Nothing
## ----eval=FALSE----------------------------------------------------------
# library(MonteCarlo)
## ----include=FALSE-------------------------------------------------------
library(MonteCarlo)
set.seed(1234)
## ------------------------------------------------------------------------
#########################################
## Example: t-test
# Define function that generates data and applies the method of interest
ttest<-function(n,loc,scale){
# generate sample:
sample<-rnorm(n, loc, scale)
# calculate test statistic:
stat<-sqrt(n)*mean(sample)/sd(sample)
# get test decision:
decision<-abs(stat)>1.96
# return result:
return(list("decision"=decision))
}
## ------------------------------------------------------------------------
# define parameter grid:
n_grid<-c(50,100,250,500)
loc_grid<-seq(0,1,0.2)
scale_grid<-c(1,2)
# collect parameter grids in list:
param_list=list("n"=n_grid, "loc"=loc_grid, "scale"=scale_grid)
## ----eval=FALSE----------------------------------------------------------
# # run simulation:
#
# MC_result<-MonteCarlo(func=ttest, nrep=1000, param_list=param_list)
## ----include=FALSE-------------------------------------------------------
MC_result<-MonteCarlo(func=ttest, nrep=1000, param_list=param_list)
## ------------------------------------------------------------------------
summary(MC_result)
## ------------------------------------------------------------------------
# generate table:
MakeTable(output=MC_result, rows="n", cols=c("loc","scale"), digits=2, include_meta=FALSE)
## ------------------------------------------------------------------------
# generate table:
MakeTable(output=MC_result, rows=c("n","scale"), cols="loc", digits=2, include_meta=FALSE)
## ------------------------------------------------------------------------
# MC_result<-MonteCarlo(func=ttest, nrep=1000, param_list=param_list, [comment]: # debug=TRUE)
## ------------------------------------------------------------------------
ttest<-function(n,loc,scale){
sample<-rnorm(n, loc, scale)
stat<-sqrt(n)*mean(sample)/sd(sample)
return(list("stat"=stat))
}
## ----include=FALSE-------------------------------------------------------
MC_result<-MonteCarlo(func=ttest, nrep=1000, param_list=param_list)
## ----eval=FALSE----------------------------------------------------------
# MC_result<-MonteCarlo(func=ttest, nrep=1000, param_list=param_list)
## ------------------------------------------------------------------------
df<-MakeFrame(MC_result)
head(df)
## ----message=FALSE, warning=FALSE----------------------------------------
library(dplyr)
library(ggplot2)
tbl <- tbl_df(df)
ggplot(filter(tbl, loc==0, scale==1)) + geom_density(aes(x=stat, col=factor(n)))
## ------------------------------------------------------------------------
mean_vs_median<-function(n,scale){
# generate sample
sample<-rnorm(n, 0, scale)
# calculate estimators
mean_sample<-mean(sample)
median_sample<-median(sample)
# return results
return(list("mean"=mean_sample, "median"=median_sample))
}
n_grid<-c(50, 250, 500)
scale_grid<-c(1, 2, 4)
param_list=list("n"=n_grid, "scale"=scale_grid)
## ----eval=FALSE----------------------------------------------------------
# # run simulation:
#
# erg_mean_median<-MonteCarlo(func=mean_vs_median, nrep=1000, param_list=param_list)
## ----include=FALSE-------------------------------------------------------
erg_mean_median<-MonteCarlo(func=mean_vs_median, nrep=1000, param_list=param_list)
## ------------------------------------------------------------------------
MakeTable(output=erg_mean_median, rows="n", cols="scale", digits=2, include_meta=FALSE)
## ------------------------------------------------------------------------
MakeTable(output=erg_mean_median, rows=c("n"), cols=c("scale","list"), digits=2, include_meta=FALSE)
## ------------------------------------------------------------------------
# use partial_grid
MakeTable(output=erg_mean_median, rows="n", cols=c("scale","list"), digits=2,
partial_grid=list("n"=c(1,3), "scale"=c(1,3)), include_meta=FALSE)
## ------------------------------------------------------------------------
MakeTable(output=erg_mean_median, rows=c("n"), cols=c("scale","list"), digits=4, include_meta=FALSE)
## ------------------------------------------------------------------------
MakeTable(output=erg_mean_median, rows=c("n"), cols=c("scale","list"), digits=2, transform=list(function(x){x*100}, function(x){x*100}), include_meta=FALSE)
## ------------------------------------------------------------------------
MakeTable(output=erg_mean_median, rows="n", cols=c("scale", "list"), digits=2, collapse=list("sd", "sd"), include_meta=FALSE)
## ------------------------------------------------------------------------
mean_vs_median<-function(n,scale,DGP){
# generate sample
if(DGP=="normal"){sample<-rnorm(n, 0, scale)}
if(DGP=="uniform"){
b<-scale/(2*sqrt(1/12))
sample<-runif(n, -b, b)
}
# calculate estimators
mean_sample<-mean(sample)
median_sample<-median(sample)
# return results
return(list("mean"=mean_sample, "median"=median_sample))
}
n_grid<-c(50, 250, 500)
scale_grid<-c(1, 2, 4)
DGP_grid<-c("normal", "uniform")
param_list=list("n"=n_grid, "scale"=scale_grid, "DGP"=DGP_grid)
## ----eval=FALSE----------------------------------------------------------
# # run simulation:
#
# erg_mean_median<-MonteCarlo(func=mean_vs_median, nrep=1000, param_list=param_list)
## ----include=FALSE-------------------------------------------------------
erg_mean_median<-MonteCarlo(func=mean_vs_median, nrep=1000, param_list=param_list)
## ------------------------------------------------------------------------
MakeTable(output=erg_mean_median, rows=c("n","DGP"), cols=c("scale", "list"), digits=2, collapse=list("sd", "sd"), include_meta=FALSE)
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