# Generalized Linear Models understanding the link function

October 15, 2018
By

(This article was first published on Bluecology blog, and kindly contributed to R-bloggers)

# Generalized Linear Models: understanding the link function

Generalized Linear Models (‘GLMs’) are one of the most useful modern
statistical tools, because they can be applied to many different types
of data. Count, binary ‘yes/no’, and waiting time data are just some of
the types of data that can be handled with GLMs.

We often call such data ‘non-normal’ because its distribution doesn’t
follow a normal distribution (also known as the bell curve or Gaussian
Distribution).

In this post I will look at how GLMs use a ‘link function’ to model
non-normal data. I think there is a sort of beautiful elegance in the
maths of how the link function works. Understanding this theory will
more nuanced ways.

We will step through the math behind the GLM and build it up from
scratch.

GLMs extend ‘General Linear Models’ (confusing names I know), read this
post first if you are not yet familiar with General Linear
Models
.

## Choosing the right distribution for your data

I learned about the Normal distribution in primary school. The normal
distribution is central to much of statistics (no pun intended), but
there are many types of data that don’t meet the basic assumptions of
the normal.

The normal distribution has ‘infinite support’, which means values
modelled by the normal can take any negative or positive number. Often
the normal is a pretty reasonable approximation of data that doesn’t
meet these assumptions, but there are many cases when using a normal for
data that isn’t will lead us to make errors in our inferences.

Statisticians have invented whole families of distributions to describe
any type of data you can imagine, from the morbid (the Exponential
distribution for deaths or decay), to wealth inequality (the Pareto
distribution) and even days of the year (the Von Mises distribution).

More specifically, we should think of the distribution as a description
of the process that generates the data.

fish on coral reefs. So your data are count data.

If the counts are large they may well look pretty normal. But there are
some important differences.

Counts are integers, whereas the normal distribution is for continuous
data that can include any fraction.

Counts also can’t be less than zero, but the Normal distribution model’s
stochastic processes that draw zeros and negative numbers.

Statisticians have invented many distributions for counts, one of the
simplest is the Poisson distribution. It is a model of positive
integers. It has one parameter λ, which is both its mean and variance.

Let’s see what that looks like with some simple R code to draw random
numbers from two Poisson distributions:

``````n <- 1000
set.seed(42)
x1 <- rpois(n, lambda = 1)
x10 <- rpois(n, lambda = 10)
mean(x1)

## [1] 0.976

var(x1)

## [1] 0.9663904

mean(x10)

## [1] 10.083

var(x10)

## [1] 10.75086
``````

We just sampled random numbers from two Poisson distributions with means
of 1 and 10. Notice that the means and variances of each are
approximately equal (not exactly equal because of we drew a small random
sample).

You can think of this sampling from the Poisson as a model of count
data. Let’s see what that looks like:

``````par(mfrow=c(1,2))
hist(x1, xlim = c(0, 25), seq(0, 25, by = 1))
hist(x10, xlim = c(0, 25), seq(0, 25, by = 1))
``````

So the data drawn from the poisson with `lambda = 1` are concentrated
near zero and strongly skewed (not very Normal). The data with
`lambda = 10` are approximately normally distribution and have a much
larger variance than the former data.

## Linear models

So far our Poisson model only has one parameter, a mean (and variance).
But what if we wanted the mean to change?

For instance, we might have counted fish on different types of coral
reefs and we want to test whether there are difference abundances on
each type of reef.

Or we might have counted fish across a gradient of pollution and we want
to know how their numbers change from low to high pollution.

I will call these hypothesized causes of changes in fish counts
‘covariates’. Others might call them explanatory variables, treatments
(if experiments) or predictor variables.

We are using Generalized Linear Models, so we could include the
covariates as variables in a simple linear equation, after all that is
what we do with linear regression (and general linear
models
):

$y_{i}=\alpha+\beta*x_{i}$

Let’s generate some such data ourselves. We will assume pollution is
measured on a zero to one (low to high) scale, that the mean number of
fish with no pollution =4 and that on average there are no fish anymore
when pollution levels = 0.5 (half their maximum).

The plot below illustrates our model for the mean expected number of
fish across the pollution gradient. In this case we are building a model
to simulate some data to practice with:

``````n <- 50
beta <- -8 #effect of polluted reefs
alpha <- 4 #intercept = mean at 'zero' pollution
x <-  seq(0, 1, length.out = n) #pollution levels
ymean <- alpha + beta*x
plot(x, ymean, type = 'l', xlab = "Pollution level", ylab = "Number of fish counted")
abline(h = 0, lty = 2, lwd = 2)
``````

(on average) for pollution levels over 0.5.

It gets even worse if we model sampling with a normal distribution:

``````set.seed(55)
yobs_normal <- ymean + rnorm(n)
plot(x, ymean, type = 'l', xlab = "Pollution level", ylab = "Number of fish counted")
points(x, yobs_normal)
abline(h = 0, lty = 2, lwd = 2)
``````

Clearly if we went in the real world to sample data, we wouldn’t ever
get any negative counts.

So we are going to have to redesign our model, which is the basis of our
statistical tests, so that negative values don’t happen.

But we still want to use a linear model, because they are convenient to
work with mathematically and convenient when it comes to estimating the
unknown effects.

## Simulating Poisson data from a linear model

So now we come to link functions. Link functions elegantly solve the
problem of using linear models with with non-normal data. There are many
types of link functions, but we will look at one that is popular for use
with count data.

If you wanted to stop a linear function from taking negative values what
is one simple transformation you could make?

Well, you could take the function’s exponent. We will redo the above
linear equation as log-linear. I will change the parameter names to
reflect that they are now logs:

``````gamma <- -3.2 #effect of polluted reefs
alpha <- 4 #intercept = mean at 'zero' pollution
yexp <- alpha*exp(gamma*x)
plot(x, yexp, type = 'l', xlab = "Pollution level", ylab = "Number of fish counted")
abline(h = 0, lty = 2, lwd = 2)
``````

Here we have the equation `y = alpha *exp(gamma*x)` which is the same as
the linear equation for `log(y)`: `log(y) = log(alpha) +gamma*x`. Note I
have retained `alpha=4` in both, because for both equations `alpha` is
the expected value at pollution of zero.

I changed the slope parameter in the log-linear equation to `gamma`
because it is not a direct analogue of our slope parameter `beta` above.

One of the nice things about the log-linear equation is that the slope
parameter now represents multiples of change. For instance,
`gamma = -3.2` means the abundance declines about 25 times decline
(=`1/exp(-3.2)`) when going from a pollution level of `0` to `1`.

Abundance declines about a five times decline if we go from a pollution
of `0` to `0.5` (= `1/exp(-3.2*0.5)`). Noting this will be important
when we come to interpreting fitted models below.

We could now use this exponential curve as the mean (and variance!) of a
Poisson:

``````yobs_pois <- rpois(n, yexp)
plot(x, yexp, type = 'l', xlab = "Pollution level",
ylab = "Number of fish counted",
ylim = c(0, 8))
points(x, yobs_pois)
``````

Notice that no data fall below zero now. Also, notice how the variance
of the samples gets smaller as the mean gets smaller.

In the real world, you will have the the sample points, but not the
‘true’ mean. In the example above we just made up the true mean
ourselves. In the real world Nature provides the ‘truth’ about how
pollution impacts fish abundance and the best we can do is take as many
measurements as we can and hope to get near the truth.

To estimate the effect of the pollution covariate you can use R’s
`glm()` function:

``````m1 <- glm(yobs_pois ~ x, family = poisson(link = "log"))
coef(m1)

## (Intercept)           x
##    1.409704   -3.345646
``````

The values we printed give the estimates for the intercept and slope
coeffcients (alpha and gamma). You can check that these are similar to
the true estimates we provided by comparing them to `log(alpha)` and
`gamma`.

I have specified above the type of distribution to use
(`family = poission()`) and which link to use. `"log"` is in fact the
default choice, but I put it there so you know you can change it.

For instance, you can use `"identity"` link for data that is far from
zero. If you use the identity link, which is basically no link function,
your model will be linear, not log-linear, so your slope estimate will

Technically we would say we fitted a Generalized Linear Model with
Poisson errors and a log link function
. We talk about Poisson errors
(not Poisson data), because it is the left over variation after we fit
the model (AKA the errors or residuals) that we are assuming is Poisson
distributed.

The model actually doesn’t make any assumptions about how the raw data
are distributed, so long as they are integers and non-negative. Note
that the data can contain zeros, but the mean of the Poisson is always
>0.

So what do the coefficients mean? Remember the coefficients are on the
log scale. So the mean abundance at a pollution level of zero =
`{r} exp(coef(m1)[1])` and a change in pollution from 0 to 1 causes an
estimated `{r} 1/exp(coef(m1)[2])` times decline in fish abundance.

Let’s also plot the fitted model with standard errors.

``````ypredict <- predict(m1, type = "response", se = TRUE)
plot(x, yexp, type = 'l', xlab = "Pollution level",
ylab = "Number of fish counted",
ylim = c(0, 8))
lines(x, ypredict\$fit, lwd = 2, col = "red", lty = 2)
lines(x, ypredict\$fit + ypredict\$se.fit, lty = 3, col = "red")
lines(x, ypredict\$fit - ypredict\$se.fit, lty = 3, col = "red")
#plot observations
points(x, yobs_pois)
legend('topright', legend = c("True mean", "Estimated mean"),
lwd = 2, lty = c(1,2), col = c("black", "red"))
``````

You can see the fitted line falls close to the ‘true’ line, and the
standard errors are pretty tight around our best estimate.

The fitting algorithm itself is attempting the maximise the
log-likelihood of the sample mean given the observations (in technical
here
.

It is also worth noting that we still need to do assumption checks, like
we would for a regression with normal errors. That is we should check
that the residuals are Poisson distributed and that the residual
variance approximately equals the mean.

A cool way to check assumptions of the Poisson model is to use
‘rootograms’, look it up.

So in my introduction I claimed that maths of GLMs is beautiful. I think
that because the maths is nicely coherent with the way nature often
works.

We wanted to fit a linear function to data that can’t be less than zero,
because linear functions are convenient to work with. So we used a log
link function to describe the mean and to ensure that the mean is always
greater than zero.

We ended up with a model where the slope describes multiples of change
in fish abundance over the pollution gradient. So the model itself is

If you think about it, natural processes that generate counts often are
multiplying’ when they breed, because population growth can be
exponential.

So our mathematically convenient link function actually ended up being a
better description of the natural process.

The effort to use a non-negative model also forced us to think about
using a more appropriate distribution for the model’s errors: the
Poisson rather than the Normal. The Poisson has the variance increasing
with the mean.

Once again, natural processes that generate counts often lead to
increases in the variance in situations where we count more. Counts near
zero will naturally have low variance, because they are constrained by
zero, whereas higher counts will naturally have a greater variabilty.

You can also relax the assumption of mean = variance with other GLM
error distributions like the negative binomial.

It turns out that proper models of variance are crucial for getting the
standard-errors right, and so crucial for detecting real effects over
spurious
ones
.

Imagine if you used a Normal distribution and assumed equal variances.
You might spuriously attribute differences between groups from high
counts to some covariate, but the difference is actually just natural
variation. Conversely, you might miss differences between groups with
low counts, because a smaller difference at low counts should actually
be statistically signficant.

The increased power we get to detect differences at low counts with a
GLM over a regression happens because it is the multiple of the
difference.

My final point is to remember that coefficients from a model with a log
link (and some other links too, like the logit) are multiplicative. This
can be very useful when it comes to making sense of your results and may
change the way you present your findings.

For instance, we used this key insight from a GLM to make a case that
pollution from logging causes a 24 times decline in the abundance of a
threatened fish
species
.

Before we considered using the GLM, we had actually presented the
results in terms of a % change in fish abundance. But % are not as easy
to generalize, because they depend on your baseline. Multiples do not.

Hope you found this post helpful, and as always you can get me on
questions.

I wanted to add a brief appendix to address this question, because the

Try take the data we generated above and fit two GLMs (you will have to
add a small number so you can log the zeros, not ideal but a common
practice)

``````yobsplus <- yobs_pois+0.1
model1 <- glm(yobsplus ~ x, family = gaussian(link = "log"))
model2 <- glm(log(yobsplus) ~ x, family = gaussian(link = "identity"))
``````

In the first model we fitted a Gaussian (=Normal distributed errors)
with a log link. In the second we fitted a Gaussian to log(y) with the

Now compare the results. Notice that the estimate of the slope is quite
different. Why is this?

The model with the log link is fitting the mean on the log scale, the
Gaussian errors will be on the natural scale. So the residual (or error)
variance will be constant for all mean values of y.

The model with the log of the data and identity link is fitting the mean
and variance on the log scale. So if we retransform log(y) back to y,
the variance will change with the mean.

So a log link isn’t the same as a log transformation. The transformation
changes the raw data. The link function doesn’t touch the raw data,
instead you can think of it as a transformation of the model for the
mean of the raw data
.

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