Colour palettes for colour blind people
Inference: Empty skeleton
Interval estimates: Generic function
Colour palettes for colour blind people
Regression coefficients: formulaR
Point estimates: For the heterogeneity parameter
Data generation: Sampling data of clinical trials
Running a computer experiment
Plotting performance: Box plot of mean width
Running a computer experiment
Steams of pivotal quantities of the regression coefficient
Plotting performance: Box plots for target value confidence-coverage
Example: Setting up the BCG-data set
Inference: Based on methods of moments and
maximum likelihood.
Data generation: Sampling data of clinical trials
Design: Gaussian responses (unknown heteroscedasticity)
Render plot: To PDF
Running a computer experiment -- Collect all the results
Point estimates: For the regression coefficients
Plotting performance: Scatter plot against heteroscedasticity
Plotting performance: Box plots for target value confidence-coverage
Plotting performance: Scatter plot against heterogeneity
Plotting performance: Scatter plot against heterogeneity
Example: Plotting study unbalances in group assignments
Pivotal distributions: Plot pivot density of the heterogeneity
The p_delta(eta) function.
Plotting performance: Box plots for target value confidence-coverage
Data generation: Gaussian-Gaussian model
Plotting performance: Box plots for standard deviation
Running a computer experiment -- Collect specific results
Running a computer experiment: Adding performance measures
Running a computer experiment
Interval estimates: Generic function
Plotting performance: Box plot of mean width
Example: Plotting the q- and p-function from the dissertation
Plotting performance: Scatter plots against heterogeneity
Render plot: To SVG
Inference: Based on generalised inference principles.
Inference: Based on methods of moments and
maximum likelihood.
Running a computer experiment: Adding performance measures
Running a computer experiment in batch mode
Pivotal distributions: Plot pivotal distribution of regression
coefficients
Data generation: Sampling data of clinical trials
Pivotal distributions: Extract pivots for heterogeneity
Regression coefficients: formulaL
Design: Binomial responses
Running a computer experiment: Adding performance measures
Data generation: Gaussian-Gaussian model
Pivotal distributions: Extract pivots for regression coefficients
Running a computer experiment: Adding performance measures
Pivotal distributions: Plot pivotal distribution of regression
coefficients
The q_delta(tau) function.
Plotting performance: Scatter plots against heterogeneity
Plotting performance: Scatter plot against heterogeneity
Example: Plotting interval estimates
Plotting performance: Scatter plot against heteroscedasticity
Example: Plotting a forest plot of a data frame
Plotting performance: Box plots for bias
Design: Gaussian responses (known heteroscedasticity)
Pivotal distributions: Plot pivotal distribution of regression
coefficients
Example: Plotting study sizes
Interval estimates: For the regression coefficients
Data generation: Log-risk-ration of a binomial-Gaussian model
Pivotal distributions: Plot pivotal distribution of heterogeneity
Plotting performance: Scatter plot against heterogeneity
Inference: Analysis of the data set
Plot pivots: Interval estimates of the heterogeneity
Plotting performance: Scatter plots against heterogeneity
plotHeterogeneityInterval
Plot pivots: Interval estimates of the heterogeneity
Plotting performance: Box plots for mean squared error
Plotting performance: Density estimate of mean width
Plotting performance: Density estimate of mean width
Pivotal distributions: Plot pivotal distribution of regression
coefficients