memisc (version 0.11-9)

Simulate: A convenience function for simulation studies

Description

Simulate is a function to simplify simulation studies. It evaluates or calls its first argument within the settings given by its remaining arguments for a predetermined number of replications.

Usage

Simulate(object,conditions,...,replications=1,names=NULL,trace=0)

Arguments

object

a function or an expression that produces simulation results for each replication.

conditions

a data frame with various simulation conditions. Each combination of values in this list is given replications times to object as arguments

...

other arguments to objects, held fixed in the simulation experiment

replications

number of replication in each experimental setting

names

names given to the data frame that results from simulation

trace

an integer value determining the amount of information output during the simulation process. If trace equals zero nothing is reported during the simulation run. Otherwise, the replication number is output for each multiple of

Value

• A data frame that contains experimental conditions and simulation results.

Examples

Normal.example <- function(mean=0,sd=1,n=10){
  x <- rnorm(n=n,mean=mean,sd=sd)
  c(
    Mean=mean(x),
    Median=median(x),
    Var=var(x)
  )
}

Normal.simres <- Simulate(
    Normal.example,
    expand.grid(
          mean=0,
          sd=c(1,10),
          n=c(10,100)
          ),
    replications=200,
    trace=50)

genTable(sd(Median)~sd+n,data=Normal.simres)


expr.simres <- Simulate(median(rnorm(n,mean,sd)),
      expand.grid(
          n=c(10,100),
          mean=c(0,1),
          sd=c(1,10)
      ),
    replications=200
    )

genTable(sd(result)~sd+n,data=expr.simres)

## This takes a little bit longer
lm.example <- function(a=0,b=1,n=101,xrange=c(-1,1),serr=1){
  x <- seq(from=xrange[1],to=xrange[2],length=n)
  y <- a + b*x + rnorm(n,sd=serr)
  lm.res <- lm(y~x)
  coef <- lm.res$coef
  names(coef) <- c("a","b")
  coef
}

lm.simres <- Simulate(
      lm.example,
      expand.grid(
      serr=c(0.1,1,10),
      n=c(11,101,501)
      ),
      replications=200,
      trace=50
    )
genTable(c(sd(a),sd(b))~serr+n,data=lm.simres)