search for: reparameterization

Hello, I want to create a design matrix using R. Can you explain the code which 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

...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 specifically interested in confidence bounds for beta2. I have looked at the help, but the closest hint I find is the I() function, and that does not seem to work this way. I confess that I am actually using S-plus, but...

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 Gaus...

Hello, I am fitting a Poisson mixed effects model. I have the number of eggs (Eggs) laid by a quail and looking at the effect of dosage of a chemical (Dose) in the study. I have counts of eggs laid by week of the study, so I am including the week number (Week) as a random effect. I'm using the lme4 package. I have, > mod1 <- lmer(Eggs~Dose + (1|Week),family=poisson) >

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 exponential >distributions us...

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

Dear All, thanks in advance for your attention. I'm struggling with this for a few weeks 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

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