)} > RogersII_B <- mle2(FR~dbinom(size=N0,prob=RogersIIbinom(N0,attackR3_B,Th3_B)/N0),start=list(attackR3_B=1.5,Th3_B=0.04),method="Nelder-Mead",data=dat) Error in dbinom(x, size, prob, log)

Dear R Help, I read through the archives pretty extensively before sending this email, as it seemed there were several threads on using predict with GLM. However, while my issue is similar to previous posts (cannot get it to predict using new data), none of the suggested fixes are working. The important bits of my code: set.seed(644) n0=200 #number of observations

Hi all, When I try to estimate the functional response of the Rogers type I equation (for the mle2 you need the package bbmle): > RogersIbinom <- function(N0,attackR2_B,u_B) {attackR2_B+u_B*N0} > RogersI_B <- mle2(FR~dbinom(size=N0,prob=RogersIbinom(N0,attackR2_B,u_B)/N0),start=list(attackR2_B=4.5,u_B=0.16),method="Nelder-Mead",data=data5) I get following error message

Hi all, I've been working on a backend of our architecture and noticed llvm performs following combining although one of operands is FrameIndex. Combining: t114: i64 = add FrameIndex:i64<0>, Constant:i64<56> Creating new node: t121: i64 = or FrameIndex:i64<0>, Constant:i64<56> ... into: t121: i64 = or FrameIndex:i64<0>, Constant:i64<56> This

Hello all, I would like to get parameter estimates for different models. For one of them I give the code in example. I am estimating the parameters (i,j and k) with the nls function, which sees the error distribution as normal, I would however like to do the same as nls with the assumption that the errors are poisson distributed. Is there a way to do this with R? Are there packages designed

I'm using the nls library to fit peaks in spectra. The model that I'm using is a quadratic background plus a Gaussian peak. The peak is well described by the Gaussian and the background is mostly linear with a bit of quadratic. My goal is an estimate of the integral (and the uncertainty of the integral) of the peak without the back ground. The procedure that I'm using is as

Dear list, I have the function (as a simple example, which is actually part of a larger function) pres.test<-function(N0=N0, N1=N1) { dt<-5 r<-log(N1/N0)/dt r } which calculates the annual growth rates in a population Where N0 is the population classified into age intervals, say 5 years, at time=1995, and N1 is the population by 5 year age classes at time=2000.

I've got a covariance matrix that I'd like to index using the dimnames: > vcov1 n0 x0 s n1 n2 n0 82.43824759 1.839505e-02 -4.975196e-01 2.882394e+03 -2.615986e-01 x0 0.01839505 6.134010e-03 -7.695922e-04 -6.373946e+01 6.086321e-03 s -0.49751964 -7.695922e-04 9.638943e-03 3.406594e+02 -3.173671e-02 n1 2882.39407745

Hi, I am new in R and stumbled on a problem my (more experienced) friends can not help with with. Why isnt this code working? The function is working, also with the loop and the graph appears, only when I build another loop around it (for different values of p) , R stays in a loop? Can't it take more then 2 loops in one program? powerb<-function(x,sp2,a,b,b1,m) {

After I spend some time working with the function attribute* deduction pass** [1,3], I would like to propose a "proper" organization***. Why? Because we do not derive nearly as many attributes as we could****, while we do maintain various (separate and diffently organized) "data-flow-like analyses" to do so. What else? I propose a single optimistic data-flow

First, let me say that I think the speex code is incredible in the way it supports floating and fixed point code from one set of code. The same is true for supporting multiple processors, etc... I've used speex with the PC, TI 64xx and 55xx. Please view the following comments not as an attack on speex (which I think is incredible!) but as my contribution to an open source project. I know

Please test if r115571 has fixed it. Evan On Oct 4, 2010, at 5:00 AM, Heikki Kultala wrote: > Bill Wendling wrote: >> On Sep 30, 2010, at 2:13 AM, Heikki Kultala wrote: >> >>> Bill Wendling wrote: >>>> On Sep 29, 2010, at 12:36 AM, Heikki Kultala wrote: >>>> >>>>> On 29 Sep 2010, at 06:25, Heikki Kultala wrote:

I am trying to define a general R function that has a function as the output that depends on the user's input arguments (this may make more sense by looking at the toy example below). My real use for this type of code is to allow a user to choose from many parameterizations of the same general model. My "issue" is that when I compile a package with this type of code in it I get a

Bill Wendling wrote: > On Sep 30, 2010, at 2:13 AM, Heikki Kultala wrote: > >> Bill Wendling wrote: >>> On Sep 29, 2010, at 12:36 AM, Heikki Kultala wrote: >>> >>>> On 29 Sep 2010, at 06:25, Heikki Kultala wrote: >>>> >>>>> Our architecture has 1-bit boolean predicate registers. >>>>> >>>>> I've