glht

glht stands for generalized linear hypothesis testing. It is a statistical method used to test hypotheses about the parameters of generalized linear models. Generalized linear models (GLMs) are a flexible class of statistical models that extend linear regression to response variables with error distribution models other than a normal distribution, and to regression models in which the response variable is related to the explanatory variables by a link function. Examples of GLMs include logistic regression for binary outcomes and Poisson regression for count data.

The glht function in statistical software, particularly in the R programming language, provides a unified framework for fitting and testing hypotheses in GLMs. It allows users to specify complex hypotheses that go beyond simple coefficient tests. These hypotheses can involve linear combinations of regression coefficients or comparisons between coefficients. For instance, one might want to test if the effect of one predictor is significantly different from the effect of another predictor, or if a specific linear combination of parameters equals a certain value.

The glht function can handle various types of hypothesis tests, including equality constraints, inequality constraints, and combinations thereof. It computes test statistics and p-values, enabling researchers to draw conclusions about the significance of their hypotheses. This approach is valuable for exploring relationships within GLMs in a more nuanced and flexible manner than standard significance tests often allow. It is widely used in fields such as biostatistics, econometrics, and social sciences.