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title: "Simulation Studies with R"
author: "Douglas Bates"
date: "11/12/2014"
fig_caption: yes
keep_tex: yes
latex_engine: lualatex
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```{r preliminaries,echo=FALSE,results='hide'}
Simulation studies using `R`
## Simulation studies
One of the great advantages of having statistical software like `R`
available, even for a course in statistical theory, is the ability to
simulate samples from various probability distributions and
statistical models.
This area is worth studying when learning R programming because
simulations can be computationally intensive making it worthwhile
learning effective programming techniques.
We begin with some background on `R` functions associated with distributions.
## R functions associated with probability distributions
A simulation study typically begins with a probability model for the
data and simulation of responses from this model.
For [several common probability distributions](
`R` provides a set of functions, sometimes called a `d-p-q-r` family,
to evaluate the probability density function (for continuous distributions,
the probability mass function for discrete distributions),
the cumulative distribution function or the quantile function
(inverse of the c.d.f) and for simulation of a random sample.
As shown in the
the names of the functions are composed of the initial letter indicating
* `d`: density function (or probability mass function)
* `p`: (cumulative) probability function (values are always in the
interval [0,1])
* `q`: quantile function - the inverse (more-or-less) of the *p*
* `r`: simulation of a random sample from the distribution
and an abbreviation of the distribution name, as shown in the table
referenced above, which also states the parameter names used for the distribution.
### Common distributions
Common continuous distributions include
* [Exponential](
[`exp`]( The parameter is
`rate` (defaults to `1`). The mean of the distribution is `1/rate`.
* [Normal]( )(or Gaussian):
[`norm`]( The most famous
distribution in statistics, this is the well-known _bell-curve_.
Parameters of the distribution are `mean` (defaults to `0`) and `sd`
(defaults to `1`).
* [Uniform](
[`unif`]( Parameters are
`min` (defaults to `0`) and `max` (defaults to `1`).
Common discrete distributions include
* [Binomial](
[`binom`]( Parameters are
`size`, the number of trials, and `prob`, the probability of success
on a single trial (no defaults).
* [Geometric](
[`geom`]( Parameter is `prob`,
the probability of success on each independent trial.
Note that the distribution is defined in terms of the number of failures
**before** the first success.
* [Poisson](
[`pois`]( The parameter is `lambda`,
the mean.
### Less common and derived distributions
Less common continuous distributions include
* [Beta](
[beta]( Parameters are `shape1`
and `shape2` (without defaults) and `ncp`, the non-centrality parameter,
with a default of `0`, corresponding to the central beta.
* [Cauchy](
[`cauchy`]( Parameters are
`location` (defaults to `0`) and `scale` (defaults to `1`).
* [Gamma](
[`gamma`]( Parameters are
`shape` and one of `rate` or `scale`.
The last two parameters are inverses of one another and both default to `1`.
* [Logistic](
[`logis`]( Parameters are
`location` and `scale`.
* [Log-normal](
[`lnorm`]( Parameters are
`meanlog` and `sdlog`.
* [Weibull](
[`weibull`]( Parameters are
`shape` and `scale`.
Continuous distributions describing sample statistics from a Gaussian
population include
* [Chi-square](
[`chisq`]( Parameters are
`df` and `ncp`, the non-centrality parameter, which defaults to `0`.
* [F](
[`f`]( Parameters are `df1` and
`df2`, the numerator and denominator degrees of freedom.
An optional parameter is `ncp`, the non-centrality parameter.
If omitted a central F distribution is assumed.
* [t](
Parameters are `df` and `ncp`, the non-centrality parameter.
If omitted a central t distribution is assumed.
Less common discete distributions include
* [hypergeometric](
* [negative binomial](
## Reproducibility of "random" samples
Although the "random" numbers generated by the `r<dabb>` functions
appear to be random they are, in fact, calculated according to
systematic algorithms.
We say they are _pseudo-random_ numbers.
For all intents and purposes they seem to be random (and there are
extensive checks performed on the generators to ensure that they do
have the properties we would expect for a random sample) but they are
reproducible, if we so choose.
### Setting the seed for the pseudo-random number generator
The random number stream depends on a _seed_ value.
If we want to produce a reproducible example (so, for example, we can
discuss properties of a particular sample and the reader can generate the same
sample for herself so she can examine it) then we set the seed to a
known value before generating the sample. The stored seed is a
complicated structure but we can set it to an integer, often something
trivial like
```{r setseed1}
set.seed(1) # Ensure a reproducible stream
If we simulate a sample,
```{r rnorm1}
(s1 <- rnorm(5))
then similate a second sample
```{r rnorm2}
(s2 <- rnorm(5))
we get different values.
However, if we reset the seed to 1 and *then* simulate the second sample
we reproduce the original values.
```{r setseed2}
(s2 <- rnorm(5))
We can see from the output that the printed values of the two samples
are similar. However, the printed values are rounded. A more
reliable check is
all.equal(s1, s2)
## A first use of simulation - verifying theoretical properties
With resources like [Wikipedia]( we now have
effectively instant access to many of the theoretical results about
distributions that, in the past, many of us needed to memorize.
Nevertheless, it is still a good idea every once in a while to
validate expressions for, say, the mean, the variance (or standard
deviation), the median, etc. of a distribution.
### Comparing sample statistics to theoretical values
The theoretical expected value and variance of a distribution are
defined in terms of the parameters.
Sometimes the definitions are simple and easy to remember; other times they aren't.
A simple way to examine how, say, the expected value depends on the
parameter is to simulate a very large sample from the distribution
and evaluate the sample mean.
Recall that the parameter `rate` in the `R` __d-p-q-r__ functions for the
exponential distribution is the inverse of the expected value.
We would anticipate that the sample mean for a large sample will be very
close to the theoretical mean, `1/rate`.
```{r meanexp1}
mean(rexp(1e6, rate = 1))
mean(rexp(1e6, rate = 0.5))
mean(rexp(1e6, rate = 2))
So the theoretical value of `1/rate` seems to hold.
What about the variance or the standard deviation?
I can remember that one of them is also 1/rate but sometimes I forget
which one.
```{r varexp}
var(rexp(1e6, rate = 1))
var(rexp(1e6, rate = 0.5))
var(rexp(1e6, rate = 2))
So it looks as if the variance is `1/rate^2` which would mean that it is
the standard deviation that is `1/rate`.
## Exercises
1. Evaluate the sample mean, variance and standard deviation of one
or more large samples from the _Geometric_
distribution for different values of `prob` (remember that this
parameter represents a probability and must be between 0 and 1).
Look at the Wikipedia description for the
distribution, which describes two ways of writing the
a. the number of failures *before* the first success
b. the number of trials *until* the first success
Which version is implemented in `R`?
2. Evaluate the sample mean, variance and standard deviation of one
or more large samples from the
distribution for different values of the parameters `min` and
How does the variance related to `min` and `max`.
3. Evaluate the sample mean, variance and standard deviation of one or
more large samples from the
[Binomial] distribution for different values
values of the parameters `size` and `prob`. Compare these to the
theoretical values.
### What is a "large" sample?
We use a large sample size (the notation `1e6` indicates $10^6$ which
is one million) so that the sample statistic will be very close to its
theoretical value based on the parameter values.
Because larger sample sizes lead to less variability in the sample
statistics, we try to balance the compute time versus accuracy.
## Simulation of sample statistics
A more common use of simulation is to assess the _distribution_ of a
_sample statistic_ for a sample from a particular probability
Just about every introductory text book shows simulations of the
distribution of the mean from i.i.d. samples of different probability
distributions when the [Central Limit
Theorem]( is
It is important here to distinguish between `n`, the size of the sample
being considered, and `N`, the number of replications of the value of
the statistic to be simulated.
In practice `n` is small, often less than 100, and `N` is large,
on the order of hundreds of thousands or more.
The interest is in the properties of statistics calculated from "real
world" samples for which `n`, the sample size, may be moderate.
We use a large number of replications, `N`, to get a realistic
representation of the distribution, so that we can examine its
empirical distribution expecting that it will be close to the
theoretical distribution.
### Simulation of the Central Limit Effect
We just saw that for an exponential distribution with parameter `rate`
the mean is 1/rate and the standard deviation is also 1/rate. The
shape of the distribution is, of course, the negative exponential
The Central Limit Theorem states that for the distribution of the
sample mean of i.i.d. samples of size `n` from a distribution with mean
$\mu$ and standard deviation $\sigma$ will have mean $\mu$ and standard deviation
$\sigma/sqrt(n)% and, furthermore, the shape of the distribution will
tend to a normal or Gaussian shape as `n` gets large.
But the result on the shape is an asymptotic result (i.e. as `n` gets
Can we count on it holding for moderate values of `n`?
Our steps in such a simulation are:
1. Choose the values on n that we wish to examine. Because of the
factor of `sqrt(n)` in the denominator of the standard deviation
of the sample mean, we may want to use numbers that are squares,
such as 1, 4, 9, 16, 25, 36, 49.
2. Choose a value of `N`, the number of replications. I would
recommend at least $10^5$ and perhaps as much as $10^6$.
The trade-offs are that a larger value allows you to get a
smoother approximation to the density and more precise
approximations to the theoretical quantities but large values of N
take longer.
Start with a moderately large value and, if your computer can
handle that quickly, then scale up until it seems to be taking too
3. Choose the distribution and parameter values and the statistic of
interest. For our simulation we will use the exponential (=rexp=)
and the default value of the parameter `rate`. We use the function
`mean` to evaluate the sample mean.
### Automatic replication of a calculation
If we want just one value of the sample mean from an i.i.d. sample of
size `n = 9` we could use
```{r meanrexp}
mean(rexp(9, rate = 1))
but now we want to repeat that operation `N` times and save the
resulting values for later analysis.
Because `R` is a programming language we can use some of the control
structures to accomplish this.
Neophyte `R` programmers often do this
in a `for` loop but you have to be careful exactly how you set up the
loop if you want to do this effectively.
Fortunately, there is an alternative to the `for` loop.
The `replicate` function in R is a convenient way of repeating a
calculation, usually involving a simulation, and collecting the
```{r meanrexpdist}
str(mns9 <- replicate(10^5, mean(rexp(9, rate = 1))))
### Examining the sample of realizations of the test statistic
So now we have generated a sample of size 100,000 of means of samples
of size 9 from the standard exponential distribution.
We expect
```{r samplemean1}
to be close to `1` and
```{r samplesd1}
to be close to `1/3`.
To examine the shape of the distribution we can use an empirical
density plot (Figure \ref{fig:empiricaldens})
```{r empiricaldens,echo=FALSE,fig.cap="Empirical density plot of 100,000 means of samples of size 9 from an exponential distribution with rate = 1"}
qplot(mns9, geom="density", xlab="Means of samples of size 9 from an exponential dist.")
or a normal probability plot, also called a `qq` or quantile-quantile
plot (Figure \ref{fig:qqsample})
```{r qqsample,echo=FALSE,fig.cap="Normal quantile-quantile plot of 100,000 means of samples of size 9 from an exponential distribution with rate = 1",fig.width=4.5,fig.height=4.5}
qplot(sample = mns9, main="Means of samples of size 9 from an exponential dist.")
That last plot takes a long time to produce because there are so many
points to plot. There are techniques, to be discussed later, to thin
the plot and avoid plotting so many points.
## Summary
- R functions for producing a random sample from a particular
distribution have names of the form `r<dabb>` where `dabb` is an
abbreviated for of the distribution name.
- For a large sample the values of sample statistics, such as `mean`,
`var`, `sd`, `median`, etc. should be close to the theoretical
values determined by the model parameters.
- We also use simulation to approximate the distribution of sample
statistics calculated from small-sized samples. To evaluate the
distribution we *replicate* the operation of selecting the sample
and evaluating the sample statistic. The number of replicates, N,
is as large as feasible. The larger N is, the better we
approximate the distribution but it also can take much longer to
run. The sample size, n, is often small.
- An idiom to remember where `statFun` is the function to calculate the sample statistic
(`mean`, `median`, etc.), `<dabb>` is the abbreviated name of the
distribution and `<par values>` are values of the parameters of
the distribution is
```{r idiom,eval=FALSE}
statSamp <- replicate(N, <statFun>(r<dabb>(n, <par values>)))