Available Applications

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1. Read_Data

Data must be uploaded in .csv format. Any "data cleaning" should be done outside of the tool. 

In particular, the tool may not recognize all "n/a" indicators. We recommend such fields be left as blanks, or the records omitted entirely.

There are a few options on the lhs in respect of separators, decimal points, etc.

Based on user experience, we recommend short variables names, particularly for the compositional covariates where it is important that it is possible to distinguish between two compositional covariates by the first two letters of the variable name.

Compositional covariates should use a common scale, e.g. minutes, hours, proportion of the day, but it does not matter which.

Once the data is uploaded it will display on the right hand side of the screen. Depending on the size of your data it may be necessary to scroll right to see all of the fields.

Identify the response variable / outcome you are interested in on the bottom left. You can only consider one at a time.

If you just want to try out the tool without using your own data there is a simple dummy dataset below.

2. Covariates

The next stage is to identify the covariates / confounding variables you would like to include in the model. You will need to indicate continuous and categorical variables separately to enable the tool to identify variable types.

The tool includes some limited functionality for selecting covariates for inclusion (AIC, and likelihood ratio tests on the effect of dropping individual variables) displayed on the right. Changing the selected covariates will cause these calculations to update automatically.

3. ILR Coordinates

You should select the compositional variables in your data you would like to examine the composition of.

If there are zeroes in the data you will need to either remove them manually outside of the tool, or use the imputation functionality to replace the zeroes with conditionally (less than detection threshold) imputed values.

If you use the imputation functionality then select the detection limits by component, e.g.

• if sleep time is reported to nearest hour and data is given in minutes then the detection limit is 0.5*60 = 30 (the smallest time that will be distinguished from zero);
• if MVPA is measured on an accelerometer using 1 minute epochs, and data is given in proportion of the day, then the detection limit is 1/60/24 = 0.00069444;

Once you have identified the compositional variables, imputed as needed, and given the tool a little time to compute, it will provide a list of the available transformed CoDa variables for carrying out the regression.

These are calculated using the "robcompositions" package in R.

We have used a similar naming convention to robcompositions:

"_" indicates division, "." indicates geometric averaging, so for example "SB_MV.LI" would indicate the ilr coordinate is based on the logratio between "SB", and the geometric average of "MV" and "LI." The original variable names are often abbreviated in the ilr coordinate names.

Currently the tool has been built to handle 3 or 4 components. In addition, the list of ilr coordinates for 4 components is restricted to those built on the pivot coordinate approach. We hope to expand the functionality to higher dimensions, and a more general set of balances, in the near future.

The ilr coordinates for individual members are shown on the right hand side.

Basic model selection statistics (AIC, Likelihood Ratio Test p-values) for the complete compositional model with covariates are shown on the right hand side.

Regression coefficients are shown on the right hand side.

4. Diagnostics

Basic diagnostic plots and the coefficient of determination (as for standard linear regression models) are displayed here. No inputs.

5. Comparator/Null Hypothesis

On this tab you can compare two compositional linear regression models using a likelihood ratio test. Typically we would use this functionality to test inclusion / exclusion of multiple covariates, for example testing the significance of the covarites + compositional variables against just the covariates to determine the statistical significance of the overall composition.

6. Forecasting

It will display (given sufficient time) a ternary heat map of the expected outcomes forecast by the model. It will impose data points over the heat map if requested. You can adjust the covariates on the left hand side.

Due to the limitations of the ternary plot, you can only look at 3 compositional variables at one time. For 4d compositions, we recommend producing 4 ternary plots of each possible 3-d subcomposition.