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Piet van den Berg
Piet van den Berg
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I would like to obtain 95% confidence intervals on the predictions of a non-linear mixed nlme model. As nothing standard is provided to do this within nlme, I was wondering if it is correct to use the method of "population prediction intervals", as outlined in Ben Bolker's book chapter in the context of models fit with maximum likelihood, based on the idea of resampling fixed effect parameters based on the variance-covariance matrix of the fitted model, simulating predictions based on this, and then taking the 95% percentiles of these predictions to get the 95% confidence intervals?

Is this approach valid, or are there any other or better approaches to calculate 95% confidence intervals on the predictions of a nonlinear mixed model? I am not entirely sure of how to deal with the random effect stucture of model... Should one average perhaps over random effect levels? Or would it be OK to have confidence intervals for an average subject, which would seem to be closer to what I have now?

I would like to obtain 95% confidence intervals on the predictions of a non-linear mixed nlme model. As nothing standard is provided to do this within nlme, I was wondering if it is correct to use the method of "population prediction intervals", as outlined in Ben Bolker's book chapter, based on the idea of resampling fixed effect parameters based on the variance-covariance matrix of the fitted model, simulating predictions based on this, and then taking the 95% percentiles of these predictions to get the 95% confidence intervals?

Is this approach valid, or are there any other or better approaches to calculate 95% confidence intervals on the predictions of a nonlinear mixed model?

I would like to obtain 95% confidence intervals on the predictions of a non-linear mixed nlme model. As nothing standard is provided to do this within nlme, I was wondering if it is correct to use the method of "population prediction intervals", as outlined in Ben Bolker's book chapter in the context of models fit with maximum likelihood, based on the idea of resampling fixed effect parameters based on the variance-covariance matrix of the fitted model, simulating predictions based on this, and then taking the 95% percentiles of these predictions to get the 95% confidence intervals?

Is this approach valid, or are there any other or better approaches to calculate 95% confidence intervals on the predictions of a nonlinear mixed model? I am not entirely sure of how to deal with the random effect stucture of model... Should one average perhaps over random effect levels? Or would it be OK to have confidence intervals for an average subject, which would seem to be closer to what I have now?

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Piet van den Berg
Piet van den Berg
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I'm tryingI would like to getobtain 95% confidence intervals on mythe predictions forof a non-linear mixed nlme model in. As nothing standard is provided to do this within nlme, usingI was wondering if it is correct to use the method of "population prediction intervals", as outlined in Ben Bolker's book chapter, based on the idea of resampling fixed effect parameters based on the variance-covariance matrix of parameter values. I got a result but would likethe fitted model, simulating predictions based on this, and then taking the 95% percentiles of these predictions to know if I'm going inget the right direction.95% confidence intervals?

IThe code to do this looks as follows : (I here use the 'Loblolly' data from the nlme help to illustrate what I have done.file)

Here's the plot with the 95% confidence intervals obtained this way:

All data (red lines), means and confidence limits (black lines)

Is this approach valid, or are there any other or better approaches to calculate 95% confidence intervals on the predictions of a nonlinear mixed model?

I'm trying to get confidence intervals on my predictions for a non-linear mixed model in nlme, using resampling of parameter values. I got a result but would like to know if I'm going in the right direction.

I here use the 'Loblolly' data from the nlme help to illustrate what I have done.

Here's the plot:

All data (red lines), means and confidence limits (black lines)

I would like to obtain 95% confidence intervals on the predictions of a non-linear mixed nlme model. As nothing standard is provided to do this within nlme, I was wondering if it is correct to use the method of "population prediction intervals", as outlined in Ben Bolker's book chapter, based on the idea of resampling fixed effect parameters based on the variance-covariance matrix of the fitted model, simulating predictions based on this, and then taking the 95% percentiles of these predictions to get the 95% confidence intervals?

The code to do this looks as follows : (I here use the 'Loblolly' data from the nlme help file)

Here's the plot with the 95% confidence intervals obtained this way:

All data (red lines), means and confidence limits (black lines)

Is this approach valid, or are there any other or better approaches to calculate 95% confidence intervals on the predictions of a nonlinear mixed model?

Source Link
Piet van den Berg
Piet van den Berg
  • 173
  • 1
  • 1
  • 6

Confidence intervals on predictions for a non-linear mixed model (nlme)

I'm trying to get confidence intervals on my predictions for a non-linear mixed model in nlme, using resampling of parameter values. I got a result but would like to know if I'm going in the right direction.

I here use the 'Loblolly' data from the nlme help to illustrate what I have done.

library(effects) library(nlme) library(MASS) fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc), data = Loblolly, fixed = Asym + R0 + lrc ~ 1, random = Asym ~ 1, start = c(Asym = 103, R0 = -8.5, lrc = -3.3)) xvals=seq(min(Loblolly$age),max(Loblolly$age),length.out=100) nresamp=1000 pars.picked = mvrnorm(nresamp, mu = fixef(fm1), Sigma = vcov(fm1)) # pick new parameter values by sampling from multivariate normal distribution based on fit yvals = matrix(0, nrow = nresamp, ncol = length(xvals)) for (i in 1:nresamp) { yvals[i,] = sapply(xvals,function (x) SSasymp(x,pars.picked[i,1], pars.picked[i,2], pars.picked[i,3])) } quant = function(col) quantile(col, c(0.025,0.975)) # 95% percentiles conflims = apply(yvals,2,quant) # 95% confidence intervals

Now that I have my confidence limits I create a graph:

meany = sapply(xvals,function (x) SSasymp(x,fixef(fm1)[[1]], fixef(fm1)[[2]], fixef(fm1)[[3]])) par(cex.axis = 2.0, cex.lab=2.0) plot(0, type='n', xlim=c(3,25), ylim=c(0,65), axes=F, xlab="age", ylab="height"); axis(1, at=c(3,1:5 * 5), labels=c(3,1:5 * 5)) axis(2, at=0:6 * 10, labels=0:6 * 10) for(i in 1:14) { data = subset(Loblolly, Loblolly$Seed == unique(Loblolly$Seed)[i]) lines(data$age, data$height, col = "red", lty=3) } lines(xvals,meany, lwd=3) lines(xvals,conflims[1,]) lines(xvals,conflims[2,])

Here's the plot:

All data (red lines), means and confidence limits (black lines)