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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,141 +1,156 @@ | ||
| ## FIEmspro https://github.com/aberHRML/FIEmspro nlda functionality | ||
| ## Based on FIEmspro https://github.com/aberHRML/FIEmspro nlda functionality | ||
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| #'@importFrom e1071 naiveBayes | ||
| #'@importFrom stats cov predict | ||
| setGeneric('nlda',function(x,cls = 'class',prior = NULL,scale = FALSE,comprank = FALSE,...) | ||
| standardGeneric('nlda')) | ||
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| `nlda.default` <- | ||
| function(dat,cl, prior=NULL,scale=FALSE,comprank = FALSE,...) | ||
| { | ||
| if (missing(dat) || missing(cl)) | ||
| stop("data set or class are missing") | ||
| dat <- as.matrix(dat) | ||
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| cl <- as.factor(cl) | ||
| if (any(table(cl) == 0)) stop("Can't have empty classes in cl.") | ||
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| if (nrow(dat) != length(cl)) stop("dat and cl don't match.") | ||
| if (length(unique(cl)) < 2) | ||
| stop("Classification needs at least two classes.") | ||
| if (any(is.na(dat)) || any(is.na(cl))) | ||
| stop("NA is not permitted in data set or class labels.") | ||
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| if(is.null(prior)){prior <- as.vector(table(cl)/length(cl))} | ||
| if(is.null(names(prior))){names(prior) <- levels(cl)} | ||
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| pc <- prcomp(dat,scale=scale) | ||
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| rankmat <- max(1,ncol(pc$x)-1) | ||
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| if(comprank == TRUE) | ||
| rankmat <- qr(cov(dat)*(dim(dat)[1]-1))$rank | ||
| score <- pc$x[,1:rankmat,drop=FALSE] | ||
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| g <- nlevels(cl) | ||
| mx <- apply(score,2,mean) | ||
| t <- matrix(0,nrow = rankmat,ncol=rankmat) | ||
| W <- matrix(0,nrow = rankmat,ncol=rankmat) | ||
| for(j in 1:g){ | ||
| idx <- which(cl==levels(cl)[j]) | ||
| L <- length(idx) | ||
| K <- score[idx,,drop=FALSE] | ||
| zz <- apply(K,2,mean) | ||
| A <- K - t(matrix(rep(mx, L),length(mx),L)) | ||
| C <- K - t(matrix(rep(zz, L),length(zz),L)) | ||
| t <- t + t(A)%*%A | ||
| W <- W + t(C)%*%C | ||
| } | ||
| B <- t-W | ||
|
|
||
| Ng <- nrow(score)-g | ||
| P <- W/(Ng) | ||
| eP <- eigen(P) | ||
| ord <- sort.list(eP$values) | ||
| V <- sweep( | ||
| eP$vectors[,ord,drop=FALSE], | ||
| 2, | ||
| sqrt(colSums(eP$vectors[,ord,drop=FALSE]^2)), | ||
| "/") | ||
| Dg <- eP$values[ord] | ||
| nDg <- length(Dg) | ||
| Dmean <- sum(diag(P))/nDg | ||
| Dn <- matrix(0,nDg,nDg) | ||
| for(i in 1:nDg) | ||
| Dn[i,i] <- max(c(Dg[i],Dmean)) | ||
|
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||
| Wn <- V%*%Dn%*%t(V)*Ng | ||
| ratio <- solve(Wn)%*%B | ||
| er <- eigen(ratio) | ||
| ev <- Re(er$values) | ||
| ev[Im(er$values)>0] <- 0 | ||
| vec <- Re(er$vectors) | ||
| ord <- sort.list(ev,decreasing=TRUE) | ||
| vec <- sweep( | ||
| vec[,ord,drop=FALSE], | ||
| 2, | ||
| sqrt(colSums(vec[,ord,drop=FALSE]^2)), | ||
| "/") | ||
| ev <- ev[ord] | ||
| maxg <- min(c(g-1,dim(vec)[1])) | ||
| vec <- vec[,1:maxg] ## discriminant functions | ||
| Tw <- ev[1:maxg] | ||
| names(Tw) <- paste("DF", 1:maxg, sep = "") | ||
|
|
||
| ## get stats here | ||
| flip <- function(x) x[rev(seq_along(x))] | ||
| n <- dim(dat)[1] | ||
| st <- matrix(0,length(Tw),3) | ||
| st[,1] <- round(Tw,3) | ||
| st[,2] <- round(Tw*100/sum(Tw),3) | ||
| st[,3] <- round(sqrt(Tw/(1+Tw)),3) | ||
| st <- as.data.frame(st) | ||
| dimnames(st) <- list( | ||
| paste("DF", 1:maxg, sep = ""), | ||
| c("Eig","Perceig","Cancor")) | ||
|
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| res <- list() | ||
| res$stats <- st | ||
| res$Tw <- Tw | ||
| res$rankmat <- rankmat | ||
| res$means <- pc$center | ||
| res$loadings <- pc$rotation[,1:rankmat,drop=FALSE] %*% | ||
| vec ## discriminant functions with PCA | ||
|
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| colnames(res$loadings) <- paste("DF", 1:maxg, sep = "") | ||
|
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| ## rotated data (projection) | ||
| x <- sweep(dat, 2, res$means) %*% res$loadings | ||
|
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| ## group means based on the rotated data | ||
| xmeans <- tapply(x, list(rep(cl,ncol(x)),col(x)), mean) | ||
| dimnames(xmeans)[[2]] <- colnames(x) | ||
|
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| #if(type==1){ | ||
| # mdist=as.matrix(dist(rbind(xmeans,x))) | ||
| # mdist=mdist[1:g,(g+1):ncol(mdist)] | ||
| # prob= (1-t(sweep(mdist,2,apply(mdist,2,sum),"/")))/(g-1) | ||
| # pred = apply(prob,1,which.max) | ||
| # pred <- factor(dimnames(prob)[[2]][pred], levels = levels(cl)) | ||
| #} | ||
| #else{ | ||
| nbmod <- naiveBayes(data.frame(x),cl) | ||
| prob <- predict(nbmod,data.frame(x),type="raw") | ||
| pred <- apply(prob,1,which.max) | ||
| pred <- factor(levels(cl)[pred], levels = levels(cl)) | ||
| #} | ||
| res$x <- x | ||
| res$xmeans <- xmeans | ||
| res$pred <- pred | ||
| res$cl <- cl | ||
| res$prior <- prior | ||
| res$conf <- table(cl,pred) | ||
| res$acc <- round(sum(diag(res$conf))*100/nrow(dat),2) | ||
| res$lev <- levels(cl) | ||
| res$call <- match.call() | ||
| res$call[[1]] <- as.name("nlda") | ||
| class(res) <- "nlda" | ||
|
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| return(res) | ||
| } | ||
| #' @importFrom e1071 naiveBayes | ||
| #' @importFrom stats cov predict | ||
| #' @importFrom methods as | ||
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| nlda <- function (dat, ...) UseMethod ("nlda") | ||
| setMethod('nlda',signature = 'AnalysisData', | ||
| function(x,cls = 'class',prior=NULL,scale=FALSE,comprank = FALSE,...) { | ||
|
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| cl <- x %>% | ||
| clsExtract(cls) | ||
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| if (is.numeric(cl)) | ||
| stop('Classes should not be numeric',call. = FALSE) | ||
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| cl <- factor(cl,levels = unique(cl)) | ||
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| if (any(table(cl) < 2)) { | ||
| remove_classes <- cl %>% | ||
| table() %>% | ||
| names() %>% | ||
| {.[table(cl) < 2]} | ||
|
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| x <- x %>% | ||
| removeClasses(cls = cls, | ||
| classes = remove_classes) | ||
|
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| warning(str_c('Classes with a single replicate removed: ', | ||
| str_c(str_c('"', | ||
| remove_classes, | ||
| '"'), | ||
| collapse = ', ')), | ||
| call. = FALSE) | ||
|
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| cl <- x %>% | ||
| clsExtract(cls) %>% | ||
| factor(levels = unique(.)) | ||
| } | ||
|
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| if (length(unique(cl)) < 2) | ||
| stop('More than 1 class needed for PC-LDA.',call. = FALSE) | ||
|
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| d <- x %>% | ||
| dat() %>% | ||
| as.matrix() | ||
|
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| if(is.null(prior)){prior <- as.vector(table(cl)/length(cl))} | ||
| if(is.null(names(prior))){names(prior) <- levels(cl)} | ||
|
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| pc <- prcomp(d,scale=scale) | ||
|
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| rankmat <- max(1,ncol(pc$x)-1) | ||
|
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| if(comprank == TRUE) | ||
| rankmat <- qr(cov(d)*(dim(d)[1]-1))$rank | ||
| score <- pc$x[,1:rankmat,drop=FALSE] | ||
|
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||
| g <- nlevels(cl) | ||
| mx <- apply(score,2,mean) | ||
| t <- matrix(0,nrow = rankmat,ncol=rankmat) | ||
| W <- matrix(0,nrow = rankmat,ncol=rankmat) | ||
| for(j in 1:g){ | ||
| idx <- which(cl==levels(cl)[j]) | ||
| L <- length(idx) | ||
| K <- score[idx,,drop=FALSE] | ||
| zz <- apply(K,2,mean) | ||
| A <- K - t(matrix(rep(mx, L),length(mx),L)) | ||
| C <- K - t(matrix(rep(zz, L),length(zz),L)) | ||
| t <- t + t(A)%*%A | ||
| W <- W + t(C)%*%C | ||
| } | ||
| B <- t-W | ||
|
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||
| Ng <- nrow(score)-g | ||
| P <- W/(Ng) | ||
| eP <- eigen(P) | ||
| ord <- sort.list(eP$values) | ||
| V <- sweep( | ||
| eP$vectors[,ord,drop=FALSE], | ||
| 2, | ||
| sqrt(colSums(eP$vectors[,ord,drop=FALSE]^2)), | ||
| "/") | ||
| Dg <- eP$values[ord] | ||
| nDg <- length(Dg) | ||
| Dmean <- sum(diag(P))/nDg | ||
| Dn <- matrix(0,nDg,nDg) | ||
| for(i in 1:nDg) | ||
| Dn[i,i] <- max(c(Dg[i],Dmean)) | ||
|
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||
| Wn <- V%*%Dn%*%t(V)*Ng | ||
| ratio <- solve(Wn)%*%B | ||
| er <- eigen(ratio) | ||
| ev <- Re(er$values) | ||
| ev[Im(er$values)>0] <- 0 | ||
| vec <- Re(er$vectors) | ||
| ord <- sort.list(ev,decreasing=TRUE) | ||
| vec <- sweep( | ||
| vec[,ord,drop=FALSE], | ||
| 2, | ||
| sqrt(colSums(vec[,ord,drop=FALSE]^2)), | ||
| "/") | ||
| ev <- ev[ord] | ||
| maxg <- min(c(g-1,dim(vec)[1])) | ||
| vec <- vec[,1:maxg] ## discriminant functions | ||
| Tw <- ev[1:maxg] | ||
| names(Tw) <- paste("DF", 1:maxg, sep = "") | ||
|
|
||
| ## get stats here | ||
| flip <- function(x) x[rev(seq_along(x))] | ||
| n <- dim(d)[1] | ||
| st <- matrix(0,length(Tw),3) | ||
| st[,1] <- round(Tw,3) | ||
| st[,2] <- round(Tw*100/sum(Tw),3) | ||
| st[,3] <- round(sqrt(Tw/(1+Tw)),3) | ||
| st <- as.data.frame(st) | ||
| dimnames(st) <- list( | ||
| paste("DF", 1:maxg, sep = ""), | ||
| c("Eig","Perceig","Cancor")) | ||
|
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| res <- as(x,'LDA') | ||
| res@stats <- as_tibble(st) | ||
| res@Tw <- Tw | ||
| res@rankmat <- rankmat | ||
| res@means <- pc$center | ||
|
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| loadings <- pc$rotation[,1:rankmat,drop=FALSE] %*% vec | ||
| colnames(loadings) <- paste("DF", 1:maxg, sep = "") | ||
| x <- sweep(d, 2, res@means) %*% loadings | ||
|
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| ## group means based on the rotated data | ||
| xmeans <- tapply(x, list(rep(cl,ncol(x)),col(x)), mean) | ||
| dimnames(xmeans)[[2]] <- colnames(x) | ||
|
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| nbmod <- naiveBayes(data.frame(x),cl) | ||
| prob <- predict(nbmod,data.frame(x),type="raw") | ||
| pred <- apply(prob,1,which.max) | ||
| pred <- factor(levels(cl)[pred], levels = levels(cl)) | ||
|
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| res@loadings <- as_tibble(loadings) | ||
| res@x <- as_tibble(x) | ||
| res@xmeans <- as_tibble(xmeans) | ||
| res@pred <- pred | ||
| res@cl <- cl | ||
| res@prior <- prior | ||
| res@conf <- table(cl,pred) | ||
| res@acc <- round(sum(diag(res@conf))*100/nrow(d),2) | ||
| res@lev <- levels(cl) | ||
| res@call <- match.call() | ||
| res@call[[1]] <- as.name("nlda") | ||
|
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||
| return(res) | ||
| } | ||
| ) |
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