我尝试使用nlme
和lsoda
拟合一阶微分模型。
这是基本思想:我首先定义允许生成微分方程解的函数:
library(deSolve)
ODE1 <- function(time, x, parms) {with(as.list(c(parms, x)), {
import <- excfunc(time)
dS <- import*k/tau - (S-yo)/tau
res <- c(dS)
list(res)})}
solution_ODE1 = function(tau1,k1,yo1,excitation,time){
excfunc <- approxfun(time, excitation, rule = 2)
parms <- c(tau = tau1, k = k1, yo = yo1, excfunc = excfunc)
xstart = c(S = yo1)
out <- lsoda(xstart, time, ODE1, parms)
return(out[,2])
}
然后我根据两个ID的公式生成数据:
time <- 0:49
excitation <- c(rep(0,10),rep(1,10),rep(0,10),rep(1,10),rep(0,10))
simu_data <- data.frame(signal = c(solution_ODE1(3,2,0.1,excitation,time)+rnorm(length(time),0,0.1),
solution_ODE1(3.2,1.5,0.3,excitation,time)+rnorm(length(time),0,0.1)),
time = rep(time,2),
excitation = rep(excitation,2),
ID = rep(c("A","B"),each = length(time)))
这是它的样子:
library(ggplot2)
ggplot(simu_data)+
geom_point(aes(time,signal,color = "signal"),size = 2)+
geom_line(aes(time,excitation,color = "excitation"))+
facet_wrap(~ID)
然后我尝试使用nlme进行调整:
fit1 <- nlme(signal ~ solution_ODE1(damping,gain,eq,excitation,time),
data = simu_data,
fixed = damping + gain + eq ~1,
random = damping ~ 1 ,
groups = ~ ID,
start = c(damping = 5, gain = 1,eq = 0))
我得到了这个错误,但我没有得到:
eval(substitute(expr),data,enclos = parent.frame())中的错误: 找不到对象“ k”
traceback
表明错误来自ODE1模型,该模型在生成值时起作用。
16. eval(substitute(expr), data, enclos = parent.frame())
15. eval(substitute(expr), data, enclos = parent.frame())
14. with.default(as.list(c(parms, x)), {
import <- excfunc(time)
dS <- import * k/tau - (S - yo)/tau
res <- c(dS) ...
13. with(as.list(c(parms, x)), {
import <- excfunc(time)
dS <- import * k/tau - (S - yo)/tau
res <- c(dS) ...
12. func(time, state, parms, ...)
11. Func2(times[1], y)
10. eval(Func2(times[1], y), rho)
9. checkFunc(Func2, times, y, rho)
8. lsoda(xstart, time, ODE1, parms)
7. solution_ODE1(damping, gain, eq, excitation, time)
6. eval(model, data.frame(data, pars))
5. eval(model, data.frame(data, pars))
4. eval(modelExpression[[2]], envir = nlEnv)
3. eval(modelExpression[[2]], envir = nlEnv)
2. nlme.formula(signal ~ solution_ODE1(damping, gain, eq, excitation,
time), data = simu_data, fixed = damping + gain + eq ~ 1,
random = damping ~ 1, groups = ~ID, start = c(damping = 5,
gain = 1, eq = 0))
1. nlme(signal ~ solution_ODE1(damping, gain, eq, excitation, time),
data = simu_data, fixed = damping + gain + eq ~ 1, random = damping ~
1, groups = ~ID, start = c(damping = 5, gain = 1, eq = 0))
有人知道我应该如何进行吗?
答案 0 :(得分:1)
在您的示例中,您的times
向量不是单调运行的。我认为这与lsoda
一团糟。时间在这里工作的方式的背景/含义是什么?用两组拟合随机效应模型真的没有意义。您是否正在尝试将同一条曲线拟合到两个独立的时间序列?
这是一个经过精简的示例,并进行了一些调整(并非所有内容都可以折叠为数值向量而不丢失必要的结构):
library(deSolve)
ODE1 <- function(time, x, parms) {
with(as.list(parms), {
import <- excfunc(time)
dS <- import*k/tau - (x-yo)/tau
res <- c(dS)
list(res)
})
}
solution_ODE1 = function(tau1,k1,yo1,excitation,time){
excfunc <- approxfun(time, excitation, rule = 2)
parms <- list(tau = tau1, k = k1, yo = yo1, excfunc = excfunc)
xstart = yo1
out <- lsoda(xstart, time, ODE1, parms)
return(out[,2])
}
time <- 0:49
excitation <- c(rep(0,10),rep(1,10),rep(0,10),rep(1,10),rep(0,10))
simu_data <- data.frame(time = rep(time,2),
excitation = rep(excitation,2))
svec <- c(damping = 3, gain = 1.75, eq = 0.2)
这有效:
with(c(simu_data, as.list(svec)),
solution_ODE1(damping,gain,eq,excitation[1:50],time[1:50]))
但是,如果我们再增加一个步骤(以便时间重置为0),它将失败:
with(c(simu_data, as.list(svec)),
solution_ODE1(damping,gain,eq,excitation[1:51],time[1:51]))
lsoda中的错误(xstart,time,ODE1,parms): 在执行任何集成步骤之前检测到非法输入-请参阅书面消息