1 The aim of the course is to show how to fit a range of regression models,
   2 their interpretation and useful output. We start off looking at data file types
   3 and how they are read into R. We will illustrate statistical inference using the
   4 likelihood ratio test to assess the importance of factors and covariates on outcomes in a 
   5 variety of data sets. The emphasis is on hypothesis testing to answer questions of
   6 interest to researchers mostly involving group differences.
   7 
   8 SCHEDULE:
   9 
  10 INTRODUCTION:
  11 
  12 Looking at central themes such as data files and how to read these into R.
  13 I will also mention use of libraries to access functions which are not in the core library.
  14 
  15 DATA FILE TYPES
  16 ACCESSING DATA FILES 
  17 DATA FRAMES
  18 
  19 WE WILL BE USING R TO FIT THE FOLLOWING CLASSES OF REGRESSION MODELS:
  20 
  21 GENERAL LINEAR MODEL: 
  22 
  23 We illustrate these models using Crime data and naming task data.
  24 
  25 T-TEST & MULTIPLE REGRESSION (GLM in R.txt), 
  26 ILLUSTRATING THE PLOT FUNCTION WITH ANCOVA (regression two line group plot+int.txt)
  27 BETWEEN SUBS ANOVA (Banova.txt)
  28 
  29 GENERALISED LINEAR MODELS: (CHISQ IN R Sept 2022.txt)
  30 
  31 2x2 tables of frequencies looking at proportions of people living in different regions in America.
  32 
  33 PEARSON CHI-SQUARE INCLUDING COMPUTING AND INTEPRETING CELL RESIDUALS
  34 STATISTICAL INFERENCE USING LIKELIHOOD RATIO TEST
  35 LOGISTIC REGRESSION; FITTING AND INTERPRETATION OF OUTPUT, 
  36 POISSON REGRESSION AND COMPARISON WITH LOGISTIC REGRESSION
  37 
  38 GENERALISED LINEAR MIXED MODELS (R COURSE LMER.txt)
  39 
  40 We use beck depression data from a randomised clinical trial here.
  41 
  42 USING AND COMPARING LME AND LMER ON REPEATED MEASURES DATA
  43 INTERPRETING THE OUTPUT
  44 OBTAINING PREDICTED MEANS FOLLOWING FITTING OF LINEAR MIXED MODEL
  45 SHOWING THE EQUIVALENCE OF PREDICTED MEANS AND REGRESSION COEFFICIENTS FROM THE LINEAR MIXED MODEL
  46 NORMAL PROBABILITY PLOT TO CHECK MODEL ASSUMPTIONS
  47 SHOWING MEAN DIFFERENCES WITH BOXPLOTS
  48 
  49 REPEATED MEASURES ANOVA: (EZANOVA no input group.txt)
  50 
  51 Comparing changes over time in reactions to moods between control and patient groups.
  52 
  53 TWO WITHIN SUBJECT FACTOR ANOVA
  54 TWO WITHIN SUBJECT FACTOR AND ONE BETWEEN SUBJECT FACTOR ANOVA
  55 GREENHOUSE-GEISSER CORRECTIONS
  56 
  57 POST-HOCS VIA T-TESTS FOLLOWING A REPEATED MEASURES ANOVA (POST HOCS IN R.txt)