MangroveContPreds: The MangroveContPreds class
In Mangrove: Risk Prediction on Trees

Description Usage Arguments Details Value See Also Examples

Methods to generate, summarise and apply MangroveContPreds objects

calcBetas(ped,betas)
applyBetas(contpred,mu,sigma)

## S3 method for class 'MangroveContPreds'
print(x, ...)
## S3 method for class 'MangroveContPreds'
summary(object, ...)
## S3 method for class 'MangroveContPreds'
plot(x, ...)

`ped`	A `MangrovePed` object
`betas`	A `MangroveBetas` object
`contpred`	A `MangroveContPreds` object
`object`	A `MangroveContPreds` object
`x`	A `MangroveContPreds` object
`mu`	The population mean of the quantitative trait being predicted
`sigma`	The population standard deviation of the quantitative trait being predicted
`...`	Arguments to be passed on to downstream methods.

MangroveContPreds objects are used to hold quantitative (or continuous) trait predictions for a number of individuals. This object contains the beta value for the trait (i.e. the person's value of the trait on a scale with the population mean being zero and the population standard deviation being one). You can generate per-individual beta values from a pedigree object and some per-variant beta values using calcBetas, and you can transform these to quantitative trait predictions given a population mean and standard deviation using applyBetas.

The print method writes the beta value for each individual to screen. The summary method gives some quantile information, and the plot method plots a histogram of predicted values, which should be approximately normal.

calcBetas returns a MangroveContPreds object, applyBetas returns a numeric vector containing posterior probabilities. The methods do not return anything.

MangroveRiskPreds for doing risk prediction for binary traits.

data(contped)
data(exampleBetas)
predbetas <- calcBetas(contped,exampleBetas)

summary(predbetas)
plot(predbetas)

contpreds <- applyBetas(predbetas,162,6.4)