问题描述
我正在使用蒙特卡罗模拟对纵向中介模型进行功效分析。我正在使用 bmem 包 (lavaan) 中的 power.boot 函数。
我只用 5 次代表/5 次引导程序检查了代码，以确保它有效并且确实有效。
然后我按照包文档的建议使用 1000 次重复和 1000 次引导程序运行代码。
现在已经一个多小时了，它仍在运行 - 这正常吗？多长时间太长了？
powermodel1 <-'
x2 ~ start(.6)*x1 + x*x1
x3 ~ start(.6)*x2 + x*x2
m2 ~ start(.15)*x1 + a*x1 + start(.3)*m1 + m*m1
m3 ~ start(.15)*x2 + a*x2 + start(.3)*m2 + m*m2
y2 ~ start(.5)*m1 + b*m2 + start(.3)*y1 + y*y1
y3 ~ start(.5)*m2 + b*m2 + start(.3)*y2 + y*y2 + start(0.05)*x1 + c*x1
x1 ~~ start(.15)*m1
x1 ~~ start(.15)*y1
y1 ~~ start(.5)*m1
'
indirect <- 'ab:=a*b'
N<-200
system.time(bootstrap<-power.boot(powermodel1,indirect,N,nrep=1000,nboot=1000,parallel = 'multicore'))
summary(bootstrap)
解决方法
不幸的是，它看起来需要一段时间；在我的系统上大约 8 小时：
library(bmem)
powermodel1 <-'
x2 ~ start(.6)*x1 + x*x1
x3 ~ start(.6)*x2 + x*x2
m2 ~ start(.15)*x1 + a*x1 + start(.3)*m1 + m*m1
m3 ~ start(.15)*x2 + a*x2 + start(.3)*m2 + m*m2
y2 ~ start(.5)*m1 + b*m2 + start(.3)*y1 + y*y1
y3 ~ start(.5)*m2 + b*m2 + start(.3)*y2 + y*y2 + start(0.05)*x1 + c*x1
x1 ~~ start(.15)*m1
x1 ~~ start(.15)*y1
y1 ~~ start(.5)*m1
'
indirect <- 'ab:=a*b'
N<-200
system.time(bootstrap<-bmem::power.boot(powermodel1,indirect,N,nrep = 10,nboot = 10,parallel = 'multicore'))
system.time(bootstrap<-bmem::power.boot(powermodel1,nrep = 30,nboot = 30,nrep = 60,nboot = 60,nrep = 100,nboot = 100,parallel = 'multicore'))
library(tidyverse)
# Load the times from above into a dataframe
benchmark <- tibble(bootstraps = c(10,30,60,100),times = c(4.021,30.122,121.103,311.236))
# Plot the points and fit a curve
ggplot(benchmark,aes(x = bootstraps,y = times)) +
geom_point() +
geom_smooth(se = FALSE,span = 5)
# Fit a model
fit <- lm(data = benchmark,times~poly(bootstraps,2,raw=TRUE))
newtimes <- data.frame(bootstraps = seq(100,1000,length = 4))
# Predict the time it will take for larger bootstrap/rep values
predict(fit,newdata = newtimes)
> 1 2 3 4
> 311.6829 4568.3812 13789.6754 27975.5655
# Convert from seconds to hours
print(27975.5655/60/60)
>[1] 7.77099