similar to: weighted nonlinear fits: `nls' and `eval'

dear list, my question concerns the use of `eval' in defining the model formula for `nls' (version 2.6.2.). consider the following simple example, where the same model and data are used to perform unweighted and weighted fits. I intentionally used very uneven weights to guarantee large differences in the results #================================CUT=========================== ln

Are there any implicit assumptions about where alloca instructions can appear. I've got a failing test where the only difference between a passing test and a failing test is one application of this code in instcombine: // Convert: malloc Ty, C - where C is a constant != 1 into: malloc [C x Ty], 1 Seems pretty harmless to me. Later on the instcombine code does this: // Scan to the end of

Hello everyone! I am currently working with a time series panel data set measuring six dependent variables: 4 of which are binary and 2 of which are count data. I am interested in constructing a model to measure if the dependent variables influence one another. For example: DV1~ DV2 + IV1+IV2+ Controls and DV2~ DV1 + IV1+ IV2+ Controls (where IV stands for independent variable, not

Dear all, I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from 'nls'): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) This is the unweighted fit, in the code of 'nls' one can see that 'nls' generates a vector

Hi, Sorry to bother you again. I would like to estimate the effect of several categorical factors (two between subjects and one within subjects) on two continuous dependent variables that probably covary, with subjects as a random effect. *I want to control for the covariance between those two DVs when estimating the effects of the categorical predictors** on those two DVs*. The thing is, i

# I've tried to make this easy to paste into R, though it's probably so simple you won't need to. # I have some data (there are many more variables, but this is a reasonable approximation of it) # here's a fabricated data frame that is similar in form to mine: my.df <- data.frame(replicate(10, round(rnorm(100, mean=3.5, sd=1)))) var.list <- c("dv1",

Does anyone know if there is a way to run multivariate linear regression in R? I tried using the lm function (e.g., lm(dv1, dv2~iv1+iv2+iv3), but got error messages. Is my syntax wrong, or do I need a particular package? Thanks, Jeff-- ________________________________________________________ Jeffrey J. Lusk, Ph.D. Postdoctoral Research Associate Department of Forestry &

Hello all, Is there an r function that exists that will perform repeated measures MANOVAs? For example, let's say I have 3 DVs, one between-subjects IV, and one within-subjects IV. Based on the documentation for the manova command, a function like that below is not appropriate because it cannot take Error arguments. manova(cbind(DV1,DV2,DV3) ~ BetweenSubjectsIV * WithinSubjectsIV +

Hi, I have been using R to do multiple analyses of variance with two covariates, but recently found that the results in SPSS were very different. I have check several books and web resources and I think that both methods are correct, but I am less familiar with R, so I was hoping someone could offer some suggestions. Oddly simple ANOVA is the same in SPSS and R. Including covariates improves the

Hello there, Suppose you want to solve the following system of ODE's (a simple Lotka-Volterra predator prey model) dP/dt = beta*P*V - mu*P dV/dt = r*V - beta*P*V where P and V are the numbers of predators and prey. Now, this is easy to do, but suppose you have a system of equations like this, dP1/dt = beta1*P1*V1 - mu1*P1 dP2/dt = beta2*P2*V2 - mu2*P2 dV1/dt = r1*V1 - beta1*P1*V1

Dear R-users, I am trying to create a model using the NLS function, such that: Y = f(X) + q + e Where f is a nonlinear (Weibull: a*(1-exp(-b*X^c)) function of X and q is a covariate (continous variable) and e is an error term. I know that you can create multiple nonlinear regressions where x is polynomial for example, but is it possible to do this kind of thing when x is a function with unknown

for unweighted fits using `nls' I compute confidence intervals for the fitted model function by using: #------------------- se.fit <- sqrt(apply(rr$m$gradient(), 1, function(x) sum(vcov(rr)*outer(x,x)))) luconf <- yfit + outer(se.fit, qnorm(c(probex, 1 - probex))) #------------------- where `rr' contains an `nls' object, `x' is the independent variable vector, `yfit'

The following warning message occurs when running the nls on some data: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Warning message: In is.na(wts) : is.na() applied to non-(list or vector) of type 'NULL' ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ and I would like to know what is causing it and how I can fix it. As an example, the following, from Venables and Ripley, which

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

Hi, This is more of a general question than a pure R one, but I hope that is OK. I want to combine one-tailed independent p-values using the weighted version of fisher's inverse chi-square method. The unweighted version is pretty straightforward to implement. If x is a vector with p-values, then I guess that this will do for the unweighted version: statistic <- -2*sum(log(x)) comb.p <-

Hello, nls library provides 6 self-starting models, among them: SSfp, a four parameters logistic function. Its self-starting procedure involves several steps. One of these steps is: pars <- as.vector(coef(nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xydata, start = list(lscal = 0), algorithm = "plinear"))) which assumes an initial value of lscal equal to 0. If lscal

Greetings, I'm having trouble finding a simple way to calculate a weighted histogram where there may be zero raw counts in a given interval. Given equal-length vectors of data 'data' and weights 'w', and breaks (intervals) for the histogram, I calculate a weighted histogram as follows (see MASS's 'truehist' for an unweighted histogram): bin <- cut(data,

I'm trying to plot and compare weighted histograms and I can't seem find where to start. I have data similar to this: Miles2LAX RADAM2005Pct LAWA2005Pct 35.57 .00123 .00684 24.74 .00118 .00187 27.09 .00965 .00876 16.23 .00587 .00397 { ...