nutrigenomic {randomForestSRC} | R Documentation

## Nutrigenomic Study

### Description

Study the effects of five diet treatments on 21 liver lipids and 120 hepatic gene expression in wild-type and PPAR-alpha deficient mice. We use a multivariate mixed random forest analysis by regressing gene expression, diet and genotype (the x-variables) on lipid expressions (the multivariate y-responses).

### References

Martin P.G. et al. (2007). Novel aspects of PPAR-alpha-mediated regulation of lipid and xenobiotic metabolism revealed through a nutrigenomic study. Hepatology, 45(3), 767–777.

### Examples

    ## ------------------------------------------------------------
    ## multivariate mixed forests
    ## lipids used as the multivariate y-responses
    ## ------------------------------------------------------------

    ## load the data
    data(nutrigenomic, package = "randomForestSRC")

    ## multivariate mixed forest call
    mv.obj <- rfsrc(get.mv.formula(colnames(nutrigenomic$lipids)),
                data.frame(do.call(cbind, nutrigenomic)),
                importance=TRUE, nsplit = 10)

    ## ------------------------------------------------------------
    ## plot the standarized performance and VIMP values
    ## ------------------------------------------------------------

    ## acquire the error rate for each of the 21-coordinates
    ## standardize to allow for comparison across coordinates
    serr <- get.mv.error(mv.obj, standardize = TRUE)

    ## acquire standardized VIMP
    svimp <- get.mv.vimp(mv.obj, standardize = TRUE)

    par(mfrow = c(1,2))
    plot(serr, xlab = "Lipids", ylab = "Standardized Performance")
    matplot(svimp, xlab = "Genes/Diet/Genotype", ylab = "Standardized VIMP")

[Package randomForestSRC version 2.9.3 Index]