In
statistics
Statistics (from German language, German: ''wikt:Statistik#German, Statistik'', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of ...
, a generalized linear model (GLM) is a flexible generalization of ordinary
linear regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is call ...
. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a ''link function'' and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
Generalized linear models were formulated by
John Nelder and
Robert Wedderburn as a way of unifying various other statistical models, including
linear regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is call ...
,
logistic regression and
Poisson regression. They proposed an
iteratively reweighted least squares
The method of iteratively reweighted least squares (IRLS) is used to solve certain optimization problems with objective functions of the form of a ''p''-norm:
:\underset \sum_^n \big, y_i - f_i (\boldsymbol\beta) \big, ^p,
by an iterative met ...
method for
maximum likelihood estimation (MLE) of the model parameters. MLE remains popular and is the default method on many statistical computing packages. Other approaches, including
Bayesian regression and
least squares fitting
The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the Resi ...
to
variance stabilized responses, have been developed.
Intuition
Ordinary linear regression predicts the
expected value
In probability theory, the expected value (also called expectation, expectancy, mathematical expectation, mean, average, or first moment) is a generalization of the weighted average. Informally, the expected value is the arithmetic mean of a l ...
of a given unknown quantity (the ''response variable'', a
random variable
A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which depends on random events. It is a mapping or a function from possible outcomes (e.g., the po ...
) as a
linear combination of a set of observed values (''predictors''). This implies that a constant change in a predictor leads to a constant change in the response variable (i.e. a ''linear-response model''). This is appropriate when the response variable can vary, to a good approximation, indefinitely in either direction, or more generally for any quantity that only varies by a relatively small amount compared to the variation in the predictive variables, e.g. human heights.
However, these assumptions are inappropriate for some types of response variables. For example, in cases where the response variable is expected to be always positive and varying over a wide range, constant input changes lead to geometrically (i.e. exponentially) varying, rather than constantly varying, output changes. As an example, suppose a linear prediction model learns from some data (perhaps primarily drawn from large beaches) that a 10 degree temperature decrease would lead to 1,000 fewer people visiting the beach. This model is unlikely to generalize well over different sized beaches. More specifically, the problem is that if you use the model to predict the new attendance with a temperature drop of 10 for a beach that regularly receives 50 beachgoers, you would predict an impossible attendance value of −950. Logically, a more realistic model would instead predict a constant ''rate'' of increased beach attendance (e.g. an increase of 10 degrees leads to a doubling in beach attendance, and a drop of 10 degrees leads to a halving in attendance). Such a model is termed an ''exponential-response model'' (or ''
log-linear model
A log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model, which makes it possible to apply (possibly multivariate) linear regression. That is, it has ...
'', since the
logarithm of the response is predicted to vary linearly).
Similarly, a model that predicts a probability of making a yes/no choice (a
Bernoulli variable
In probability and statistics, a Bernoulli process (named after Jacob Bernoulli) is a finite or infinite sequence of binary random variables, so it is a discrete-time stochastic process that takes only two values, canonically 0 and 1. The ...
) is even less suitable as a linear-response model, since probabilities are bounded on both ends (they must be between 0 and 1). Imagine, for example, a model that predicts the likelihood of a given person going to the beach as a function of temperature. A reasonable model might predict, for example, that a change in 10 degrees makes a person two times more or less likely to go to the beach. But what does "twice as likely" mean in terms of a probability? It cannot literally mean to double the probability value (e.g. 50% becomes 100%, 75% becomes 150%, etc.). Rather, it is the ''
odds'' that are doubling: from 2:1 odds, to 4:1 odds, to 8:1 odds, etc. Such a model is a ''log-odds or
logistic model''.
Generalized linear models cover all these situations by allowing for response variables that have arbitrary distributions (rather than simply
normal distribution
In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is
:
f(x) = \frac e^
The parameter \mu ...
s), and for an arbitrary function of the response variable (the ''link function'') to vary linearly with the predictors (rather than assuming that the response itself must vary linearly). For example, the case above of predicted number of beach attendees would typically be modeled with a
Poisson distribution and a log link, while the case of predicted probability of beach attendance would typically be modelled with a
Bernoulli distribution (or
binomial distribution
In probability theory and statistics, the binomial distribution with parameters ''n'' and ''p'' is the discrete probability distribution of the number of successes in a sequence of ''n'' independent experiments, each asking a yes–no quest ...
, depending on exactly how the problem is phrased) and a log-odds (or ''
logit'') link function.
Overview
In a generalized linear model (GLM), each outcome Y of the
dependent variable
Dependent and independent variables are variables in mathematical modeling, statistical modeling and experimental sciences. Dependent variables receive this name because, in an experiment, their values are studied under the supposition or demand ...
s is assumed to be generated from a particular
distribution Distribution may refer to:
Mathematics
*Distribution (mathematics), generalized functions used to formulate solutions of partial differential equations
* Probability distribution, the probability of a particular value or value range of a vari ...
in an
exponential family, a large class of
probability distributions that includes the
normal,
binomial
Binomial may refer to:
In mathematics
*Binomial (polynomial), a polynomial with two terms
* Binomial coefficient, numbers appearing in the expansions of powers of binomials
*Binomial QMF, a perfect-reconstruction orthogonal wavelet decomposition
...
,
Poisson and
gamma
Gamma (uppercase , lowercase ; ''gámma'') is the third letter of the Greek alphabet. In the system of Greek numerals it has a value of 3. In Ancient Greek, the letter gamma represented a voiced velar stop . In Modern Greek, this letter re ...
distributions, among others. The mean, ''μ'', of the distribution depends on the independent variables, X, through:
:
where E(Y, X) is the
expected value
In probability theory, the expected value (also called expectation, expectancy, mathematical expectation, mean, average, or first moment) is a generalization of the weighted average. Informally, the expected value is the arithmetic mean of a l ...
of Y
conditional
Conditional (if then) may refer to:
* Causal conditional, if X then Y, where X is a cause of Y
* Conditional probability, the probability of an event A given that another event B has occurred
*Conditional proof, in logic: a proof that asserts a ...
on X; X''β'' is the ''linear predictor'', a linear combination of unknown parameters ''β''; ''g'' is the link function.
In this framework, the variance is typically a function, V, of the mean:
:
It is convenient if V follows from an exponential family of distributions, but it may simply be that the variance is a function of the predicted value.
The unknown parameters, ''β'', are typically estimated with
maximum likelihood, maximum
quasi-likelihood
In statistics, quasi-likelihood methods are used to estimate parameters in a statistical model when exact likelihood methods, for example maximum likelihood estimation, are computationally infeasible. Due to the wrong likelihood being used, quasi- ...
, or
Bayesian techniques.
Model components
The GLM consists of three elements:
: 1. A particular distribution for modeling
from among those which are considered exponential families of probability distributions,
: 2. A linear predictor
, and
: 3. A link function
such that
.
Probability distribution
An overdispersed exponential family of distributions is a generalization of an
exponential family and the
exponential dispersion model of distributions and includes those families of probability distributions, parameterized by
and
, whose density functions ''f'' (or
probability mass function
In probability and statistics, a probability mass function is a function that gives the probability that a discrete random variable is exactly equal to some value. Sometimes it is also known as the discrete density function. The probability mass ...
, for the case of a
discrete distribution) can be expressed in the form
:
The ''dispersion parameter'',
, typically is known and is usually related to the variance of the distribution. The functions
,
,
,
, and
are known. Many common distributions are in this family, including the normal, exponential, gamma, Poisson, Bernoulli, and (for fixed number of trials) binomial, multinomial, and negative binomial.
For scalar
and
(denoted
and
in this case), this reduces to
:
is related to the mean of the distribution. If
is the identity function, then the distribution is said to be in
canonical form (or ''natural form''). Note that any distribution can be converted to canonical form by rewriting
as
and then applying the transformation
. It is always possible to convert
in terms of the new parametrization, even if
is not a
one-to-one function; see comments in the page on
exponential families. If, in addition,
is the identity and
is known, then
is called the ''canonical parameter'' (or ''natural parameter'') and is related to the mean through
:
For scalar
and
, this reduces to
:
Under this scenario, the variance of the distribution can be shown to be
:
For scalar
and
, this reduces to
:
Linear predictor
The linear predictor is the quantity which incorporates the information about the independent variables into the model. The symbol ''η'' (
Greek "
eta
Eta (uppercase , lowercase ; grc, ἦτα ''ē̂ta'' or ell, ήτα ''ita'' ) is the seventh letter of the Greek alphabet, representing the close front unrounded vowel . Originally denoting the voiceless glottal fricative in most dialects, ...
") denotes a linear predictor. It is related to the
expected value
In probability theory, the expected value (also called expectation, expectancy, mathematical expectation, mean, average, or first moment) is a generalization of the weighted average. Informally, the expected value is the arithmetic mean of a l ...
of the data through the link function.
''η'' is expressed as linear combinations (thus, "linear") of unknown parameters ''β''. The coefficients of the linear combination are represented as the matrix of independent variables X. ''η'' can thus be expressed as
:
Link function
The link function provides the relationship between the linear predictor and the
mean of the distribution function. There are many commonly used link functions, and their choice is informed by several considerations. There is always a well-defined ''canonical'' link function which is derived from the exponential of the response's
density function. However, in some cases it makes sense to try to match the
domain
Domain may refer to:
Mathematics
*Domain of a function, the set of input values for which the (total) function is defined
**Domain of definition of a partial function
**Natural domain of a partial function
**Domain of holomorphy of a function
* Do ...
of the link function to the
range
Range may refer to:
Geography
* Range (geographic), a chain of hills or mountains; a somewhat linear, complex mountainous or hilly area (cordillera, sierra)
** Mountain range, a group of mountains bordered by lowlands
* Range, a term used to i ...
of the distribution function's mean, or use a non-canonical link function for algorithmic purposes, for example
Bayesian probit regression.
When using a distribution function with a canonical parameter
, the canonical link function is the function that expresses
in terms of
, i.e.
. For the most common distributions, the mean
is one of the parameters in the standard form of the distribution's
density function, and then
is the function as defined above that maps the density function into its canonical form. When using the canonical link function,
, which allows
to be a
sufficient statistic
In statistics, a statistic is ''sufficient'' with respect to a statistical model and its associated unknown parameter if "no other statistic that can be calculated from the same sample provides any additional information as to the value of the pa ...
for
.
Following is a table of several exponential-family distributions in common use and the data they are typically used for, along with the canonical link functions and their inverses (sometimes referred to as the mean function, as done here).
In the cases of the exponential and gamma distributions, the domain of the canonical link function is not the same as the permitted range of the mean. In particular, the linear predictor may be positive, which would give an impossible negative mean. When maximizing the likelihood, precautions must be taken to avoid this. An alternative is to use a noncanonical link function.
In the case of the Bernoulli, binomial, categorical and multinomial distributions, the support of the distributions is not the same type of data as the parameter being predicted. In all of these cases, the predicted parameter is one or more probabilities, i.e. real numbers in the range