G-test

In statistics, G-tests are likelihood-ratio or maximum likelihood statistical significance tests that are increasingly being used in situations where chi-squared tests were previously recommended.

Formulation

The general formula for test statistics of the G-test is

$G=2\sum _i{O}_i\cdot \ln \left(\frac{O_i}{E_i}\right),$

where $O_i\ge 0$ is the observed count in a cell, $E_i>0$ is the expected count under the null hypothesis, $\ln$ denotes the natural logarithm, and the sum is taken over all non-empty cells. The resulting $G$ is asymptotically chi-squared distributed as the total number of observations tends to infinity (convergence in distribution).

Furthermore, the total observed count must be equal to the total expected count:

$\sum _i{O}_i=\sum _i{E}_i=N,$

where $N$ is the total number of observations.

Both, the G-test statistics $G$ and the chi-square test statistics ${\chi }^2$ are special cases of a general family of power divergence statistics by Cressie and Read. For $\lambda \notin \{0,-1\}$ set

${\mathrm{CR}}_{\lambda }=\frac{2}{\lambda (\lambda +1)}\sum _i{O}_i({\left(\frac{O_i}{E_i}\right)}^{\lambda }-1).$

Then,

$G=\underset{\lambda \to 0}{lim}{\mathrm{CR}}_{\lambda },{\chi }^2={\mathrm{CR}}_1.$

Derivation

We can derive the value of the G-test from the log-likelihood ratio test where the underlying model is a multinomial model.

Suppose we had a sample $O=(O_1,\dots ,O_m)$ where each $O_i$ is the number of times that an object of type $i$ was observed. Furthermore, let $N=\sum _{i=1}^m{O}_i$ be the total number of observations. If we assume that the underlying model is multinomial, then the test statistic is defined by

$\ln \left(\frac{L(\tilde{p}|O)}{L(\hat{p}|O)}\right)=\ln \left(\frac{\prod _{i=1}^m{\tilde{p}}_i^{O_i}}{\prod _{i=1}^m{\hat{p}}_i^{O_i}}\right),$

where $\tilde{p}=({\tilde{p}}_1,\dots ,{\tilde{p}}_m)$ is the null hypothesis and $\hat{p}=({\hat{p}}_1,\dots ,{\hat{p}}_m)$ is the maximum likelihood estimate (MLE) of the parameters given the data. Recall that for the multinomial model, the MLE of ${\hat{p}}_i$ given some data is given by

${\hat{p}}_i=\frac{O_i}{N}.$

Furthermore, we may represent each null hypothesis parameter ${\tilde{p}}_i$ as

${\tilde{p}}_i=\frac{E_i}{N},$

where $E_i$ is the expected count of objects of type $i$ under the null hypothesis. Thus, by substituting the representations of ${\tilde{p}}_i$ and ${\hat{p}}_i$ in the log-likelihood ratio, the equation simplifies to

$\ln \left(\frac{L(\tilde{p}|O)}{L(\hat{p}|O)}\right)=\ln \left(\prod _{i=1}^m{\left(\frac{E_i}{O_i}\right)}^{O_i}\right)=\sum _{i=1}^m{O}_i\ln \left(\frac{E_i}{O_i}\right)$

Finally, multiply by a factor of $-2$ (used to make the G-test formula asymptotically equivalent to the Pearson's chi-squared test statistics) to achieve the form

$G=-2\sum _{i=1}^m{O}_i\ln \left(\frac{E_i}{O_i}\right)=2\sum _{i=1}^m{O}_i\ln \left(\frac{O_i}{E_i}\right)$

Heuristically, one can imagine $O_i$ as continuous and approaching zero, in which case $O_i\ln O_i\to 0$, and terms with zero observations can simply be dropped. However the expected count in each cell must be strictly greater than zero for each cell ($E_i>0$ for all $i$) to apply the method.

Distribution and use

Given the null hypothesis that the observed frequencies result from random sampling from a distribution with the given expected frequencies, the distribution of the test statistics $G$ is approximately a chi-squared distribution, with the same number of degrees of freedom as in the corresponding chi-squared test.

For very small samples the multinomial test for goodness of fit, and Fisher's exact test for contingency tables, or even Bayesian hypothesis selection are preferable to the G-test. McDonald recommends to always use an exact test (exact test of goodness-of-fit, Fisher's exact test) if the total sample size is less than 1 000 .

There is nothing magical about a sample size of 1 000, it's just a nice round number that is well within the range where an exact test, chi-square test, and G–test will give almost identical Template:Mvar values. Spreadsheets, web-page calculators, and SAS shouldn't have any problem doing an exact test on a sample size of 1 000 .

— John H. McDonald (2014)

G-tests have been recommended at least since the 1981 edition of Biometry, a statistics textbook by Robert R. Sokal and F. James Rohlf.

Relation to other metrics

Relation to the chi-squared test

The commonly used chi-squared tests for goodness of fit to a distribution and for independence in contingency tables are in fact approximations of the log-likelihood ratio on which the G-tests are based.

The general formula for Pearson's chi-squared test statistic is

${\chi }^2=\sum _i\frac{{(O_i-E_i)}^2}{E_i}.$

The approximation of the G-test statistics by chi-squared test statistics is obtained by a second order Taylor expansion of the natural logarithm around 1 (see the derivation below). We have $G\approx {\chi }^2$ when the observed counts $O_i$ are close to the expected counts $E_i$. When this difference is large, however, the approximation by the chi-squared test statistics begins to break down. Here, the effects of outliers in data will be more pronounced, and this explains the why chi-squared tests fail in situations with little data.

For samples of a reasonable size, the G-test and the chi-squared test will lead to the same conclusions. However, the approximation to the theoretical chi-squared distribution for the G-test is better than for the Pearson's chi-squared test. In cases where $O_i>2\cdot E_i$ for some cell case the G-test is always better than the chi-squared test.Template:Citation needed

For testing goodness-of-fit the G-test is infinitely more efficient than the chi-squared test in the sense of Bahadur, but the two tests are equally efficient in the sense of Pitman or in the sense of Hodges and Lehmann.

Derivation (chi-squared)

Consider

$G=2\sum _i{O}_i\ln \left(\frac{O_i}{E_i}\right),$

and let $O_i=E_i+{\delta }_i$ with ${\sum }_i{\delta }_i=0$, so that the total number of counts remains the same. Assume that ${\delta }_i=O_i-E_i$ is small in comparison to $E_i$ for all $i$. To be more precise, notice that $E_i=\mathrm{\Theta }(n)$ using big Θ notation. If $O_i=E_i+𝒪(n^{1/2})$ using big O notation for large $n$, which should be true under the null hypothesis because of the central limit theorem, then ${\delta }_i=𝒪(n^{1/2})$ and

$\frac{{\delta }_i^3}{E_i^2}=𝒪\left(\frac{n^{3/2}}{n^2}\right)=𝒪(n^{-1/2})$

follow, which will be used later.

Upon substitution we find,

$G=2\sum _i(E_i+{\delta }_i)\ln (1+\frac{{\delta }_i}{E_i}).$

Using the Taylor expansion $\ln (1+x)=x-\frac{1}{2}{x}^2+𝒪(x^3)$ yields

$G=2\sum _i(E_i+{\delta }_i)(\frac{{\delta }_i}{E_i}-\frac{1}{2}\frac{{\delta }_i^2}{E_i^2}+𝒪\left(\frac{{\delta }_i^3}{E_i^3}\right)),$

and distributing terms we find,

$G=2\sum _i({\delta }_i+\frac{1}{2}\frac{{\delta }_i^2}{E_i}+𝒪\left(\frac{{\delta }_i^3}{E_i^2}\right)).$

Now, using ${\sum }_i{\delta }_i=0$ and ${\delta }_i=O_i-E_i$ and $𝒪({\delta }_i^3/E_i^2)=𝒪(n^{-1/2})$ for large $n$, we can write the result,

$G\approx \sum _i\frac{{(O_i-E_i)}^2}{E_i}.$

Relation to Kullback–Leibler divergence

The G-test statistic is proportional to the Kullback–Leibler divergence of the theoretical distribution $\tilde{p}=({\tilde{p}}_1,\dots ,{\tilde{p}}_m)$ of the null hypothesis from the empirical distribution $\hat{p}=({\hat{p}}_1,\dots ,{\hat{p}}_m)$ of the observed data:

$\begin{array}{cc}G& =2\sum _i{O}_i\cdot \ln \left(\frac{O_i}{E_i}\right)=2N\sum _i{\hat{p}}_i\cdot \ln \left(\frac{{\hat{p}}_i}{{\tilde{p}}_i}\right)\\ & =2N{D}_{\mathrm{K}\mathrm{L}}(\hat{p}\Vert \tilde{p}),\end{array}$

where $N$ is the total number of observations and ${\tilde{p}}_i=\frac{E_i}{N}$ and ${\hat{p}}_i=\frac{O_i}{N}$ are the theoretical and empirical probabilities of objects of type $i$, respectively.

Relation to mutual information

For analysis of contingency tables the value of the G-test statistics can also be expressed in terms of mutual information.

In this case objects with two-dimensional types $(i,j)$ are considered. Let $O_{ij}$ be the count of objects of type $(i,j)$, i.e., $O_{ij}$ is the entry in the contingency table in row $i$ and column $j$. Set

$N=\sum _{ij}{O}_{ij},{\hat{p}}_{ij}=\frac{O_{ij}}{N},{\hat{p}}_{i\bullet }=\frac{\sum _j{O}_{ij}}{N},{\hat{p}}_{\bullet j}=\frac{\sum _i{O}_{ij}}{N}.$

Then the estimated expected count of objects of type $(i,j)$ assuming independence is given by

$E_{ij}=N{\hat{p}}_{i\bullet }{\hat{p}}_{\bullet j}.$

Finally, the G-test statistics in this case is given by

$G=2\sum _{ij}{O}_{ij}\ln \left(\frac{O_{ij}}{E_{ij}}\right)$

Let $X,Y$ be random variables with joint distribution given by the empirical distribution ${\hat{p}}_{ij}$ of the contingency table, i.e.,

$P(X=i,Y=j)={\hat{p}}_{ij},P(X=i)={\hat{p}}_{i\bullet },P(Y=j)={\hat{p}}_{\bullet j}.$

Then the G-test statistics can be expressed in several alternative forms:

$\begin{array}{cc}G& =2N\cdot \sum _{ij}{\hat{p}}_{ij}(\ln ({\hat{p}}_{ij})-\ln ({\hat{p}}_{i\bullet })-\ln ({\hat{p}}_{\bullet j}))\\ & =2N\cdot (H(X)+H(Y)-H(X,Y))\\ & =2N\cdot \mathrm{MI}(X,Y),\end{array}$

where the entropies $H(X)$ and $H(Y)$ are given

$H(X)=-\sum _i{\hat{p}}_{i\bullet }\ln ({\hat{p}}_{i\bullet }),H(Y)=-\sum _j{\hat{p}}_{\bullet j}\ln ({\hat{p}}_{\bullet j})$

and the joint entropy $H(X,Y)$ is given by

$H(X,Y)=-\sum _{ij}{\hat{p}}_{ij}\ln ({\hat{p}}_{ij})$

and the mutual information of $X$ and $Y$ is

$\mathrm{MI}(X,Y)=H(X)+H(Y)-H(X,Y).$

It can also be shownTemplate:Citation needed that the inverse document frequency weighting commonly used for text retrieval is an approximation of G applicable when the row sum for the query is much smaller than the row sum for the remainder of the corpus. Similarly, the result of Bayesian inference applied to a choice of single multinomial distribution for all rows of the contingency table taken together versus the more general alternative of a separate multinomial per row produces results very similar to the G-test statistic.Template:Citation needed

Application

• The McDonald–Kreitman test in statistical genetics is an application of the G-test.
• Dunning introduced the test to the computational linguistics community where it is now widely used.
• The R-scape program (used by Rfam) uses G-test to detect co-variation between RNA sequence alignment positions.

Statistical software

• In R fast implementations can be found in the AMR and Rfast packages. For the AMR package, the command is g.test which works exactly like chisq.test from base R. R also has the likelihood.test Template:Webarchive function in the Deducer Template:Webarchive package. Note: Fisher's G-test in the GeneCycle Package of the R programming language (fisher.g.test) does not implement the G-test as described in this article, but rather Fisher's exact test of Gaussian white-noise in a time series.
• Another R implementation to compute the G-test statistic and corresponding p-values is provided by the R package entropy. The commands are Gstat for the standard G statistic and the associated p-value and Gstatindep for the G statistic applied to comparing joint and product distributions to test independence.
• In SAS, one can conduct G-test by applying the /chisq option after the proc freq.
• In Stata, one can conduct a G-test by applying the lr option after the tabulate command.
• In Java, use org.apache.commons.math3.stat.inference.GTest.
• In Python, use scipy.stats.power_divergence with lambda_=0.

External links

• G²/Log-likelihood calculator