Question

我希望在已知直径和物种的映射树图中优化模型的拟合，该模型描述在0.5m ^ 2'垃圾陷阱网络中收集的垃圾量。选择模型有两个因素，即枯枝落叶产生的异速生长，以及凋落物行程距离的指数衰减。

tree1.litter = alpha*gamma^2 * DBH^Beta/(2*pi)  *  exp(-gamma*z-delta*DBH)

但是，我们的陷阱数据包含来自多个树的输入（这是标题中提到的“缺失级别”）：

Obs.Litter = tree1.litter + tree2.litter + ... + treej.litter + error

到目前为止，甚至模拟数据的结果都非常复杂。似乎有足够的直径和距离组合，功能应该受到一定程度的约束。这个分析是在我正在复制的文章中进行的。我也尝试过对日志（Obs.Litter）的分析，我认为这是要走的路。但我不确定我对日志版本进行编码的方式会产生一些你期望执行得更好的东西。

在这一点上，我想我只是寻找任何一种建议（基于代码或概念），这些建议来自于对这种“隐藏过程”拟合非线性回归或模型拟合问题更有经验的人。下面包括数据模拟代码和各种可能性。在OpenBUGS中使用贝叶斯分层模型估算这些参数时，我已经获得了位更多的成功，只有信息丰富的先验。

library(plyr)
########################
##Generate Data#########
########################

alpha = 5
Beta = 2
gamma = .2
delta = .02

n = 600 #Number of trees
N.trap = 45 #Number of litter traps

D = rlnorm(n, 2)+5  #generate diameters
Z = runif(n, 0, 25) #generate distances
trap.id = sort(sample(1:N.trap, size = n, replace = T)) #assign trees to traps
tree.lit = (2*pi)^-1*alpha*gamma^2*D^Beta  *  exp(-gamma*Z-delta*D) #generate litter
log.tree.lit = -(2*pi) + log(alpha) + 2*log(gamma) + Beta*DBH -gamma*Z - delta*D

litter = data.frame(D=D, Z = Z, trap.id = trap.id, tree.lit = tree.lit)
data = ddply(litter, .(trap.id), summarize, trap.lit = sum(tree.lit), n.trees=length(trap.id) )

trap.lit = data[,2]

#####################################
##### Maximum Likelihood Optimization
#####################################
library(bbmle)

log.Litter.Func<-function(alpha, Beta, gamma, delta, sigma, D, Z, N.trap, trap.id,     Obs.Litter){

    log.Expected.Litter.tree = -log(2*pi) + log(alpha) + 2*log(gamma) + Beta*log(D) -gamma*Z - delta*D
    log.Expected.Litter.trap = rep(0, N.trap)
    for(i in 1:N.trap){
        log.Expected.Litter.trap[i] <-     sum(exp(log.Expected.Litter.tree[trap.id==i]))
        }
    -sum(dlnorm(log(Obs.Litter), log.Expected.Litter.trap, sd=sigma, log=T))
    }


Litter.Func<-function(alpha, Beta, gamma, delta, sigma, D, Z, N.trap, trap.id,     Obs.Litter){

    Expected.Litter.tree = 1/(2*pi) * alpha * gamma^2 * D^Beta *exp(-gamma*Z - delta*D)
    Expected.Litter.trap = rep(0, N.trap)
        for(i in 1:N.trap){
            Expected.Litter.trap[i] <- sum(Expected.Litter.tree[trap.id==i])
            }
    -sum(dnorm(Obs.Litter, Expected.Litter.trap, sd=sigma, log=T))
    }


log.fit<-mle2(log.Litter.Func, 
start = list(alpha = 5,gamma = .2,Beta = 2,delta = .02, sigma = 1),
#upper = list(alpha = 20,gamma = 1,Beta = 4,delta = .2,sigma = 20),
#lower = list(alpha = .002,gamma = .002,Beta = .0002,delta = .000000002,sigma = .020),
#method="L-BFGS-B",
data=list(D=D, Z=Z, N.trap=N.trap, trap.id=litter$trap.id, Obs.Litter=trap.lit)
)

fit<-mle2(Litter.Func, 
start = list(alpha = 5,gamma = .2,Beta = 2,delta = .02, sigma = 1),
#upper = list(alpha = 20,gamma = 1,Beta = 4,delta = .2,sigma = 20),
#lower = list(alpha = .002,gamma = .002,Beta = .0002,delta = .000000002,sigma = .020),
#method="L-BFGS-B",
data=list(D = D,Z = Z,N.trap=N.trap,  trap.id=litter$trap.id,Obs.Litter = trap.lit)
)

拟合非线性函数＆＃34;缺少级别＆＃34;在mle2（警告：生态学家与计算机）

0 个答案: