MLPowSim User Manual

The following are output scripts generated from the examples in the MLPowSim User Manual:

• 1.3.2: Running MLPowSim for a simple example analyse.txt graphs.txt setup.txt simu.txt
• 1.5: Introduction to R and MLPowSim powersimu.r
• 2.2.1: Testing for differences between two groups (a) analyse.txt graphs.txt setup.txt simu.txt
• 2.2.1: Testing for differences between two groups (b) analyse.txt graphs.txt setup.txt simu.txt
• 2.2.2: Testing for a significant continuous predictor analyse.txt graphs.txt setup.txt simu.txt
• 2.2.3a: Fitting a multiple regression model (a) analyse.txt graphs.txt setup.txt simu.txt
• 2.2.3b: Fitting a multiple regression model (b) analyse.txt graphs.txt setup.txt simu.txt
• 2.2.5: Using RIGLS analyse.txt graphs.txt setup.txt simu.txt
• 2.2.6: Using MCMC estimation analyse.txt graphs.txt setup.txt simu.txt
• 2.2.7: Using R powersimu.r
• 2.3.1: The Design Effect formula analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 2.3.3: Multilevel two sample t-test example (a) analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 2.3.3: Multilevel two sample t-test example (b) analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 2.3.4: Higher level predictor variables analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 2.3.5: A model with 3 predictors analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 2.3.6.1: Pupil non-response (a) analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 2.3.6.1: Pupil non-response (b) analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 2.3.6.2: Structured sampling analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 2.4: Random slopes/ Random coefficient models (a) analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 2.4: Random slopes/ Random coefficient models (b) analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 2.5.1: Balanced 3-level models - The ILEA dataset analyse.txt graphs.txt setup.txt simu.txt simu2.txt simu3.txt
• 2.5.2: Non-response at the first level in a 3-level design analyse.txt graphs.txt setup.txt simu.txt simu2.txt simu3.txt
• 2.5.3: Non-response at the second level in a 3-level design analyse.txt graphs.txt setup.txt simu.txt simu2.txt simu3.txt
• 2.5.4: Individually chosen sample sizes at level 1 analyse.txt graphs.txt setup.txt simu.txt simu2.txt simu3.txt
• 2.6.1: Balanced cross-classified models (a) powersimu.r
• 2.6.1: Balanced cross-classified models (b) powersimu.r
• 2.6.1: Balanced cross-classified models (c) powersimu.r
• 2.6.2: Non-response of single observations powersimu.r
• 2.6.3: Dropout of whole groups powersimu.r
• 2.6.4: Unbalanced designs – sampling from a pupil lookup table powersimu.r
• 2.6.5: Unbalanced designs - sampling from lookup tables for each primary/secondary school (a) powersimu.r fife.txt
• 2.6.5: Unbalanced designs - sampling from lookup tables for each primary/secondary school (b) powersimu.r fife2.txt
• 2.6.6: Using MCMC in MLwiN for cross-classified models (a) analyse.txt genresp.txt graphs.txt setup.txt simu.txt simu2.txt simu3.txt
• 2.6.6: Using MCMC in MLwiN for cross-classified models (b) analyse.txt genresp.txt graphs.txt setup.txt simu.txt simu2.txt simu3.txt
• 3.3.1: A single proportion in the logistic regression framework analyse.txt graphs.txt setup.txt simu.txt
• 3.3.2: Comparing two proportions in the logistic regression framework analyse.txt graphs.txt setup.txt simu.txt
• 3.4: Multilevel logistic regression models (a) analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 3.4: Multilevel logistic regression models (b) analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 3.5: Multilevel logistic regression models in R powersimu.r
• 4.3: Poisson log-linear regressions analyse.txt graphs.txt setup.txt simu.txt
  
• 4.3.1: Using R powersimu.r
• 4.4: Random effect Poisson regressions analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 4.5: Further thoughts on Poisson data analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 5.1: An example using MLwiN analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 5.2.1: Initial macros analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 5.2.2: Creating a multiple category predictor analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 5.2.3: Linking gender to school gender analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 5.2.4: Performing a deviance test analyse.txt graphs.txt setup.txt simu.txt simu2.txt
• 5.3.2: The output file produced by R: powerout.txt powersimu.r
• 5.4.1: Initial changes powersimu.r
• 5.4.2: Creating a multiple category predictor powersimu.r
• 5.4.3: Linking gender to school gender powersimu.r
• 5.4.4: Performing the deviance test powersimu.r
• 5.5: The Wang and Gelfand (2002) method (a) analyse.txt graphs.txt setup.txt simu.txt
  
• 5.5: The Wang and Gelfand (2002) method (b) analyse.txt graphs.txt setup.txt simu.txt