labs(title ='Variables Contribution to Principal Components') theme_minimal() # 可视化变量和个体 fviz_pca_biplot(pca_result, geom.ind ='point',# Show points only (but not 'text') col.ind = iris$Species,# Color by groups palette = c('#00AFBB','#E7B800','#FC4E07'), addEllipses =...
labs(x = "UMAP1", y = "UMAP2", subtitle = "UMAP plot") UMAP plot in R: Example 1 # 分面 umap_df %>% ggplot(aes(x = UMAP1, y = UMAP2, color = species)) + geom_point(size=3, alpha=0.5)+ facet_wrap(~island)+ labs(x = "UMAP1", y = "UMAP2", subtitle="UMAP plot...
计算G矩阵setDF(g012)rownames(g012) = g012$IIDg012$IID = NULLg012$FID=NULLGmat = A.mat(g012-1) # 计算特征值和特征向量re = eigen(Gmat) # 计算解释百分比por = re$values/sum(re$values) # 整理格式pca_re1 = re$vectors
scale_color_manual(values = mycol1) +#散点颜色调整 labs(x = 'PC1 (19.7%)', y = 'PC2 (11.2%)')#修改X/Y轴标题 p2 2. 根据分组添加置信区间椭圆 ##虚线无填充置信椭圆: p3<- p2 + stat_ellipse(aes(color = Group), level = 0.95, linetype = 2, show.legend = FALSE)#linetype参数...
(x=X1,y=X2,color=Gen))+geom_point(size=2)+#stat_ellipse(level=0.95,size=1)+stat_ellipse(aes(fill=Gen),type="norm",geom="polygon",alpha=0.2,color=NA)+geom_hline(yintercept=0)+# 添加x坐标geom_vline(xintercept=0)+# 添加y坐标labs(x=xlab,y=ylab,color="")+guides(fill=F)+...
labs(x=xlab,y=ylab,color="Species",title ="PCA Plot")+ theme(plot.title = element_text(face ="bold",size =20,hjust =0.5))+ #添加0刻度虚线 geom_vline(xintercept=0,linetype=4,color="grey")+ geom_hline(yintercept=0,linetype=4,color="...
labs(title = "PCA:鸢尾花数据", x = "主成分1", y = "主成分2") 1. 2. 3. 4. 5. 6. 7. 8. 总结 在这个示例中,我们使用PCA对鸢尾花数据进行了降维,并通过可视化手段展示了数据的主成分。PCA是处理高维数据的一种有效方法,它能够帮助我们理解数据中的主要变异来源,并减少计算的复杂性。
(x=X1,y=X2,color=Gen))+geom_point(size=2)+#stat_ellipse(level=0.95,size=1)+stat_ellipse(aes(fill=Gen),type="norm",geom="polygon",alpha=0.2,color=NA)+geom_hline(yintercept=0)+# 添加x坐标geom_vline(xintercept=0)+# 添加y坐标labs(x=xlab,y=ylab,color="")+guides(fill=F)+...
) %>% ggplot(aes(Tag, Value, label = Tag, fill = Tag)) + geom_col(alpha = 0.9, show.legend = FALSE) + geom_text(aes(Tag, 0.001), hjust = 0, color = "white", size = 4, family = "IBMPlexSans-Bold") + coord_flip() + labs(x = NULL, y = "Averag...
("PC1", "PC2") # 假设data$group是一个包含样本分组的因子变量 pca_data$group <- factor(data$group) # 绘制PCA图 ggplot(pca_data, aes(x = PC1, y = PC2, color = group)) + geom_point(size = 3) + labs(title = "PCA Plot", x = "Principal Component 1", y = "Principal ...