similar to: Need help to understand integrate function

Hi, I have great problems with my work in R. I look for to model the growth of fish. I have "Longitudinal data", a serie of repeated measures for each individual. Using the corresponding packages "nlme" in R. I treat to fit to the data different growth functions, wich were entered by me. Unfortunately for no it was arrived at the convergence, several error messages appeared. I

Hello, I'm having problems creating and using a selfStart model with nlme. Briefly, I've defined the model, a selfStart object, and then combined them to make a selfStart.default model. If I apply getInitial to the selfStart model, I get results. However, if I try usint it with nls or nlsList, these routines complain about a lack of initial conditions. If someone could point out

On Sat, Nov 21, 2015 at 9:28 AM, Rui Ueyama <ruiu at google.com> wrote: > On Fri, Nov 20, 2015 at 10:13 PM, Simon Atanasyan <simon at atanasyan.com> > wrote: >> >> In case of MIPS O32 ABI we have to find a matching R_MIPS_LO16 >> relocation to calculate R_MIPS_HI16 one because R_MIPS_HI16 uses >> combined addend (AHI << 16) + (short)ALO where AHI is

Hello. I'm having problems at instruction selection with my back end with the following basic-block due to a vector add with immediate constant vector (obtained by vectorizing a simple C program doing vector sum map): vector.ph: ; preds = %vector.memcheck50 %.splatinsert = insertelement <8 x i64> undef, i64 %i.07.unr, i32 0

Hi, I am having some issues with how some of the instructions are being legalized. So this is my intial basic block. The area of concern is the last three instructions. I will pick and choose debug output to keep this small. SelectionDAG has 36 nodes: t0: ch = EntryToken t6: i32,ch = CopyFromReg t0, Register:i32 %vreg507 t2: i32,ch = CopyFromReg t0, Register:i32 %vreg17

I'm attempting to create an array of treatment comparisons for modelling data generation. This involves comparison of one treatment (c2) with another (c3), relative to a common comparator (c1). Attached code gives me the correct array but need to remove duplicates. Duplicates relate only to c2 and c3 such that I need to remove r3 because c2 and c3 are same as r1 with c2 and c3 swapped r5

Hi all, Our target does not have native support for 64-bit integers, so we rely on library calls for certain operations (like sdiv). We recently ran into a problem where these operations that are expanded to library calls aren't maintaining the proper ordering in relation to other chains in the DAG. The following snippet of a DAG demonstrates the problem. t0: ch = EntryToken t2:

Is there any way to get it to delay this optimization where it goes from this: Initial selection DAG: BB#0 'bclr64:entry' SelectionDAG has 14 nodes: t0: ch = EntryToken t2: i64,ch = CopyFromReg t0, Register:i64 %vreg0 t4: i64,ch = CopyFromReg t0, Register:i64 %vreg1 t6: i64 = sub t4, Constant:i64<1> t7: i64 = shl Constant:i64<1>, t6

But isn't kinda silly that we transform to xor and then we transform it back. What is the advantage in doing so? Also, since we do that method, I now have to introduce setcc patterns for i1 values, instead of being able to just use logical pattern operators like not. -Dilan On Fri, Jul 21, 2017 at 11:00 AM Dilan Manatunga <manatunga at gmail.com> wrote: > For some reason I

Hello, I am not entirely sure the subject line captures what I am trying to do, but hopefully this description of the problem will help folks to see my challenge and hopefully offer constructive assistance. I have an experimental setup where I measure the decrease in oxygen in small vials as an organism, such as an oyster, consumes the oxygen. Each vial is calibrated before the experiment and

I'm trying to use nls() to fit an exponential decay with an unknown offset in the time (independent variable). (Perhaps this is inherently very difficult?). > decay.pl <- nls (amp ~ expn(b0,b1,tau,t0,t), data = decay, + start = c(b0=1, b1=7.5, tau=3.5, t0=0.1), trace=T) Error in nlsModel(formula, mf, start) : singular gradient matrix at initial parameter estimates

I was surprised when seq.POSIXt() returned a single value rather than a vector, from inputs that I thought were reasonable. Here's an example to illustrate: > t0 <- ISOdatetime(2002,6,24,0,0,10) ## expected a sequence of 16 times 1 second apart > seq.POSIXt(from=t0,to=t0+15,by='1 sec') [1] "2002-06-24 00:00:10 PDT" ## traces to this call >

On Sat, Nov 21, 2015 at 10:08 AM, Rui Ueyama <ruiu at google.com> wrote: > On Fri, Nov 20, 2015 at 11:02 PM, Simon Atanasyan <simon at atanasyan.com> > wrote: >> >> On Sat, Nov 21, 2015 at 9:28 AM, Rui Ueyama <ruiu at google.com> wrote: >> > On Fri, Nov 20, 2015 at 10:13 PM, Simon Atanasyan <simon at atanasyan.com> >> > wrote: >>

Setting the ISD::ROTL to Expand doesn't work? (via SetOperation) You could also do a Custom hook if that's what you're looking for. On Thu, Nov 3, 2016 at 5:12 PM, Phil Tomson <phil.a.tomson at gmail.com> wrote: > ... or perhaps to rephrase: > > In 3.9 it seems to be doing a smaller combine much sooner, whereas in 3.6 > it deferred that till later in the

Dear R-list members, Some months ago I wrote a message on the usage of gnls (nlme library) and here I come again. Let me give an example: I have a 10 year length-at-age data set of 10 fishes (see growth.dat at the end of this message) and I want to fit a von Bertalanffy growth model, Li= Linf*(1-exp(-k*(ti-t0))) where Li = length at age i, Linf= asymptotic length, k= curvature parameter, ti=

Hello - I am having a problem with the function 'integrate'. I am running R on OSX (R 2.5.1). I am trying to suppress the error message when 'integrate' attempts to integrate across a parameter set giving a non-finite function value. I'm using it in a MCMC / simulated annealing algorithm, and it is entirely possible that some parameter sets will give non-

Dear R-Help list, I have a nonlinear least squares problem, which involves a changepoint; at the beginning, the outcome y is constant, and after a delay, t0, y follows a biexponential decay. I log-transform the data, to stabilize the error variance. At time t < t0, my model is log(y_i)=log(exp(a0)+exp(b0)) at time t >= t0, the model is log(y_i)=log(exp(a0-a1*(t_i - t0))+exp(b0=b1*(t_i -

Hi! I have some questions about R function. I try to write a function for multi-returns. The function code is as attachment. dgp.par<-function(ai, bi, t, n) { t0<-t+20 y0<-matrix(0, nr=t0, nc=n) y0[1,]<-ai/(1-bi) for(tt in 2:t0) { y0[tt,]<-ai+bi*y0[tt-1,]+rnorm(n, 0, 1) } y<-y0[21:t0,] x<-y0[20:t0-1,] z<-y0[19:t0-2,] z<-z[2:t,] dy<-y[2:t,]-y[1:t-1,]

One option may be to prevent the formation of ROTL, if possible, and then generating rol by hand. Marking it as "expand" would likely stop the DAG combiner from creating it. Then you could "preprocess" the selection DAG before the instruction selection and do the pattern matching yourself. -Krzysztof On 11/3/2016 4:24 PM, Phil Tomson via llvm-dev wrote: > I could try

POSIXct/lt supports fractional seconds (see Sub-second Accuracy section of man page), but there seem to be some inconsistencies in their handling. Converting to POSIXlt and back does not give back the same time for times before the origin: > t0 <- as.POSIXct('1934-01-05 23:59:59.00001') > t0 [1] "1934-01-06 00:00:00 EST" # rounding issue, see below >