## R Functions for Probability Distributions

Every distribution that R handles has four functions. There is a root name, for example, the root name for the normal distribution is `norm`. This root is prefixed by one of the letters

• `p` for probability, the cumulative distribution function (CDF)
• `q` for quantile, the inverse CDF for a continuous random variable, the quantile function for a discrete random variable
• `d` for density, the probability mass function (PMF) for a discrete random variable or the probability density function (PDF) for a continuous random variable
• `r` for random, a random variable having the specified distribution

For the normal distribution, these functions are `pnorm`, `qnorm`, `dnorm`, and `rnorm`. For the binomial distribution, these functions are `pbinom`, `qbinom`, `dbinom`, and `rbinom`. And so forth.

For a continuous distribution (like the normal), the most useful functions for doing problems involving probability calculations are the `p` and `q` functions (CDF and inverse CDF), because the the PDF calculated by the `d` function can only be used to calculate probabilities via integrals and R doesn't do integrals.

For a discrete distribution (like the binomial), the `d` function calculates the PMF, which in this case is a probability

f(x) = P(X = x)

and hence is useful in calculating probabilities.

R has functions to handle many probability distributions. The table below gives the names of the functions for each distribution and a link to the on-line documentation that is the authoritative reference for how the functions are used. But don't read the on-line documentation yet. First, try the examples in the sections following the table.

Distribution Functions
Beta `pbeta` `qbeta` `dbeta` `rbeta`
Binomial `pbinom` `qbinom` `dbinom` `rbinom`
Cauchy `pcauchy` `qcauchy` `dcauchy` `rcauchy`
Chi-Square `pchisq` `qchisq` `dchisq` `rchisq`
Discrete Uniform `sample`
Exponential `pexp` `qexp` `dexp` `rexp`
F `pf` `qf` `df` `rf`
Gamma `pgamma` `qgamma` `dgamma` `rgamma`
Geometric `pgeom` `qgeom` `dgeom` `rgeom`
Hypergeometric `phyper` `qhyper` `dhyper` `rhyper`
Logistic `plogis` `qlogis` `dlogis` `rlogis`
Log Normal `plnorm` `qlnorm` `dlnorm` `rlnorm`
Negative Binomial `pnbinom` `qnbinom` `dnbinom` `rnbinom`
Normal `pnorm` `qnorm` `dnorm` `rnorm`
Poisson `ppois` `qpois` `dpois` `rpois`
Student t `pt` `qt` `dt` `rt`
Studentized Range `ptukey` `qtukey` `dtukey` `rtukey`
Uniform `punif` `qunif` `dunif` `runif`
Weibull `pweibull` `qweibull` `dweibull` `rweibull`
Wilcoxon Rank Sum Statistic `pwilcox` `qwilcox` `dwilcox` `rwilcox`
Wilcoxon Signed Rank Statistic `psignrank` `qsignrank` `dsignrank` `rsignrank`

That's a lot of distributions. Fortunately, they all work the same way. If you learn one, you've learned them all.

Of course, the discrete distributions are discrete and the continuous distributions are continuous, so there's some difference just from that aspect alone, but as far as the computer is concerned, they're all the same. We'll do a continuous example first.

## The Normal Distribtion

### Direct Look-Up

`pnorm` is the R function that calculates the CDF.

F(x) = P(Xx)

where X is normal. Optional arguments described on the on-line documentation specify the parameters of the particular normal distribution.

Both of the R commands in the box below do exactly the same thing.

They look up P(X < 27.4) when X is normal with mean 50 and standard deviation 20.

#### Example

Question: Suppose widgit weights produced at Acme Widgit Works have weights that are normally distributed with mean 17.46 grams and variance 375.67 grams squared. What is the probability that a randomly chosen widgit weighs more then 19 grams?

Question Rephrased: What is P(X > 19) when X has the N(17.46, 375.67) distribution?

Caution: R wants the standard deviation (SD) as the parameter, not the variance. We'll need to take a square root!

Answer:

### Inverse Look-Up

`qnorm` is the R function that calculates the inverse CDF. F-1 of the normal distribution The CDF and the inverse CDF are related by

p = F(x)
x = F-1(p)

So given a number p between zero and one, `qnorm` looks up the p-th quantile of the normal distribution. As with `pnorm`, optional arguments specify the mean and standard deviation of the distribution.

#### Example

Question: Suppose IQ scores are normally distributed with mean 100 and standard deviation 15. What is the 95th percentile of the distribution of IQ scores?

Question Rephrased: What is F-1(0.95) when X has the N(100, 152) distribution?

Answer:

#### Optional Argument `lower.tail = FALSE`

There is an optional argument `lower.tail = FALSE` to `p` and `q` functions. It can be useful. It calculates the same thing as one minus the value without this argument, but does it without catastrophic cancellation when the result is small. Notice the difference.

### Density

`dnorm` is the R function that calculates the PDF f of the normal distribution. As with `pnorm` and `qnorm`, optional arguments specify the mean and standard deviation of the distribution.

There's not much need for this function in doing calculations, because you need to do integrals to use any PDF, and R doesn't do integrals. In fact, there's not much use for the `d` function for any continuous distribution (discrete distributions are entirely another matter, for them the `d` functions are very useful, see the section about dbinom).

For an example of the use of `dnorm`, see the following section.

### Random Variates

`rnorm` is the R function that simulates random variates having a specified normal distribution. As with `pnorm`, `qnorm`, and `dnorm`, optional arguments specify the mean and standard deviation of the distribution.

We won't be using the `r` functions (such as `rnorm`) much. So here we will only give an example without full explanation.

This generates 1000 independent and identically distributed (IID) normal random numbers (first line), plots their histogram (second line), and graphs the PDF of the same normal distribution (third line).

## The Binomial Distribtion

### Direct Look-Up, Points

`dbinom` is the R function that calculates the PMF of the binomial distribution. Optional arguments described on the on-line documentation specify the parameters of the particular binomial distribution.

Both of the R commands in the box below do exactly the same thing.

They look up P(X = 27) when X is has the Bin(100, 0.25) distribution.

#### Example

Question: Suppose widgits produced at Acme Widgit Works have probability 0.005 of being defective. Suppose widgits are shipped in cartons containing 25 widgits. What is the probability that a randomly chosen carton contains exactly one defective widgit?

Question Rephrased: What is P(X = 1) when X has the Bin(25, 0.005) distribution?

Answer:

### Direct Look-Up, Intervals

`pbinom` is the R function that calculates the CDF of the binomial distribution. Optional arguments described on the on-line documentation specify the parameters of the particular binomial distribution.

Both of the R commands in the box below do exactly the same thing.

They look up P(X ≤ 27) when X is has the Bin(100, 0.25) distribution. (Note the less than or equal to sign. It's important when working with a discrete distribution!)

#### Example

Question: Suppose widgits produced at Acme Widgit Works have probability 0.005 of being defective. Suppose widgits are shipped in cartons containing 25 widgits. What is the probability that a randomly chosen carton contains no more than one defective widgit?

Question Rephrased: What is P(X ≤ 1) when X has the Bin(25, 0.005) distribution?

Answer:

### Inverse Look-Up

`qbinom` is the R function that calculates the quantile function of the binomial distribution. How does it do that when the CDF is a step function and hence not invertible? The on-line documentation for the binomial probability functions explains.

The quantile is defined as the smallest value x such that F(x) >= p, where F is the distribution function.

When the p-th quantile is nonunique, there is a whole interval of values each of which is a p-th quantile. The documentation says that `qbinom` (and other `q` functions, for that matter) returns the smallest of these values. That is one sensible definition of an inverse CDF. The technical name for this concept is quantile function.

#### Example

Question: What are the 10th, 20th, and so forth quantiles of the Bin(10, 1/3) distribution?

Answer:

Note the nonuniqueness.