Nothing
## ----nomessages, echo = FALSE-------------------------------------------------
knitr::opts_chunk$set(
warning = FALSE,
message = FALSE,
fig.height = 5,
fig.width = 5
)
options(digits=4)
par(mar=c(3,3,1,1)+.1)
## ----include=FALSE------------------------------------------------------------
options(digits = 2)
## ----comment=NA---------------------------------------------------------------
library(SimDesign)
SimFunctions()
## ----eval=FALSE---------------------------------------------------------------
# SimDesign::SimFunctions('mysim')
## ----eval=FALSE---------------------------------------------------------------
# SimFunctions('mysim', singlefile = FALSE, comments = TRUE)
## -----------------------------------------------------------------------------
Design <- createDesign(sample_size = c(30, 60, 120, 240),
distribution = c('norm', 'chi'))
Design
## -----------------------------------------------------------------------------
Generate <- function(condition, fixed_objects) {
N <- condition$sample_size
dist <- condition$distribution
if(dist == 'norm'){
dat <- rnorm(N, mean = 3)
} else if(dist == 'chi'){
dat <- rchisq(N, df = 3)
}
dat
}
## -----------------------------------------------------------------------------
Analyse <- function(condition, dat, fixed_objects) {
M0 <- mean(dat)
M1 <- mean(dat, trim = .1)
M2 <- mean(dat, trim = .2)
med <- median(dat)
ret <- c(mean_no_trim=M0, mean_trim.1=M1, mean_trim.2=M2, median=med)
ret
}
## -----------------------------------------------------------------------------
Summarise <- function(condition, results, fixed_objects) {
obs_bias <- bias(results, parameter = 3)
obs_RMSE <- RMSE(results, parameter = 3)
ret <- c(bias=obs_bias, RMSE=obs_RMSE, RE=RE(obs_RMSE))
ret
}
## ----include=FALSE------------------------------------------------------------
set.seed(1234)
## -----------------------------------------------------------------------------
res <- runSimulation(Design, replications = 1000, generate=Generate,
analyse=Analyse, summarise=Summarise)
# Final simulation object
res
## -----------------------------------------------------------------------------
# Extract complete set of stored results
results <- SimResults(res)
results
## -----------------------------------------------------------------------------
REs <- res[,grepl('RE\\.', colnames(res))]
data.frame(Design, REs)
## ----include=FALSE------------------------------------------------------------
set.seed(1)
## -----------------------------------------------------------------------------
(condition <- Design[1, ])
dat <- Generate(condition)
dat
res <- Analyse(condition, dat)
res
## -----------------------------------------------------------------------------
# repeat 1000x
results <- matrix(0, 1000, 4)
colnames(results) <- names(res)
for(i in 1:1000){
dat <- Generate(condition)
res <- Analyse(condition, dat)
results[i, ] <- res
}
head(results)
## -----------------------------------------------------------------------------
Summarise(condition, results)
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