[R] Convergence in Monte Carlo Simulation
Phat Chau
ph@t@ch@u @end|ng |rom m@||@utoronto@c@
Sun Jun 14 14:57:18 CEST 2020
Thank you Michael.
I will clarify some more. The function in the first part of the code that I posted generates the simulated dataset for a cluster randomized trial from the simstudy package.
I am not quite clear what you mean by placing it outside the loop. So the goal here is to create n = 1000 independent datasets with different (randomly drawn values from the specified normal distributions not shown) for all of the parameters. What I have tried to do is place the seed at the very top of all my code in the past, but what that does is it leads to the creation of a single dataset that gets repeated over and over n = 1000 times. Hence, there ends up being no variability in the data (and power estimates from the p-values given the stated and required power).
Regarding the counter, is it correct in this instance that the loop will continue until n = 1000 iterations have successfully converged? I am not so concerned with counting failures.
Thank you.
Edward
On 2020-06-14, 6:46 AM, "Michael Dewey" <lists using dewey.myzen.co.uk> wrote:
I am not 100% clear what your code is doing as it gets a bit wangled as
you posted in HTML but here are a couple of thoughts.
You need to set the seed outside any loops so it happens once and for all.
I would test after trycatch and keep a separate count of failures and
successes as the failure to converge must be meaningful about the
scientific question whatever that is. At the moment your count appears
to be in the correct place to count successes.
Michael
On 14/06/2020 02:50, Phat Chau wrote:
> Hello,
>
> I put together the following code and am curious about its correctness. My first question relates to the Monte Carlo simulations – the goal is to continue to iterate until I get n = 1000 simulations where the model successfully converges. I am wondering if I coded it correctly below with the while loop. Is the idea that the counter increments by one only if “model” does not return a string?
>
> I would also like to know how I can create n = 1000 independent data sets. I think to do this, I would have to set a random number seed via set.seed() before the creation of each dataset. Where would I enter set.seed in the syntax below? Would it be in the function (as indicated in red)?
>
> powercrosssw <- function(nclus, clsize) {
>
> set.seed()
>
> cohortsw <- genData(nclus, id = "cluster")
> cohortsw <- addColumns(clusterDef, cohortsw)
> cohortswTm <- addPeriods(cohortsw, nPeriods = 8, idvars = "cluster", perName = "period")
> cohortstep <- trtStepWedge(cohortswTm, "cluster", nWaves = 4, lenWaves = 1, startPer = 1, grpName = "Ijt")
>
> pat <- genCluster(cohortswTm, cLevelVar = "timeID", numIndsVar = clsize, level1ID = "id")
>
> dx <- merge(pat[, .(cluster, period, id)], cohortstep, by = c("cluster", "period"))
> dx <- addColumns(patError, dx)
>
> setkey(dx, id, cluster, period)
>
> dx <- addColumns(outDef, dx)
>
> return(dx)
>
> }
>
> i=1
>
> while (i < 1000) {
>
> dx <- powercrosssw()
>
> #Fit multi-level model to simulated dataset
> model5 <- tryCatch(lme(y ~ factor(period) + factor(Ijt), data = dx, random = ~1|cluster, method = "REML"),
> warning = function(w) { "warning" }
> )
>
> if (! is.character(model5)) {
>
> coeff <- coef(summary(model5))["factor(Ijt)1", "Value"]
> pvalue <- coef(summary(model5))["factor(Ijt)1", "p-value"]
> error <- coef(summary(model5))["factor(Ijt)1", "Std.Error"]
> bresult <- c(bresult, coeff)
> presult <- c(presult, pvalue)
> eresult <- c(eresult, error)
>
> i <- i + 1
> }
> }
>
> Thank you so much.
>
>
>
> [[alternative HTML version deleted]]
>
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>
--
Michael
http://www.dewey.myzen.co.uk/home.html
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