Analysis of Categorical Data with RLearn How to Properly Analyze Categorical DataAnalysis of Categorical Data with R presents a modern account of categorical data analysis using the popular R software. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability est |
Contents
Analyzing a binary response part 1 introduction | 1 |
Analyzing a binary response part 2 regression models | 61 |
Analyzing a multicategory response | 141 |
Analyzing a count response | 195 |
Model selection and evaluation | 265 |
Additional topics | 355 |
An introduction to R | 473 |
Likelihood methods | 495 |
| 513 | |
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Common terms and phrases
algorithm analysis anova approximation argument value association binary binomial binomial(link calculate chi-square coefficients comparing compute contingency table corresponding program counts data frame data set density discussed distance Equation error estimated odds ratio estimated probability EVPs example explanatory variables free throw given glm(formula inference interaction Intercept interpretation iterations likelihood function linear log-likelihood logistic regression logistic regression model matrix mean methods mod.fit model fit normal distribution null hypothesis observed odds ratios output overdispersion p-value package parameter estimates Pearson residuals perform placekick plot Poisson distribution Poisson regression probability of success problem procedures profile LR proportional odds quantiles random effects random variable regression model regression parameters residual deviance response variable sample saturated model scores Section simulation specified test statistic trials true confidence level variance Wald confidence interval Wald interval Wald test weights zero β₁ βο