search for: reparameterize

...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 c...

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]]

...ors in the diagnostic tests HINT: Look at plots first to identify variables with slow mixing. (Choose menu Output Analysis then Plots) Re-run your chain with a larger sample size and thinning interval. If possible, reparameterize your model to improve mixing ******************************************* Some thing is wrong. Could someone explain to me what is happening? Thanks, Juan Pablo. =========================== Juan Pablo S??nchez Serrano Dep. Ciencia Animal, UPV. C/ Camino de Vera s/n 46022 Valencia (Spain) Telf. 963...

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 --------------

...have identifiability issues, but this model seems to work fine except that the parameters t0 (the delay) is highly correlated with the initial decay slope a0 (which makes sense, as the longer the delay, the more rapid the drop has to be, conditional on the data). To get over this problem, I could reparameterize the problem, but it isn't clear to me how to do this for the above model. I also thought about using a penalized least square approach, to shrink t0 and a1 towards 0. I haven't seen much on penalized least squares in a nonlinear least squares setting; is this a good way to go? Can I justifi...

...the other functions, such as parameterizing rbeta by the mean and sample size rather than by the number of successes and failures and rgamma by the mean and total time elapsed instead of the number of events. Once I understand how the C source code works, it would be hopefully not very difficult to reparameterize them. Thanks, Luis Usier [[alternative HTML version deleted]]

...beta by the mean and sample size rather than by >> the >> number of successes and failures and rgamma by the mean and total time >> elapsed instead of the number of events. Once I understand how the C >> source >> code works, it would be hopefully not very difficult to reparameterize >> them. >> >> Thanks, >> >> Luis Usier >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-devel at r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-devel...

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 certainly get you into computational trouble. You probably want an upper bound on k1 too. The problem is discussed in the book I published with Mary Walker-Smith in 1987, but I think our treatmen...

...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 o...

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