# dbayes: Using bias method to distinguish classes In WMDB: Discriminant Analysis Methods by Weight Mahalanobis Distance and bayes

## Description

using bias method to distinguish classes

## Usage

 `1` ```dbayes(TrnX, TrnG, p = rep(1, length(levels(TrnG))), TstX = NULL, var.equal = FALSE) ```

## Arguments

 `TrnX` matrix or data frame of training set cases. `TrnG` vector of factors of the samples `p` vector of prior probability of samples `TstX` matrix or data frame of test set cases. A vector will be interpreted as a row vector for a single case. `var.equal` whether class have the same covariance or not

## Details

the distribution of samples shuold be normal distribution

## Value

result of classifications of test set will be returned. (When TstX is NULL, the function will automatically consider the user is trying to test the Discriminant Analysis Methods by weight Mahalanobis distance. Hence, a test result table and accuracy report will be shown on the R-console.)

Bingpei Wu

## References

Statistical modeling and R software,whose author is Yi Xue

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62``` ```##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. X<-iris[,1:4] G<-gl(3,50) dbayes(X,G) ## The function is currently defined as function (TrnX, TrnG, p = rep(1, length(levels(TrnG))), TstX = NULL, var.equal = FALSE) { if (is.factor(TrnG) == FALSE) { mx <- nrow(TrnX) mg <- nrow(TrnG) TrnX <- rbind(TrnX, TrnG) TrnG <- factor(rep(1:2, c(mx, mg))) } if (is.null(TstX) == TRUE) TstX <- TrnX if (is.vector(TstX) == TRUE) TstX <- t(as.matrix(TstX)) else if (is.matrix(TstX) != TRUE) TstX <- as.matrix(TstX) if (is.matrix(TrnX) != TRUE) TrnX <- as.matrix(TrnX) nx <- nrow(TstX) blong <- matrix(rep(0, nx), nrow = 1, dimnames = list("blong", 1:nx)) g <- length(levels(TrnG)) mu <- matrix(0, nrow = g, ncol = ncol(TrnX)) for (i in 1:g) mu[i, ] <- colMeans(TrnX[TrnG == i, ]) D <- matrix(0, nrow = g, ncol = nx) if (var.equal == TRUE || var.equal == T) { for (i in 1:g) { d2 <- mahalanobis(TstX, mu[i, ], var(TrnX)) D[i, ] <- d2 - 2 * log(p[i]) } } else { for (i in 1:g) { S <- var(TrnX[TrnG == i, ]) d2 <- mahalanobis(TstX, mu[i, ], S) D[i, ] <- d2 - 2 * log(p[i]) - log(det(S)) } } for (j in 1:nx) { dmin <- Inf for (i in 1:g) if (D[i, j] < dmin) { dmin <- D[i, j] blong[j] <- i } } print(blong) print("num of wrong judgement") print(which(blong != TrnG)) print("samples divided to") print(blong[which(blong != TrnG)]) print("samples actually belongs to") print(TrnG[which(blong != TrnG)]) print("percent of right judgement") print(1 - length(which(blong != TrnG))/length(blong)) } ```

WMDB documentation built on May 2, 2019, 6:12 a.m.