Multivariate Methods Pattern Recognition and Hypothesis Testing
Goals in Multivariate Analysis Model building – predicting metric variable from others Predicting dichotomies and counts – generalized linear model Testing/predicting groups Reducing the number of dimensions Exploring count data
Goals, cont Distances between items, individuals, and assemblages Grouping cases - classification
Model Building Multiple Regression Single response variable and multiple explanatory variables Search for a parsimonious, meaningful model
Generalized Linear Model A generalization of the linear model uses a link function to connect the linear model and the response Logistic regression for predicting dichotomous data Poisson regression to predict counts
Testing Groups Discriminant Functions –Confirming groups defined on independent grounds –Matching new observations to existing groups –Applications – compositional analysis, sex determination, ethnicity –Problems – normal distributions assumed, sample size requirements
Reducing Dimensionality Principal Components –Many correlated variables –Observed variables approximate what we want to study – grouping variables –Applications - assemblage data, measurement data on artifacts –Problems – evaluating significance of results and interpretation
Patterns in Count Data Correspondence analysis –Examining variables and cases simultaneously –Applications – assemblage comparisons (sites, areas within sites, features) –Problems – Interpretation of the results
Measuring Distance Multidimensional scaling –Variables converted to distances between cases –Applications – measurements –Problems - Interpretation
Classification (Grouping) Cluster Analysis –Finding clusters in multi-dimensional space – grouping cases –Applications – assemblages, artifacts, features –Problems – “real” vs. created clusters, number of clusters