ezsim (version 0.5.5)

ezsim: Create an ezsim Object.

Description

Create an ezsim object from 4 important arguments(dgp,estimator,true,parameter_def).

Usage

ezsim(m, estimator, dgp, parameter_def, true_value = NULL, display_name = NULL, estimator_parser = NULL, auto_save = 0, run = TRUE, run_test = TRUE, use_seed = round(runif(1) * 1e+05), use_core = 1, cluster_packages = NULL, cluster = NULL)

Arguments

m

The number of times you want to simulate.

estimator

A function takes the return value of dgp as argument and return estimates of the dgp

dgp

A function defines the data generating process(dgp). It returns an object (usually it is a data.frame) which can be used as argument of estimator. It will be evaluated under an environment generated by a set of parameter generated by parameter_def.

parameter_def

A parameter_def object will be used in this simulation.

true_value

A function defines the true value of estimators(TV). Similar to dgp, but it returns the true value of estimates. It is necessary for computing the bias and rmse of the estimator. The length of its return value must be the same as the lenght of estimator. If it is missing, True Value, Bias and rmse will be NA in the summary of ezsim.

display_name

Display name for the name of parameter and estimator. see plotmath for details.

estimator_parser

A function to parse the return value of estimator function

auto_save

number of auto save during the simulation, default is 0.

run

Whether the simulation will be ran right after the creation.

run_test

Whether to perform a test before the simulation.

use_seed

The seed to be used in the simulation. If use_core=1, set.seed(use_seed) will be called. If use_core=>1 and cluster=NULL, clusterSetRNGStream(cluster,use_seed) will be used. Ignored if use_core=>1 and cluster is provided.

use_core

Number of cpu core to be used.

cluster_packages

Names of the packages to be loaded in the cluster.

cluster

cluster for parallelization. If it is NULL, a cluster with use_code cores will be created automatically (will be removed after the simulation)

Value

An ezsim object.

See Also

createParDef setBanker,setSelection summary.ezsim

Examples

## Not run: 
# ## Example 1
# ezsim_basic<-ezsim(
#     m             = 100,
#     run           = TRUE,
#     display_name  = c(mean_hat="hat(mu)",sd_mean_hat="hat(sigma[hat(mu)])"),
#     parameter_def = createParDef(list(n=seq(20,80,20),mu=c(0,2),sigma=c(1,3,5))),
#     dgp           = function() rnorm(n,mu,sigma),
#     estimator     = function(x) c(mean_hat = mean(x),
#                                  sd_mean_hat=sd(x)/sqrt(length(x)-1)),
#     true_value    = function() c(mu, sigma / sqrt(n-1))
# )
# 
# ## Test whether an ezsim object is valid.
# ## Print the result of the test and dont return the name of estimator.
# test(ezsim_basic,print_result=TRUE,return_name=FALSE)
# 
# ## Summary of an ezsim object
# summary(ezsim_basic)
# 
# ## Summary of a subset of ezsim object
# summary(ezsim_basic,subset=list(estimator='mean_hat',n=c(20,40),sigma=c(1,3)))
# 
# ## More Summary Statistics
# summary(ezsim_basic,simple=FALSE,subset=list(estimator='mean_hat',n=c(20,40),sigma=c(1,3)))
# 
# ## Customize the Summary Statistics
# summary(ezsim_basic,stat=c("q25","median","q75"),Q025=quantile(value_of_estimator,0.025),
#   Q975=quantile(value_of_estimator,0.975),subset=list(estimator='mean_hat',n=c(20,40),sigma=c(1,3)))
# 
# ## Plot an ezsim object
# plot(ezsim_basic)
# ## Subet of the Plot
# plot(ezsim_basic,subset=list(estimator="sd_mean_hat",mu=0))
# plot(ezsim_basic,subset=list(estimator="mean_hat",sigma=3))
# ## Parameters Priority of the Plot
# plot(ezsim_basic,subset=list(estimator="sd_mean_hat",mu=0),parameters_priority=c("sigma","n"))
# plot(ezsim_basic,subset=list(estimator="mean_hat",sigma=c(1,3)),parameters_priority="mu")
# 
# ## Density Plot
# plot(ezsim_basic,'density')
# plot(ezsim_basic,"density",subset=list(estimator="mean_hat",sigma=3),parameters_priority="n",
#   benchmark=dnorm)
# plot(ezsim_basic,"density",subset=list(estimator="mean_hat",mu=0),parameters_priority="n" ,
#   benchmark=dnorm)
# 
# ## Plot the summary ezsim
# plot(summary(ezsim_basic,c("q25","q75")))
# plot(summary(ezsim_basic,c("q25","q75"),subset=list(estimator='mean_hat')))
# plot(summary(ezsim_basic,c("median"),subset=list(estimator='sd_mean_hat')))
# 
# ## Example 2
# ezsim_ols<-ezsim(
#     m             = 100,
#     run           = TRUE,
#     display_name  = c(beta_hat='hat(beta)',es='sigma[e]^2',xs='sigma[x]^2',
# 	                      sd_beta_hat='hat(sigma)[hat(beta)]'),
#     parameter_def = createParDef(selection=list(xs=c(1,3),beta=c(0,2),n=seq(20,80,20),es=c(1,3))),
#     dgp           = function(){
#                         x<-rnorm(n,0,xs)
#                         e<-rnorm(n,0,es)
#                         y<-beta * x + e
#                         data.frame(y,x)
#                     },
#     estimator     = function(d){
#                         r<-summary(lm(y~x-1,data=d))
#                         out<-r$coef[1,1:2]
#                         names(out)<-c('beta_hat','sd_beta_hat')
#                         out
#                     },
#     true_value    = function() c(beta, es/sqrt(n)/xs)
# )
# summary(ezsim_ols)
# plot(ezsim_ols)
# plot(ezsim_ols,subset=list(beta=0))
# 
# plot(ezsim_ols,'density')
# plot(ezsim_ols,'density',subset=list(es=1,xs=1))
# 
# 
# ## example 3
# ezsim_powerfun<-ezsim(
#     run           = TRUE,
#     m             = 100,
#     parameter_def = createParDef(selection=list(xs=1,n=50,es=c(1,5),b=seq(-1,1,0.1))),
#     display_name  = c(b='beta',es='sigma[e]^2',xs='sigma[x]^2'),
#     dgp           = function(){
#                         x<-rnorm(n,0,xs)
#                         e<-rnorm(n,0,es)
#                         y<-b * x + e
#                         data.frame(y,x)
#                     },
#     estimator     = function(d){
#                         r<-summary(lm(y~x-1,data=d))
#                         stat<-r$coef[,1]/r$coef[,2]
# 
#                         # test whether b > 0
#                         # level of significance : 5%
#                         out <- stat > c(qnorm(.95), qt(0.95,df=r$df[2]))
#                         names(out)<-c("z-test","t-test")
#                         out
#                     }
# )
# plot(ezsim_powerfun,'powerfun')
# ## End(Not run)