pcaLDA {MethylIT} | R Documentation |

The principal components (PCs) for predictor variables provided as input data are estimated and then the individual coordinates in the selected PCs are used as predictors in the LDA

Predict using a PCA-LDA model built with function 'pcaLDA'

pcaLDA(formula = NULL, data = NULL, grouping = NULL, n.pc = 1, scale = FALSE, center = FALSE, tol = 1e-04, method = "moment", max.pc = NULL) ## S3 method for class 'pcaLDA' predict(object, newdata, type = c("lda.pred", "class", "posterior", "scores", "pca.ind.coord"), ...)

`formula` |
Same as in 'lda'from pakage 'MASS'. |

`data` |
Same as in 'lda'from pakage 'MASS'. |

`grouping` |
Same as in 'lda' from pakage 'MASS'. |

`n.pc` |
Number of principal components to use in the LDA. |

`scale` |
Same as in 'prcomp' from pakage 'prcomp'. |

`center` |
Same as in 'prcomp' from pakage 'prcomp'. |

`tol` |
Same as in 'prcomp' from pakage 'prcomp'. |

`method` |
Same as in 'lda'from pakage 'MASS'. |

`max.pc` |
Same as in paramter 'rank.' from pakage 'prcomp'. |

`object` |
To use with function 'predict'. A 'pcaLDA' object containing a list of two objects: 1) an object of class inheriting from "lda" and 2) an object of class inheriting from "prcomp". |

`newdata` |
To use with function 'predict'. New data for classification prediction |

`type` |
To use with function 'predict'. . The type of prediction required. The default is "all" given by function 'predict.lda' from MASS package: 'class', 'posterior', and 'scores' (see ?predict.lda). |

`...` |
Not in use. |

The principal components (PCs) are obtained using the function 'prcomp' from R pacakage 'stats', while the LDA is performed using the 'lda' function from R package 'MASS'. The current application only uses basic functionalities of mentioned functions. As shown in the example, pcaLDA' function can be used in general classification problems.

Function 'pcaLDA' returns an object ('pcaLDA' class) consisting of list with two objects: 1) 'lda': an object of class 'lda' from package 'MASS'. 2) 'pca': an object of class 'prcomp' from package 'stats'. For information on how to use these objects see ?lda and ?prcomp.

data(iris) ld1 <- pcaLDA(formula = Species ~ Petal.Length + Sepal.Length + Sepal.Width, data = iris, n.pc = 1, max.pc = 2, scale = TRUE, center = TRUE) ## === Prediction === ## ld2 <- pcaLDA(formula = Species ~., data = iris, n.pc = 1, max.pc = 2, scale = TRUE, center = TRUE) set.seed(123) idx <- sample.int(150, 40) newdata <- iris[idx, 1:4] newdata.prediction <- predict(ld2, newdata = newdata) ## The confusion matrix x <- data.frame(TRUE.class = iris$Species[idx], PRED.class = newdata.prediction$class) table(x)

[Package *MethylIT* version 0.3.1 ]