search for: varconstpow

...lpful that you included the R code to replicate your analysis; however, I have had a problem replicating your example and wondered if you are able to give me a hint. When I use the code from the article: lme1 <- lme(Y1 ~ trt.B + trt.C + trt.D + trt.E, random = ~ 1 | trtpair, data=lumley1, var = varConstPower(form=~sigma, fixed=list(power=1))) I get an error message: Error in lme(Y1 ~ trt.B + trt.C + trt.D + trt.E, random = ~1 | trtpair, : unused argument(s) (var = list(const = numeric(0), power = numeric(0))) The problem seems to be in the varConstPower component, but I don't understand ex...

...for now. However, aN and aL values don't seem to vary w/in a Type or between Types. As a result, I would like a mixed effects model using nlme. Further, looking at the residuals, I find that they are heteroscedastic. As a result, I would like to try and model the variance in the data using varConstPowerFun within nlme. I've been trying to understand how to use this option by reading Pinheiro and Bates's book on mixed effects models. Based on this, I've tried using the syntax, --------------------------------------------- > nlme(Count ~ quad.PBMC.model(aL, aN, T0), + data =...

...tructures by using fixed effects variables in combination with the VarPower function. That is, I would like to create and evaluate my own variance function in the data frame and then incorporate it into the model using varPower, with value=.5. As a start, I am trying to recreate the function of VarConstPower by introducing two new variables in the data frame, d1 and d2. I am using a self-made function, fx, which contains a logistic equation. It all works just fine in combination with the built-in varConstPower variance structure. I try to mimic the varConstPower structure by using the varPower va...

I have been using gnls with the weights argument (and varConstPower) to specify a variance structure for curve fits. In attempting to extract the parameters for the variance model I am seeing results I don't understand. When I simply display the model (or use "summary" on the model), I get what seem like reasonable values for both "power"...

...Original Message----- From: Spencer Graves [mailto:spencer.graves at pdf.com] Sent: Sunday, January 15, 2006 6:41 PM To: Rand, Hugh Cc: 'r-help at lists.R-project.org' Subject: Re: [R] trouble with extraction/interpretation of variance structure para meters from a model built using gnls and varConstPower How about this: > exp(coef(model3$modelStruct$varStruct)["const"]) const 0.6551298 Does that answer the question about not understanding the connection between summary(model3) and coef(model3$modelStruct$varStruct)["const"]? Regarding the question about...

Dear R-Users, I have a question concerning extraction of parameter estimates of variance function from lme fit. To fit my simulated data, we use varConstPower ( constant plus power variance function). fm<-lme(UPDRS~time,data=data.simula,random=~time,method="ML",weights=varConstPower(fixed=list(power=1))) I extract the results of this function by using the following codes: y<-summary(fm) x<-y$modelStruct$varStruct > x Variance fu...

...sympOff, CO2)) (fm1CO2.nlme <- nlme(fm1CO2.lis, control = list(tolerance = 1e-2))) (fm2CO2.nlme <- update(fm1CO2.nlme, random = Asym + lrc ~ 1)) CO2.nlme.var <- update(fm2CO2.nlme, fixed = list(Asym ~ Type * Treatment, lrc + c0 ~ 1), start = c(32.412, 0, 0, 0, -4.5603, 49.344), weights=varConstPower(fixed=list(const=0.1, power=1)), verbose=T) CO2.nlme.CAR<-update(CO2.nlme.var, corr=corCAR1()) CO2.nlme.gauss<-update(CO2.nlme.var, correlation=corGaus(form=~as.numeric(conc)|Plant,nugget=F), data=CO2) CO2.nlme.exp<-update(CO2.nlme.var, correlation=corExp(form=~as.numeric(conc)|Plan...

R 1.6.2 for Windows, Win2k: I have fitted a weighted least squares model using the code "wls.out <- gls(y ~ x1 + x2 + x3 + x4 + x5 + x6 - 1, data = foo.frame, weights = varConstPower(form = ~ fitted(.), fixed = list(power = 0.5), const = 1))" The data has 62 rows and the response is zero when the covariates are zero. The purpose of the model was to account for the the fact that the variances appear to increase linearly with the fitted values in diagnostic plots. When...

...llArea) # (Intercept) Residual # StdDev: 0.1332918 1 # # Variance function: # Structure: fixed weights # Formula: ~var # Number of Observations: 43 # Number of Groups: 43 #Fay-Herriot model fitted using the variance function varSum defined above #A columns of zeros is added to tweak the varConstPower component milk$zero<-0 FH2<-gls(yi ~ as.factor(MajorArea),data=milk, weights=varSum(varConstPower(1,1,~zero,fixed=list(power=1)),varFixed(~var)), control=lmeControl(sigma = 1)) FH2 # Generalized least squares fit by REML # Model: yi ~ as.factor(MajorArea) # Data: milk # Log-restricted-l...

...nable fit of all of the groups to the observed data using an exponential decay model (a*exp-x*b).  The problem is that when I plot the fitted values to residuals, it demonstrates a pattern of increasing variance.   I attempt to model this variance using   Sig3.nlme <- update(Sig2.nlme, weights = varConstPower(power=0.1),                    verbose = TRUE)   I get the message that 'step halving factor reduced below minimum in PNLS step'.  Some of the curves are actually flat lines where there is a value of 0 for the response at every observation within the group.  My thought is that, because th...

Hi everybody, I'm interested in evaluating the effect of a continuous variable on the mean and/or the variance of my response variable. I have built functions expliciting these and used the 'mle2' function to estimate the coefficients, as follows: func.1 <- function(m=62.9, c0=8.84, c1=-1.6) { s <- c0+c1*(x) -sum(dnorm(y, mean=m, sd=s,log=T)) } m1 <- mle2(func.1,

  Hola,   Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?   http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5   Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.   Muchas gracias.   Eva [[alternative HTML

Dear All, I'm trying to model heteroscedasticity using a multilevel model. To do so, I make use of the nlme package and the weigths-parameter. Let's say that I hypothesize that the exam score of students (normexam) is influenced by their score on a standardized LR test (standLRT). Students are of course nested in "schools". These variables are contained in the