similar to: Collinearity in nls problem

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Dear All, I'm having just a little terminology problem, relating the language used in the Hosmer and Lemeshow text on Applied Survival Analysis to that of the help that comes with the survival package. I am trying to back out the values for the baseline hazard, h_o(t_i), for each event time or observation time. Now survfit(fit)$surv gives me the value of the survival function, S(t_i|X_i,B),

R-help, I'm trying to estimate some parameters using the Maximum Likehood method. The model describes fish growth using a sigmoidal-type of curve: fn_w <- function(params) { Winf <- params[1] k <- params[2] t0 <- params[3] b <- params[4] sigma <- params[5] what <- Winf * (1-exp(- k *(tt - t0)))^b

Hello. I have searched the archives but have not found anything. I need to solve a constrained optimisation problem for a nonlinear function (“maximum entropy formalism”). Specifically, Optimise: -1*SUM(p_ilog(p_i)) for a vector p_i of probabilities, conditional on a series of constraints of the form: SUM(T_i*p_i)=k_i for given values of T_i and k_i (these are constraints on

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

Hi,   I have a system in which I analyze 2 subjects and 1 variable, so I have 2 models as follow:   y ~ x_1[, 1] + x_2[, 1] + x_1[, 2] + x_2[, 2]   Where   x_1[, i] = cos(2 * pi * t / T_i) x_2[, i] = sin(2 * pi * t / T_i)   i = 1, 2   Data have two columns: t and y.   As you can see, I have a multiple components model, with rithm and without trends, and I have a fundamental

Hello Tyler, I rephrase my previous mail, as follows: In your example, T_i = X1:X2:X3. Let F_j = X3. (The numerical variables X1 and X2 are not encoded at all.) Then T_{i(j)} = X1:X2, which in the example is dropped from the model. Hence the X3 in T_i must be encoded by dummy variables, as indeed it is. Arie On Thu, Nov 2, 2017 at 4:11 PM, Tyler <tylermw at gmail.com> wrote: > Hi

I will have data in the following form: Time resp type stim type 300 a A 200 b A 155 a B 250 b B 80 c A 1000 d B ... c is left censored observation; d is right censored This sort of problem is discussed in Chap 9 of Cox & Oakes Analysis of Survival Data under the name

Hello Tyler, You write that you understand what I am saying. However, I am now at loss about what exactly is the problem with the behavior of R. Here is a script which reproduces your experiments with three variables (excluding the full model): m=expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C")) model.matrix(~(X1+X2+X3)^3-X1:X3,data=m)

I finally got (mostly) what I wanted. In an attempt to figure out how to get nls to deal with a non-differentiable function, I had (stupidly) 'simplified' the problem until it became singular. Can I do something to make optim() less sensitive to my initial guess? For this example, I get a lousy solution if I make the initial guess for t0 = min(t) = 0.05. Thanks again, -- Robert Merithew

Hello Tyler, Thank you for searching for, and finding, the basic description of the behavior of R in this matter. I think your example is in agreement with the book. But let me first note the following. You write: "F_j refers to a factor (variable) in a model and not a categorical factor". However: "a factor is a vector object used to specify a discrete classification"

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