ezsim (version 0.5.5)

plot.ezsim: Plot an ezsim Object

Description

There are 3 different modes to plot an ezsim object. 'summary', 'density' and 'powerfun' plot the summary statistics,density function and power function of an ezsim object respectively. 'summary': The y-variable of the plot are summary statistics of the estimator. Two confidence bounds will be shaded in the plot. 25% and 75% percentile will form a 50% confidence bound. Similarly, 2.5% and 97.5% percentile will form a 95% confidence bound. Each plot have only one estimator. The scalars parameter has the longest length will be the x-variable of the plot. The rest of the selection parameters will be become the facets of the plot (see ggplot2). density : Density plot of the estimator. Each plot have only one estimator. selection parameter will appear as different colour and in different facets. powerfun : Plot the power function of test(s). Estimators have to be a test (value = 1 if rejecting the null hypothesis, value = 0 if fail to reject the null hypothesis) banker parameters will not be shown in the graph.

Usage

"plot"(x, type = c("summary", "density", "powerfun"), subset, parameters_priority, return_print = FALSE, ylab, title, pdf_option, null_hypothesis, benchmark, ...)

Arguments

x

An ezsim object

type

Type of plot

subset

subset of estimators or parameters. See subset.ezsim for details.

parameters_priority

Display priority of parameter. If any parameter is missing here, they will be sorted by length.

return_print

If TRUE, return a list of ggplot2 object. If FALSE(default), all of the plot will be printed out.

ylab

Label of y-axis

title

Title of the plot

pdf_option

A list of option pass to pdf. If it is not missing, the plot will export to a pdf file

null_hypothesis

Null hypothesis of the test. For type=='powerfun' only.

benchmark

Benchmark distribution. For type=='density' only.

...

unused

Value

Optional: a list of ggplot2 object

See Also

ezsim,summary.ezsim, plot.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))
# )
# ## 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)
# 
# ## 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$coef[1,1:2]
#                         names(out)<-c('beta_hat','sd_beta_hat')
#                         out
#                     },
#     true_value    = function() c(beta, es/sqrt(n)/xs)
# )
# plot(ezsim_ols)
# plot(ezsim_ols,subset=list(beta=0))
# 
# plot(ezsim_ols,'density')
# plot(ezsim_ols,'density',subset=list(es=1,xs=1))
# 
# 
# ## example 3
# ezsim_powerfun<-ezsim(
#     run           = TRUE,
#     m             = 100,
#     parameter_def = createParDef(selection=list(xs=1,n=50,es=c(1,5),b=seq(-1,1,0.1))),
#     display_name  = c(b='beta',es='sigma[e]^2',xs='sigma[x]^2'),
#     dgp           = function(){
#                         x<-rnorm(n,0,xs)
#                         e<-rnorm(n,0,es)
#                         y<-b * x + e
#                         data.frame(y,x)
#                     },
#     estimator     = function(d){
#                         r<-summary(lm(y~x-1,data=d))
#                         stat<-r$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)