Tricks for fitting data in nlme?

问题

When I fit data in nlme, I never succeed on the first try, and after nlme(fit.model) I am accustomed to seeing things such as:

Error in nlme.formula(model = mass ~ SSbgf(day, w.max, t.e, t.m), random = list( :
  step halving factor reduced below minimum in PNLS step

Error in MEestimate(nlmeSt, grpShrunk) : 
  Singularity in backsolve at level 0, block 1

So I go back and

1)Change the units of the x-axis (e.g. from years to days, or days to growing degree days).

2)Make a x=0, y=0 measurement in my dataset

3)Add a random=pdDiag()

4)Mess with what is random and what is fixed

5)Chop up my dataset and try to fit different parts at different times

6)Achieve a very simple fit, then use update to make the model proper

Eventually something seems to work. Does anyone else have something to add to this list? What helps you get nlme to work with your data?

I realize this question will probably be closed, but if there are any suggestions on how to reword it to be acceptable to SO, I would appreciate the input.

Here is an example where I have tried some of these things, but have not had success so far:

The data: https://www.dropbox.com/s/4inldx7617fip01/proots.csv . This is already just a portion of the whole set.

The code:

roots<-read.table("proots.csv", header = TRUE)

#roots$day[roots$year == 2007] <- 0 #when I use a dataset with time=0, mass=0
roots$day[roots$year == 2008] <- 153
roots$day[roots$year == 2009] <- 518
roots$day[roots$year == 2010] <- 883
roots$day[roots$year == 2011] <- 1248
roots$day[roots$year == 2012] <- 1613
roots$day[roots$year == 2013] <- 1978

#or bigger time steps
roots$time[roots$year == 2008] <- 1
roots$time[roots$year == 2009] <- 2
roots$time[roots$year == 2010] <- 3
roots$time[roots$year == 2011] <- 4
roots$time[roots$year == 2012] <- 5
roots$time[roots$year == 2013] <- 6

roots$EU<- with(roots, factor(plot):factor(depth)) #EU is "experimental unit"
rootsG<-groupedData(mass ~ day | EU, data=roots)

#I will post the SSbgf function below -- run it first
fit.beta <- nlsList(mass ~ SSbgf(day, w.max, t.e, t.m), data = rootsG) 

fit.nlme.bgf<-nlme(fit.beta)
fit.nlme.bgf<-nlme(fit.beta, random=list(w.max + t.e + t.m ~1))
fit.nlme.bgf<-nlme(fit.beta, random=list(w.max ~ 1))
fit.nlme.bgf<-nlme(fit.beta, random=pdDiag(w.max ~1))

fit.nlme.bgf<-nlme(fit.beta, random=pdDiag(w.max + t.e + t.m ~1))
fit.nlme.bgf<-nlme(fit.beta, random=list(t.m ~1)) 
fit.nlme.bgf<-nlme(fit.beta, random=list(t.e ~1))

fit.nlme.bgf<-nlme(fit.beta, random=pdSymm(w.max ~1))
fit.nlme.bgf<-nlme(fit.beta, random=pdDiag(w.max ~1))

And here is the function (SSbgf) for the curve:

bgfInit <- function(mCall, LHS, data){

  xy <- sortedXyData(mCall[["time"]], LHS, data)
  if(nrow(xy) < 4){
    stop("Too few distinct input values to fit a bgf")
  }
  w.max <- max(xy[,"y"])
  t.e <- NLSstClosestX(xy, w.max)
  t.m <- NLSstClosestX(xy, w.max/2)
  value <- c(w.max, t.e, t.m)
  names(value) <- mCall[c("w.max","t.e","t.m")]
  value

}


bgf <- function(time, w.max, t.e, t.m){

  .expr1 <- t.e / (t.e - t.m)
  .expr2 <- (time/t.e)^.expr1
  .expr3 <- (1 + (t.e - time)/(t.e - t.m))
  .value <- w.max * .expr3 * .expr2

  ## Derivative with respect to t.e
  .exp1 <- ((time/t.e)^(t.e/(t.e - t.m))) * ((t.e-time)/(t.e-t.m) + 1)
  .exp2 <- (log(time/t.e)*((1/(t.e-t.m) - (t.e/(t.e-t.m)^2) - (1/(t.e - t.m)))))*w.max
  .exp3 <- (time/t.e)^(t.e/(t.e-t.m))
  .exp4 <- w.max * ((1/(t.e-t.m)) - ((t.e - time)/(t.e-t.m)^2))
  .exp5 <- .exp1 * .exp2 + .exp3 * .exp4 

  ## Derivative with respect to t.m
  .ex1 <- t.e * (time/t.e)^((t.e/(t.e - t.m))) * log(time/t.e) * ((t.e - time)/(t.e -     
 t.m) + 1) * w.max
  .ex2 <- (t.e - time) * w.max * (time/t.e)^(t.e/(t.e-t.m))
  .ex3 <- (t.e - t.m)^2
  .ex4 <- .ex1 / .ex3 + .ex2 / .ex3

  .actualArgs <- as.list(match.call()[c("w.max", "t.e", "t.m")])

##  Gradient
  if (all(unlist(lapply(.actualArgs, is.name)))) {
    .grad <- array(0, c(length(.value), 3L), list(NULL, c("w.max", 
                                                      "t.e", "t.m")))
    .grad[, "w.max"] <- .expr3 * .expr2
    .grad[, "t.e"] <- .exp5
    .grad[, "t.m"] <- .ex4 
    dimnames(.grad) <- list(NULL, .actualArgs)
    attr(.value, "gradient") <- .grad
  }
    .value
}

SSbgf <- selfStart(bgf, initial = bgfInit, c("w.max", "t.e", "t.m"))

回答1:

Another trick is to increase the pnls tolerance.

The required code would be:

control = nlmeControl(pnlsTol = x, msVerbose = TRUE)

The starting value for pnls tolerance is 0.001, so I like to start with 0.01 or 0.02. Just replace x with your number and you should be set.

来源：https://stackoverflow.com/questions/22921047/tricks-for-fitting-data-in-nlme

标签

curve-fitting

nlme