Jun 12, 2019 · where again \( \alpha \) and \( \beta \) are two variables we’ll be trying to fit, corresponding to the shape and rate parameters of the Gamma variational posterior. Each of the 3 mixture components also has a weight, such that all 3 weights sum to 1. We’ll use a vector, \( \theta \), to represent these weights. A support vector machine (SVM) is a supervised learning algorithm that can be used for binary classification or regression. Support vector machines are popular in applications such as natural language processing, speech and image recognition, and computer vision. Arguments fit. a fit of class "rms". a. a list containing settings for all predictors that you do not wish to set to default (adjust-to) values. Usually you will specify two variables in this list, one set to a constant and one to a sequence of values, to obtain contrasts for the sequence of values of an interacting factor. Finally R has a wide range of goodness of fit tests for evaluating if it is reasonable to assume that a random sample comes from a specified theoretical distribution. These include chi-square, Kolmogorov-Smirnov, and Anderson-Darling. For more details on fitting distributions, see Vito Ricci's Fitting Distributions with R.