如果您将它们作为包含所有参数的列表传递给模拟函数，Purrr 的累积函数可以处理随时间变化的索引。但是，要使其正常工作需要一些争论。这里的诀窍是，accumulate() 可以在列表和向量列上工作。您可以使用tidyr函数 nest() 将列分组到包含当前人口状态和参数的列表向量中，然后在结果列表列上使用累积 ()。这解释起来有点复杂，所以我包含了一个演示，以恒定增长率或随时间变化的随机增长率模拟逻辑增长。我还提供了一个示例，说明如何使用 dpylr+purrr+tidyr 来模拟给定模型的多个复制。
library(dplyr)
library(purrr)
library(ggplot2)
library(tidyr)
# Declare the population growth function. Note: the first two arguments
# have to be .x (the prior vector of populations and parameters) and .y,
# the current parameter value and population vector.
# This example function is a Ricker population growth model.
logistic_growth = function(.x, .y, growth, comp) {
pop = .x$pop[1]
growth = .y$growth[1]
comp = .y$comp[1]
# Note: this uses the state from .x, and the parameter values from .y.
# The first observation will use the first entry in the vector for .x and .y
new_pop = pop*exp(growth - pop*comp)
.y$pop[1] = new_pop
return(.y)
}
# Starting parameters the number of time steps to simulate, initial population size,
# and ecological parameters (growth rate and intraspecific competition rate)
n_steps = 100
pop_init = 1
growth = 0.5
comp = 0.05
#First test: fixed growth rates
test1 = data_frame(time = 1:n_steps,pop = pop_init,
growth=growth,comp =comp)
# here, the combination of nest() and group_by() split the data into individual
# time points and then groups all parameters into a new vector called state.
# ungroup() removes the grouping structure, then accumulate runs the function
#on the vector of states. Finally unnest transforms it all back to a
#data frame
out1 = test1 %>%
group_by(time)%>%
nest(pop, growth, comp,.key = state)%>%
ungroup()%>%
mutate(
state = accumulate(state,logistic_growth))%>%
unnest()
# This is the same example, except I drew the growth rates from a normal distribution
# with a mean equal to the mean growth rate and a std. dev. of 0.1
test2 = data_frame(time = 1:n_steps,pop = pop_init,
growth=rnorm(n_steps, growth,0.1),comp=comp)
out2 = test2 %>%
group_by(time)%>%
nest(pop, growth, comp,.key = state)%>%
ungroup()%>%
mutate(
state = accumulate(state,logistic_growth))%>%
unnest()
# This demostrates how to use this approach to simulate replicates using dplyr
# Note the crossing function creates all combinations of its input values
test3 = crossing(rep = 1:10, time = 1:n_steps,pop = pop_init, comp=comp) %>%
mutate(growth=rnorm(n_steps*10, growth,0.1))
out3 = test3 %>%
group_by(rep)%>%
group_by(rep,time)%>%
nest(pop, growth, comp,.key = state)%>%
group_by(rep)%>%
mutate(
state = accumulate(state,logistic_growth))%>%
unnest()
print(qplot(time, pop, data=out1)+
geom_line() +
geom_point(data= out2, col="red")+
geom_line(data=out2, col="red")+
geom_point(data=out3, col="red", alpha=0.1)+
geom_line(data=out3, col="red", alpha=0.1,aes(group=rep)))