UMAP plot in R: Example 2 # 圈出异常样本 library(ggforce) umap_df %>% ggplot(aes(x = UMAP1, y = UMAP2, color = species, shape = sex)) + geom_point() + labs(x = "UMAP1", y = "UMAP2", subtitle="UMAP plot") + geom_circle(aes(x0 = -5, y0 = -3.8, r = 0.65), ...
Example 2: Biplot of PCA Using factoextra PackageIt’s also possible to create a biplot using the fviz_pca_biplot() function of the factoextra package, which is specialized to visualize PCA output. Like in base R, we must input the initialized pca object to run the function. Please note ...
UMAP plot in R: Example 2 # 圈出异常样本 library(ggforce) umap_df %>% ggplot(aes(x = UMAP1, y = UMAP2, color = species, shape = sex)) + geom_point() + labs(x = "UMAP1", y = "UMAP2", subtitle="UMAP plot") + geom_circle(aes(x0 = -5, y0 = -3.8, r = 0.65), ...
Real-World Example of PCA in R Now that you understand the underlying theory of PCA, you are finally ready to see it in action. This section covers all the steps from installing the relevant packages, loading and preparing the data applying principal component analysis in R, and interpreting ...
下面的 R 代码计算活跃个体/变量的主成分分析: library("FactoMineR") res.pca <- PCA(decathlon2.active, graph = FALSE) 函数PCA()的输出是一个列表,包括以下部分: print(res.pca) ## **Results for the Principal Component Analysis (PCA)** ...
# Load iris dataset as an example iris = load_iris() X = iris.data y = iris.target # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) ...
微信公众号:医学统计与R语言 简介 From a data analysis standpoint, PCA is used for studying one table of observations and variables with the main idea of transforming the observed variables into a set of new variables, the principal components,...
Implementation of PCA in R R is a popular programming language for research-based statistical computing and graphical analysis. The prcomp function is a key player in this process. Let's walk through a basic example to illustrate the implementation of PCA in R. Code: library(ggplot2) set.seed...
importnumpyasnpfromsklearn.decompositionimportPCAfromsklearn.preprocessingimportStandardScalerfromsklearn.model_selectionimporttrain_test_splitfromsklearn.datasetsimportload_iris# Load iris dataset as an exampleiris = load_iris() X = iris.data y = iris.target# Split the dataset into training and testin...
There are multiple principal components depending on the number of dimensions (features) in the dataset and they are orthogonal to each other. The maximum number of principal component is same as a number of dimension of data. For example, in the above figure, for two-dimension data, there ...