Package: qgcomp 2.19.6

Introduction

Table of Contents | Example 7: time-to-event analysis and parallel processing | Example 8: clustering | Example 9: partial effects | Example 10: multinomial outcomes | Example 11: sample weighting from, e.g. NHANES | Missing data, limits of detection and multiple imputation | Limits of detection | Multiple imputation | References | Acknowledgments

Table of Contents | Introduction | The model | How to use the qgcomp package | Example 1: linear model | Example 2: conditional odds ratio, marginal odds ratio in a logistic model | Example 3: adjusting for covariates, plotting estimates | Example 4: non-linearity (and non-homogeneity) | Aside: some details on qgcomp methods for non-linearity | Interpretation of model parameters | Example 5: comparing model fits and further exploring non-linearity | Graphical approach to explore non-linearity in a correlated subset of exposures using splines | Caution about graphical approaches | Example 6: miscellaneous other ways to allow non-linearity | using indicator terms for each quantile | interactions between indicator terms | breaks at specific quantiles (these breaks act on the quantized basis) | Statistical approach explore non-linearity in a correlated subset of exposures using splines | FAQ | Why don't I get weights/scaled effects from the boot or ee functions? (and other questions about the weights/scaled effect sizes) | Do I need to model non-linearity and non-additivity of exposures? | Do I have to use quantiles? | Can I cite this document? | Where else can I get help? | References | Acknowledgments