I'm going to talk about simulation in this lecture. Simulation's a very important topic for statistics and for a number of other applications, so I just want to introduce some of the functions in R that can be useful for doing simulation. So, there are a couple of functions that are available for simulating numbers or variables from given probability distributions, probably the most important of which is the normal distribution. And so we can generate variates from the normal distribution by specifying a mean and a standard deviation for that distribution and then calling the rnorm function. So the rnorm function will simulate normal random variables that from a distribution has a given mean and standard deviation. So the, there's a cor, there are corresponding functions for the R, for the normal distribution that can be used to evaluate the probability density, to evaluate the cumulative distribution function and for also for evaluating the quantile function. So, another function for generating random variables is the rpoirs function or the, which generates Poisson random variables from a Poisson distribution with a given rate. And so, so there are number of functions for generating random variables from the, from kind of the standard probability distributions. And you can use these to do, to run simulations. So, probability distribution functions ha, there are basically four functions associated with them. And so for any given distribution like the normal distribution there will be a function that starts with the d, a function that starts with an r, a p, and a q. So there'll be four different functions for each distribution. So we've ready, I've already mentioned that there's the rnorm function. The rnorm function is for generating the, is for random number generation. There's a dnorm function, which evaluates the density of the probability dist distribution for given mean and standard deviation. There's the pnorm function, which evaluates the cumulative distribution. And there's the qnorm function, which evaluates the quantile function. So every distribution has these four types of functions. So for the gamma distribution, there'll be a dgamma, an rgamma, pgamma, and a qgamma function. And for the Poisson distribution there's the rpoise dpoise ppoise, and qpoise functions. So working with the normal distribution re, requires these four functions. So I mentioned there's dnorm, pnorm, qnorm, and, and rnorm, and you can see they each take a number of different parameters. All the functions have required that you specify the mean and the standard deviation, because that's what specifies the actual probability distribution. If you do not specify them, then the default values are a distribution, a standard normal distribution, which has mean zero and standard deviation one. For the dnorm function the, you wa you can evaluate the density. And there's an optional, there's a, there's an option that allows you to evaluate the log of the density. Most of the time, when you evaluate the density function for a normal distribution, you're going to want to use the log of that value. But the default is false. For the pnorm function and the qnorm function there's also an option to evaluate it on a log scale. but, but, but another option is to evaluate, is whether or not you want to evaluate the lower tail of the distribution. So the lower tail, which is the default, is the kind, if you think of it, if you look at the probability distribution it's the part that goes to the left. It's the lower tail. If you want to evaluate the upper tail, sometimes you want to do this. Then you want to say lower tail equals false, and that will evaluate the upper tail of the distribution. And finally for rnorm, there's only two parameters, mean and standard deviation, and an n, which is the number of random variables that you want to generate. So if n is 100, you'll get a vector of 100 numbers that are drawn from the, from the normal distribution. So just to be more explicit, if phi is the cumulative distribution function for the standard normal distribution, then pnorm is equal then to phi and qnorm is equal then to the inverse of phi. So, just to quickly, if you want to generate some random normal ren, er, variates. You can just rnorm and pass in an integer, which is the number of variables you want to generate. So here I'm passing ten. And you can see that the vector that's produced will be random, normal numbers which have mean zero and standard deviation one. If I wanted to generate a vector that had mean 20 and standard deviation two, I, I just need to specify that explicitly in my call to rnorm. So here, this vector has a, is, are, ten random normal vi, sorry, normal random variables and their mean is roughly 20 and their stand deviation is two. So when you, any time you simulate random numbers wi, from any distribution for any purpose, it's very important that you set the random number generator seed. And this can be done with the set dot seed function. So, what's important to know that on computers when you generate random numbers, the numbers are not actually random but they appear random and that's the important thing. And, if so the idea is that if you wanted to generate the same set of random numbers again, you could if you wanted to because the numbers are not actually random. They're called, they're wit, they're what are called pseudo random numbers. And so here I'm setting the seed to be one. So the seed can be any integer you want. You just pass in an integer, and that's the seed. So here I'm going to set seed equal to one. And then I'm going to generate five ra, random normal random variables. And so here I've got my ran, my five normal random variables. They have mean zero and standard deviation one. If I generate another five, you'll see that the vector is totally different, because it's another random sample of five. However if I reset the seed to be one, and I draw five again, you'll see that they're exactly the same as the first five that I drew. So anytime you, so when you set the seed it kind of sets the, the sequence of random variable that's just going to occur. And if you reset the seed, you kind of set the sequence to go back to where you started, and then it will continue to kind of generate random variables from there. so, this is important because it allows for you to reproduce random numbers that you generate. Now that might sound strange, because why would you want to, to re, generate the same random numbers twice? But in many applications you do want to generate the same random numbers twice so that people can reproduce what you've done. And particular if there are some errors or problems in what you've done, you want to be able to get, just to kind of go back to the exact situation that produced those problems. So whenever you do a simulation, you always want to set the random number c, so that you can go back and get the same results. So I've demonstrated how to generate normal random variables, but of course you can generate random variables for other probability distributions. So the Poisson distribution is of course very popular. Here I'm generating a ten Poisson random variables with the rate of one. And and so of course Poisson data are going to be integer. Here I'm generating a pois, ten Poisson random variables at the rate of two, so you can see they're slightly larger. And then here I'm generating ten random variables Poisson random variables with a, with a rate of 20. And so, so for the Poisson distribution, the mean is going to be equal to the rate. So you can see that roughly in each of these three cases, the mean is roughly equal to the rate that I specified. I could also evaluate the cumulative distribution function for the Poisson distribution. So here I'm in this first example I want to know what is the probability that a Poisson random variable is less than or equal to two if the rate is two. And so this is the probability. It's 0.67 roughly. If I wanted to know what's the probability that, that a Poisson random variable with rate two is less than four, less than or equal to four. You can see the probability's getting bigger. And here I can see the probability that a Poisson random variable is less than six. Less than or equal to six, and it's very close to one. So the cumulative distribution allows you to, to evaluate these probabilities.
