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下面这些代码将展示由Francois Gillet编写的两个方差分析多重比较的函数 boxplert()和boxplerk(),来源Numerical Ecology with R (second Edition)
#普通方差分析多重比较 boxplert <- function(X, Y, main = NULL, xlab = NULL, ylab = NULL, bcol = "bisque", p.adj = "none", cexy = 1, varwidth = TRUE, las = 1, paired = FALSE) {#Y=factor(Y,levels=unique(Y)) #如果运行次代码,表示组别是按照原来的顺序,而不是字母顺序排列 aa <- levels(as.factor(Y)) an <- as.character(c(1:length(aa))) tt1 <- matrix(nrow = length(aa), ncol = 6) for (i in 1:length(aa)) { temp <- X[Y == aa[i]] tt1[i, 1] <- mean(temp, na.rm = TRUE) tt1[i, 2] <- sd(temp, na.rm = TRUE) / sqrt(length(temp)) tt1[i, 3] <- sd(temp, na.rm = TRUE) tt1[i, 4] <- min(temp, na.rm = TRUE) tt1[i, 5] <- max(temp, na.rm = TRUE) tt1[i, 6] <- length(temp) } tt1 <- as.data.frame(tt1) row.names(tt1) <- aa colnames(tt1) <- c("mean", "se", "sd", "min", "max", "n") boxplot( X ~ Y, main = main, xlab = xlab, ylab = ylab, las = las, col = bcol, cex.axis = cexy, cex.lab = cexy, varwidth = varwidth ) require(agricolae) Yn <- factor(Y, labels = an) sig <- "ns" model <- aov(X ~ Yn) if (paired == TRUE & length(aa) == 2) { coms <- t.test(X ~ Yn, paired = TRUE) pp <- coms$p.value } else { pp <- anova(model)$Pr[1] } if (pp <= 0.1) sig <- "." if (pp <= 0.05) sig <- "*" if (pp <= 0.01) sig <- "**" if (pp <= 0.001) sig <- "***" mtext( sig, side = 3, line = 0.5, adj = 0, cex = 2, font = 1 ) if (pp <= 0.05) { comp <- LSD.test(model, "Yn", alpha = 0.05, p.adj = p.adj, group = TRUE) # gror <- comp$groups[order(comp$groups$groups), ] # tt1$cld <- gror$M gror <- comp$groups[order(rownames(comp$groups)), ] tt1$group <- gror$groups mtext( tt1$group, side = 3, at = c(1:length(aa)), line = 0.5, cex = 1, font = 4 ) } list(comparison = tt1, p.value = pp) }##非参数组间差异的Kruskal-Wallis检验 boxplerk <- function(X, Y, main = NULL, xlab = NULL, ylab = NULL, bcol = "bisque", p.adj = "none", cexy = 1, varwidth = TRUE, las = 1, paired = FALSE) {#Y=factor(Y,levels=unique(Y)) #如果运行次代码,表示组别是按照原来的顺序,而不是字母顺序排列 aa <- levels(as.factor(Y)) an <- as.character(c(1:length(aa))) tt1 <- matrix(nrow = length(aa), ncol = 7) for (i in 1:length(aa)) { temp <- X[Y == aa[i]] tt1[i, 1] <- mean(temp, na.rm = TRUE) tt1[i, 2] <- sd(temp, na.rm = TRUE) / sqrt(length(temp)) tt1[i, 3] <- sd(temp, na.rm = TRUE) tt1[i, 4] <- min(temp, na.rm = TRUE) tt1[i, 5] <- max(temp, na.rm = TRUE) tt1[i, 6] <- median(temp, na.rm = TRUE) tt1[i, 7] <- length(temp) } tt1 <- as.data.frame(tt1) row.names(tt1) <- aa colnames(tt1) <- c("mean", "se", "sd", "min", "max", "median", "n") boxplot( X ~ Y, main = main, xlab = xlab, ylab = ylab, las = las, col = bcol, cex.axis = cexy, cex.lab = cexy, varwidth = varwidth ) require(agricolae) Yn <- factor(Y, labels = an) comp <- kruskal(X, Yn, p.adj = p.adj) sig <- "ns" if (paired == TRUE & length(aa) == 2) { coms <- wilcox.test(X ~ Yn, paired = TRUE) pp <- coms$p.value } else { pp <- comp$statistics$p.chisq } if (pp <= 0.1) sig <- "." if (pp <= 0.05) sig <- "*" if (pp <= 0.01) sig <- "**" if (pp <= 0.001) sig <- "***" gror <- comp$groups[order(rownames(comp$groups)), ] tt1$rank <- gror$X tt1$group <- gror$groups mtext( sig, side = 3, line = 0.5, adj = 0, cex = 2, font = 1 ) if (pp <= 0.1) mtext( tt1$group, side = 3, at = c(1:length(aa)), line = 0.5, cex = 1, font = 4 ) list(comparison = tt1, p.value = pp) } # # 普通方差分析的多重比较 library(agricolae)
## Warning: package 'agricolae' was built under R version 3.4.4
data(sweetpotato)# # 检验方差分析假设 shapiro.test(resid(aov(sweetpotato$yield ~ sweetpotato$virus)))
## ## Shapiro-Wilk normality test ## ## data: resid(aov(sweetpotato$yield ~ sweetpotato$virus)) ## W = 0.95878, p-value = 0.7663
bartlett.test(sweetpotato$yield, sweetpotato$virus)
## ## Bartlett test of homogeneity of variances ## ## data: sweetpotato$yield and sweetpotato$virus ## Bartlett's K-squared = 2.3886, df = 3, p-value = 0.4958
boxplert( sweetpotato$yield, sweetpotato$virus, ylab = "yield", xlab = "virus", bcol = "orange", p.adj = "holm" )
## $comparison ## mean se sd min max n group ## cc 24.40000 2.084067 3.609709 21.7 28.5 3 b ## fc 12.86667 1.246774 2.159475 10.6 14.9 3 c ## ff 36.33333 4.233727 7.333030 28.0 41.8 3 a ## oo 36.90000 2.482606 4.300000 32.1 40.4 3 a ## ## $p.value ## [1] 0.0007334172
# # 案例 library(agricolae) library(stats) data(InsectSprays) boxplerk( InsectSprays$count, InsectSprays$spray, ylab = "count", xlab = "spray", bcol = "bisque", p.adj = "holm", las = 1)
## $comparison ## mean se sd min max median n rank group ## A 14.500000 1.3623732 4.719399 7 23 14.0 12 52.16667 a ## B 15.333333 1.2329647 4.271115 7 21 16.5 12 54.83333 a ## C 2.083333 0.5701984 1.975225 0 7 1.5 12 11.45833 c ## D 4.916667 0.7225621 2.503028 2 12 5.0 12 25.58333 b ## E 3.500000 0.5000000 1.732051 1 6 3.0 12 19.33333 bc ## F 16.666667 1.7936476 6.213378 9 26 15.0 12 55.62500 a ## ## $p.value ## [1] 1.510845e-10
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