解决方法是安装deSolve的最新版本,并将参数METHOD = "lsoda"改为METHOD = "lsoda_dense"。这将改变算法以使用更稳定的步长计算。以下是代码示例:
# 安装deSolve的最新版本
install.packages("deSolve")
# 加载库
library(deSolve)
# 构建ODE
ODE <- function(t, y, parms) {
with(as.list(c(parms, y)), {
dN_dt <- r * ((K - N) / K) * N
dL_dt <- a * N - b * L
return(list(c(dN_dt, dL_dt)))
})
}
# 设定参数和初始条件
params <- list(r = 0.5, K = 100, a = 0.01, b = 0.1)
y <- c(N = 10, L = 1)
# 设定时间步长
times <- seq(0, 100, by = 0.1)
# 运行模型
out <- ode(y = y, times = times, func = ODE, parms = params, method = "lsoda_dense")
# 绘制模型输出
plot(out, col = c("red", "blue"), lty = 1, lwd = 2, xlab = "Time", ylab = "Population", main = "")
legend("topleft", c("N", "L"), col = c("red", "blue"), lty = 1, lwd = 2)
这个例子中的ODE为Lotka-Volterra方程,求解动态人口模型。通过将METHOD参数设置为"lsoda_dense",我们避免了步长问题。