القائمة
R mclust package for model-based clustering. Use for Gaussian mixture models and model-based clustering.
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استنادا إلى تصنيف SOC المهني
R language data analysis and visualization skill. Use when user asks to (1) run R scripts or code, (2) install/update R packages, (3) perform data analysis with R, (4) create visualizations with ggplot2/plotly, (5) statistical analysis, (6) data manipulation with tidyverse/dplyr/data.table. Triggers on keywords like "R语言", "R脚本", "ggplot", "tidyverse", "数据分析", "可视化".
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| name | mclust |
| description | R mclust package for model-based clustering. Use for Gaussian mixture models and model-based clustering. |
Gaussian mixture modeling for model-based clustering.
library(mclust)
# Model-based clustering
mc <- Mclust(data)
# Summary
summary(mc)
# Results
mc$classification # Cluster assignments
mc$G # Number of clusters
mc$modelName # Best model
mc$BIC # BIC values
# Fixed number of clusters
mc <- Mclust(data, G = 3)
# Range of clusters
mc <- Mclust(data, G = 1:9)
# BIC plot
plot(mc, what = "BIC")
# Available models
# E = equal, V = variable
# I = spherical, E = diagonal, V = ellipsoidal
# EII, VII, EEI, VEI, EVI, VVI, EEE, EVE, VEE, VVE, EEV, VEV, EVV, VVV
# Classification plot
plot(mc, what = "classification")
# Uncertainty plot
plot(mc, what = "uncertainty")
# Density plot
plot(mc, what = "density")
# All plots
plot(mc)
# Density estimation
dens <- densityMclust(data)
# Plot
plot(dens, what = "density")
plot(dens, what = "persp") # 3D
# Model-based discriminant analysis
mda <- MclustDA(train_data, train_labels)
# Predict
pred <- predict(mda, newdata = test_data)
pred$classification
# Cluster with dimension reduction
mc <- MclustDR(data)
# Plot
plot(mc, what = "pairs")
plot(mc, what = "boundaries")
# Bootstrap LRT
boot <- mclustBootstrapLRT(data, modelName = "VVV")
boot
# ICL (Integrated Complete-data Likelihood)
mc <- Mclust(data)
icl <- mclustICL(data)
plot(icl)