R中的判别分析(FDA和MDA)图
R中的判别分析(FDA和MDA)图
提问于 2020-08-23 03:25:12
我正在尝试使用mda和ggplot2软件包绘制灵活判别分析和混合判别分析的结果。我这样做是为了线性判别分析(LDA)，但我不知道继续下去。有什么帮助或想法如何使用ggplot2对这些图形进行编码吗？
代码：
require(MASS)
require(ggplot2)
require(mda)
require(scales)
irislda <- lda(Species ~ ., iris)
prop.lda = irislda$svd^2/sum(irislda$svd^2)
plda <- predict(irislda, iris)
dataset = data.frame(species = iris[,"Species"], irislda = plda$x)
p1 <- ggplot(dataset) + geom_point(aes(irislda.LD1, irislda.LD2, colour = species, shape = species), size = 2.5) +
labs(x = paste("LD1 (", percent(prop.lda[1]), ")", sep=""),
y = paste("LD2 (", percent(prop.lda[2]), ")", sep=""))
p1
irisfda <- fda(Species ~ ., data = iris, method = mars)
irismda <- mda(Species ~ ., data = iris)回答 1
Stack Overflow用户
发布于 2020-08-25 00:50:23
我相信这就是你想要的。fda的模型只有两个维度，所以它可以解释100%。mda模型有5个维度，所以我只展示解释最多的两个维度。
library(dplyr)
irisfda <- fda(Species ~ ., data = iris, method = mars)
irisfda$fit$fitted.values %>%
as_tibble() %>%
bind_cols(species = iris[,"Species"]) %>%
ggplot() +
geom_point(aes(V1, V2, color = species, shape = species), size = 2.5) +
labs(x = paste("FDA1 (", percent(irisfda$percent.explained[1]/100), ")", sep=""),
y = paste("FDA2 (", percent(irisfda$percent.explained[2]/100 - irisfda$percent.explained[1]/100), ")", sep=""))
irismda <- mda(Species ~ ., data = iris)
irismda$fit$fitted.values %>%
as_tibble() %>%
bind_cols(species = iris[,"Species"]) %>%
ggplot() +
geom_point(aes(V1, V2, color = species, shape = species), size = 2.5) +
labs(x = paste("MDA1 (", percent(irismda$percent.explained[1]/100), ")", sep=""),
y = paste("MDA2 (", percent(irismda$percent.explained[2]/100 - irismda$percent.explained[1]/100), ")", sep=""))
编辑：
为了消除您看到的警告，我们可以在将矩阵传递给as_tibble之前命名矩阵的列。此编辑不使用%>%运算符。
colnames(irisfda$fit$fitted.values) <- c("V1", "V2")
df1 <- bind_cols(as_tibble(irisfda$fit$fitted.values),
species = iris[,"Species"])
ggplot(df1) +
geom_point(aes(V1, V2, color = species, shape = species), size = 2.5) +
labs(x = paste("FDA1 (", percent(irisfda$percent.explained[1]/100), ")", sep=""),
y = paste("FDA2 (", percent(irisfda$percent.explained[2]/100 - irisfda$percent.explained[1]/100), ")", sep=""))
colnames(irismda$fit$fitted.values) <- c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8")
df2 <- bind_cols(as_tibble(irismda$fit$fitted.values),
species = iris[,"Species"])
ggplot(df2) +
geom_point(aes(V1, V2, color = species, shape = species), size = 2.5) +
labs(x = paste("MDA1 (", percent(irismda$percent.explained[1]/100), ")", sep=""),
y = paste("MDA2 (", percent(irismda$percent.explained[2]/100 - irismda$percent.explained[1]/100), ")", sep=""))EDIT 2:似乎您不想使用dplyr，所以我在ggplot图中包含了基数R函数。
library(dplyr)
require(MASS)
require(ggplot2)
require(mda)
require(scales)
irisfda <- fda(Species ~ ., data = iris, method = mars)
irismda <- mda(Species ~ ., data = iris)
df1 <- cbind(data.frame(irisfda$fit$fitted.values),
species = iris[,"Species"])
ggplot(df1) +
geom_point(aes(X1, X2, color = species, shape = species), size = 2.5) +
labs(x = paste("FDA1 (", percent(irisfda$percent.explained[1]/100), ")", sep=""),
y = paste("FDA2 (", percent(irisfda$percent.explained[2]/100 - irisfda$percent.explained[1]/100), ")", sep=""))
df2 <- cbind(data.frame(irismda$fit$fitted.values),
species = iris[,"Species"])
ggplot(df2) +
geom_point(aes(X1, X2, color = species, shape = species), size = 2.5) +
labs(x = paste("MDA1 (", percent(irismda$percent.explained[1]/100), ")", sep=""),
y = paste("MDA2 (", percent(irismda$percent.explained[2]/100 - irismda$percent.explained[1]/100), ")", sep=""))页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/63540254
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