search for: reparameterized

...ich creates the following please? I understand the first part. b=g1(?) does what? dd <- data.frame(a = gl(3,4), b = gl(4,1,12)) # balanced 2-way dd a b 1 1 1 2 1 2 3 1 3 4 1 4 5 2 1 6 2 2 7 2 3 8 2 4 9 3 1 10 3 2 11 3 3 12 3 4 I am using the tree dataset in R. I want to form a reparameterized design matrix in ones, zeroes and minus ones. The dataframe dd is very important here. Can anyone assist here? Thanks in advance. Mary A. Marion [[alternative HTML version deleted]]

Dear All, I am trying to specify the following fixed effects model for lme: y ~ constant1 - beta1*(x - beta2) where y is the response, x is the independent variable, and the operators above are real arithmetic operations of addition, subtraction, and multiplication. I realize that this model is just a reparameterization of y=beta0+beta1*x, but I am using this parameterization because I am

Dear All, A couple of questions about the nls package. 1. I'm trying to run a nonlinear least squares regression but the routine gives me the following error message: step factor 0.000488281 reduced below `minFactor' of 0.000976563 even though I previously wrote the following command: nls.control(minFactor = 1/4096), which should set the minFactor to a lower level than the default

Hi all; I'm trying to fit a reparameterization of the assymptotic regression model as that shown in Ratkowsky (1990) page 96. Y~y1+(((y2-y1)*(1-((y2-y3)/(y3-y1))^(2*(X-x1)/(x2-x1))))/(1-((y2-y3)/(y3-y1))^2)) where y1,y2,y3 are expected-values for X=x1, X=x2, and X=average(x1,x2), respectively. I tried first with Statistica v7 by LS and Gauss-Newton algorithm without success (no

...*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) Dose -0.324 My problem is that I need to find the lower limit on the dose that causes a 10% effect. I can get the dose that causes a 10% effect, but getting the lower-limit is not straightforward. Thus, I have reparameterized the model in terms of this dosage and want to re-fit. The reparameterized model is: Log(E(Eggs)) = A - (B/A*0.1)*Dose where E(Eggs) is the expected value from the Poisson distribution, A is the intercept and B is the dose causing a 10% reduction. Is it possible to directly fit A and B in this ca...

Hi,I know only mle function but it seems that in mle one can only specify the bound of the unknowns forming the likelihood function. But I would like to specify something like, a = 2b or a <= 2b where 'a' and 'b' could be my parameters in the likelihood function. Any help would be really appreciated. Thank you!- adschai [[alternative HTML version deleted]]

Dear R users: I am using the package coda (the last verison in CRAN) to analyse the output from a MCMC Bayesian analysis. And I get unconsitented results. I have export the chain using the read.table function and after I have transformed this data frame to an mcmc object using the mcmc function. I am interested in three variables, when I use the function effectiveSize I have these figures:

Hi, I need help regarding estimating a linear model where restrictions are imposed on the coefficients. An example is as follows: Y_{t+2}=a1Y_{t+1} + a2 Y_t + b x_t + e_t restriction a1+ a2 =1 Is there a function or a package that can estimate the coefficient of a model like this? I want to estimate the coefficients rather than test them. Thank you for your help Ahmad Abu Hammour --------------

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 all, Looking at the body for the function rnorm, I see that the body of the function is: .Call(C_rnorm, n, mean, sd) I want to implement functions that generate normal (and other) random variables. Now, I understand that I can perfectly well just call the R wrapper for these functions and that will be almost indistinguishable for most purposes, but for whatever reason I wanted to try and

Gabriel, Thanks for that! I guess I really should have figured that one out sooner, huh? I understand why that wouldn't be CRAN-compliant. But then, what *is* the proper way to do it? Is there any way I can call unexported functions from another package and have it accepted by CRAN? Also, if I instead re-write the random variable generating functions, do you have any idea of where the

Greets, I'm trying to use nlminb() to estimate the parameters of a bivariate normal sample and during one of the iterations it passes a parameter vector to the likelihood function resulting in an invalid covariance matrix that causes dmvnorm() to throw an error. Thus, it seems I need to somehow communicate to nlminb() that the final three parameters in my parameter vector are used to

I am trying to fit a rank-frequency distribution with 3 unknowns (a, b and k) to a set of data. This is my data set: y <- c(37047647,27083970,23944887,22536157,20133224, 20088720,18774883,18415648,17103717,13580739,12350767, 8682289,7496355,7248810,7022120,6396495,6262477,6005496, 5065887,4594147,2853307,2745322,454572,448397,275136,268771) and this is the fit I'm trying to do: nlsfit

Many thanks to those took time replied to my question. They were very helpful and I solved my problem by reparameterization. With the help of optim() the fitting procedure is very robust and insensitive to initial starting value. Once again, many thanks. Kevin -------- Original post --------- >Dear R and S users, > >I've been working on fitting finite mixture of negative

Hi, I have a scalar valued function with several variables. One of the variables is restricted to be non-negative. For example, f(x,y)=sqrt(x)*exp(y), then x should be non-negative. I need the gradient and hessian for some vector (0,y), i.e., I need the gradient and hessian at the boudary of parameter space. The "numderiv" package does not work, even for f(x)=sqrt(x), if you do

Hi Guys, Does anyone know how I could constrain my regression coefficients so that they are positive and add up to one? Any help will be greatly appreciated. Kind Regards, Andre [[alternative HTML version deleted]]

I am trying to optimize a likelihood function using constrOptim. I know from prior research that, e.g. x1>x2. Is there a way to include that constraint into the optimization routine, i.e. the ci constraint? The examples I found only use absolute numeric values for the constraint and not relative values. My attempts to include it into ci failed: e.g. ci=c(1, x[1]). Am I using the

The multiple exponential problem you are attempting has a well-known and long history. Lanczos 1956 book showed that changing the 4th decimal in a data set changes the parameters hugely. Nevertheless, if you just need a "fit" and not reliable paramters, you could reparameterize to k1 and k2diff=k2-k1, so k2=k1+kdiff. Then kdiff has a lower bound of 0, though putting 0 will almost

...s now, and figured it's time to get some help. I want to integrate(f(x), lower=-Inf, upper=Inf) with f(x) = ((gamma(K+1)/(gamma(r+1)*gamma(K-r+1)))*(q(x)^r)*(((1-q(x))^(K-r))*phi(x), where phi(x) is the standard normal pdf, and q(x) the logistic CDF (with inverse scale parameter, so a little reparameterized). In short: it's a compound binomial probability. It can be shown that this integral can be expressed as a sum of moments of the System-Bounded Johnson distribution, for which it is proven that no analytical expressions exist. Moreover, it can be written as an infinite Taylor series in terms of...

Hi there are some predefined self-start functions, like SSmicmen, SSbiexp, SSasymp, SSasympOff, SSasympOrig, SSgompertz, SSflp, SSlogis, SSweibull, Quadratic, Qubic, SSexp (nlrwr) Btw, do you know graphic examples for this functions? The SSexpDecay (exponential decay) for y = (y0 - plateau)*exp(-k*x) + plateau from