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我已经使用 nlme 拟合了一个模型，但我认为残差看起来不够好，所以我正在尝试通过

fit.nlme6 <- update(fit.nlme5, weights = varPower())

我明白了Error: Singularity in backsolve at level 0, block 1。我尝试了其他我认为没有多大意义的课程，并尝试了各种forms = ~and fixed。所有这些都带有相同的错误消息，有时带有额外的警告消息。

这是残差。我认为varPower应该可以完美地工作，那为什么不呢？

残差：

更多信息，fit.nlme5是适合具有三个参数的 beta 生长函数的模型，w.max（最大生物量）、t.e（生长结束时刻）和t.m（最大生长时刻）。模型看起来像这样

fit.nlme5 <- update(fit.nlme4, fixed = list(t.e ~ trt + ground + trt:ground, w.max + t.m ~ 1),
                start = c(cfsTD[1:4], rep(0,2) ,cfsTD[5:6]))

在两个位置（地面）有三个处理（trt）。在残差固定后，我将运行一些对比来比较不同位置的处理。

这是数据https://dl.dropbox.com/u/21080842/cobsgddv8.txt

并且代码需要到达最终模型：

  #You'll need these functions
## Implementing the beta growth function from (Yin et al 2003)

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"))

#Now for the data and fitting
grow<-read.table("cobsgddv8.txt", header=T)

library(nlme)

grow10<-subset(grow, grow$year == "2010")
grow10$EU<- with(grow10, factor(ground):factor(plot))
grow10G<-groupedData(mass ~ gdd | EU, data=grow10)

fit.beta.10 <- nlsList(mass ~ SSbgf(gdd, w.max, t.e, t.m), data = grow10G)

fit.nlme.10<-nlme(fit.beta.10, random=pdDiag(w.max ~1))

cfs <- fixef(fit.nlme.10)
fit.nlme2 <- update(fit.nlme.10, fixed = list(t.e ~ trt, w.max + t.m ~ 1),
                    start = c(cfs[2], rep(0,2), cfs[c(1,3)]))

cfsT <- fixef(fit.nlme2)


fit.nlme3 <- update(fit.nlme.10, fixed = list(t.e ~ ground, w.max + t.m ~ 1),
                    start = c(cfs[2], rep(0,1), cfs[c(1,3)]))

cfsG <- fixef(fit.nlme3)

fit.nlme4 <- update(fit.nlme.10, fixed = list(t.e ~ trt + ground, w.max + t.m ~ 1),
                    start = c(cfsT[1:2], cfsG[1:2], cfs[c(1,3)]))

cfsTD <- fixef(fit.nlme4)

fit.nlme5 <- update(fit.nlme4, fixed = list(t.e ~ trt + ground + trt:ground, w.max +     
t.m ~ 1),
                    start = c(cfsTD[1:4], rep(0,2) ,cfsTD[5:6]))


fit.nlme6 <- update(fit.nlme5, weights = varPower())
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1 回答 1

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正如克里斯所说，这还不够信息。由于这么多点接近于零，你应该给

varConstPower(power=0.5,fixed=list(const=10))

尝试一下，并使用偏移量。

于 2012-08-17T17:50:21.223 回答