Applies a function repeatedly for a specified number of replications or over a list/data.frame with plot and summary methods for summarizing the Monte Carlo experiment. Can be parallelized via the future package (use the future::plan function).
function or 'sim' object
Number of replications or data.frame with parameters
Optional function (i.e., if x is a matrix)
Optional column names
(optional) Seed (needed with cl=TRUE)
(optional) list of named arguments passed to (mc)mapply
If TRUE the iteration number is passed as first argument to (mc)mapply
Optional number of cores. Will use parallel::mcmapply instead of future
Optional message for the progressr progress-bar
Additional arguments to future.apply::future_mapply
To parallelize the calculation use the future::plan function (e.g., future::plan(multisession()) to distribute the calculations over the R replications on all available cores). The output is controlled via the progressr package (e.g., progressr::handlers(global=TRUE) to enable progress information).
summary.sim plot.sim print.sim
m <- lvm(y~x+e)
distribution(m,~y) <- 0
distribution(m,~x) <- uniform.lvm(a=-1.1,b=1.1)
transform(m,e~x) <- function(x) (1*x^4)*rnorm(length(x),sd=1)
onerun <- function(iter=NULL,...,n=2e3,b0=1,idx=2) {
d <- sim(m,n,p=c("y~x"=b0))
l <- lm(y~x,d)
res <- c(coef(summary(l))[idx,1:2],
confint(l)[idx,],
estimate(l,only.coef=TRUE)[idx,2:4])
names(res) <- c("Estimate","Model.se","Model.lo","Model.hi",
"Sandwich.se","Sandwich.lo","Sandwich.hi")
res
}
val <- sim(onerun,R=10,b0=1)
val
#> Estimate Model.se Model.lo Model.hi Sandwich.se Sandwich.lo Sandwich.hi
#> 1 0.928718 0.033366 0.863282 0.994155 0.047050 0.836502 1.020935
#> 2 0.995991 0.001633 0.992788 0.999193 0.002255 0.991571 1.000411
#> 3 0.998523 0.003280 0.992090 1.004955 0.004726 0.989261 1.007785
#> 4 1.001350 0.004145 0.993220 1.009480 0.005812 0.989960 1.012741
#> 5 0.999646 0.002153 0.995424 1.003869 0.003048 0.993673 1.005620
#> 6 1.004597 0.006829 0.991204 1.017990 0.009495 0.985987 1.023207
#> 7 0.998076 0.009524 0.979397 1.016754 0.013488 0.971640 1.024511
#> 8 0.997917 0.003406 0.991237 1.004596 0.004853 0.988405 1.007428
#> 9 0.999195 0.001333 0.996582 1.001809 0.001906 0.995459 1.002932
#> 10 1.000199 0.000730 0.998768 1.001631 0.001023 0.998195 1.002204
#>
#> Estimate Model.se Model.lo Model.hi Sandwich.se Sandwich.lo Sandwich.hi
#> Mean 0.992421 0.0066400 0.979399 1.0054433 0.0093655 0.974065 1.0107773
#> SD 0.022501 0.0097691 0.041125 0.0074485 0.0137735 0.048867 0.0090643
val <- sim(val,R=40,b0=1) ## append results
summary(val,estimate=c(1,1),confint=c(3,4,6,7),true=c(1,1))
#> 50 replications Time: 1.118s
#>
#> Estimate Estimate.1
#> Mean 0.9941568 0.9941568
#> SD 0.0152271 0.0152271
#> Coverage 0.8800000 0.9800000
#>
#> Min 0.9287185 0.9287185
#> 2.5% 0.9546727 0.9546727
#> 50% 0.9984014 0.9984014
#> 97.5% 1.0140017 1.0140017
#> Max 1.0169750 1.0169750
#>
#> Missing 0.0000000 0.0000000
#>
#> True 1.0000000 1.0000000
#> Bias -0.0058432 -0.0058432
#> RMSE 0.0163097 0.0163097
#>
summary(val,estimate=c(1,1),se=c(2,5),names=c("Model","Sandwich"))
#> 50 replications Time: 1.118s
#>
#> Model Sandwich
#> Mean 0.9941568 0.994157
#> SD 0.0152271 0.015227
#> SE 0.0084199 0.011813
#> SE/SD 0.5529578 0.775782
#>
#> Min 0.9287185 0.928718
#> 2.5% 0.9546727 0.954673
#> 50% 0.9984014 0.998401
#> 97.5% 1.0140017 1.014002
#> Max 1.0169750 1.016975
#>
#> Missing 0.0000000 0.000000
#>
summary(val,estimate=c(1,1),se=c(2,5),true=c(1,1),
names=c("Model","Sandwich"),confint=TRUE)
#> 50 replications Time: 1.118s
#>
#> Model Sandwich
#> Mean 0.9941568 0.9941568
#> SD 0.0152271 0.0152271
#> SE 0.0084199 0.0118129
#> SE/SD 0.5529578 0.7757816
#> Coverage 0.8800000 0.9800000
#>
#> Min 0.9287185 0.9287185
#> 2.5% 0.9546727 0.9546727
#> 50% 0.9984014 0.9984014
#> 97.5% 1.0140017 1.0140017
#> Max 1.0169750 1.0169750
#>
#> Missing 0.0000000 0.0000000
#>
#> True 1.0000000 1.0000000
#> Bias -0.0058432 -0.0058432
#> RMSE 0.0163097 0.0163097
#>
if (interactive()) {
plot(val,estimate=1,c(2,5),true=1,
names=c("Model","Sandwich"),polygon=FALSE)
plot(val,estimate=c(1,1),se=c(2,5),main=NULL,
true=c(1,1),names=c("Model","Sandwich"),
line.lwd=1,col=c("gray20","gray60"),
rug=FALSE)
plot(val,estimate=c(1,1),se=c(2,5),true=c(1,1),
names=c("Model","Sandwich"))
}
f <- function(a=1, b=1) {
rep(a*b, 5)
}
R <- Expand(a=1:3, b=1:3)
sim(f, R)
#> [,1] [,2] [,3] [,4] [,5]
#> 1 1 1 1 1 1
#> 2 2 2 2 2 2
#> 3 3 3 3 3 3
#> 4 2 2 2 2 2
#> 5 4 4 4 4 4
#> 6 6 6 6 6 6
#> 7 3 3 3 3 3
#> 8 6 6 6 6 6
#> 9 9 9 9 9 9
#>
#> [,1] [,2] [,3] [,4] [,5]
#> Mean 4.0000 4.0000 4.0000 4.0000 4.0000
#> SD 2.5495 2.5495 2.5495 2.5495 2.5495
sim(function(a,b) f(a,b), 3, args=c(a=5,b=5))
#> [,1] [,2] [,3] [,4] [,5]
#> 1 25 25 25 25 25
#> 2 25 25 25 25 25
#> 3 25 25 25 25 25
#>
#> [,1] [,2] [,3] [,4] [,5]
#> Mean 25 25 25 25 25
#> SD 0 0 0 0 0
sim(function(iter=1,a=5,b=5) iter*f(a,b), iter=TRUE, R=5)
#> [,1] [,2] [,3] [,4] [,5]
#> 1 25 25 25 25 25
#> 2 50 50 50 50 50
#> 3 75 75 75 75 75
#> 4 100 100 100 100 100
#> 5 125 125 125 125 125
#>
#> [,1] [,2] [,3] [,4] [,5]
#> Mean 75.000 75.000 75.000 75.000 75.000
#> SD 39.528 39.528 39.528 39.528 39.528