Robust Mixture Regression
biscalew :Robust M-estimates for scale.
bisquare : Robust estimates for mean.
Plot the coefficient matrix.
The plot wrapper function.
Compute the row space using SVD.
The main function of the RBSL algorithm.
Perform the RBSL algorithm one times.
The predict function of the CSMR algorithm.
The train function of the CSMR algorithm.
CTLERob: Robust mixture regression based on component-wise adaptive tr...
denLp : Density function for Laplace distribution.
DeOut : Detect outlier observations.
flexmix_2: Multiple runs of MLE based mixture regression to stabilize ...
lars variant for LSA.
Obtain Log-likelihood from a mixtureReg Object
Least square approximation. This version Oct 19, 2006.
mixlinrb_bi: mixlinrb_bione estimates the mixture regression parameter...
mixlinrb_bione : mixlinrb_bione estimates the mixture regression param...
mixLp : mixLp_one estimates the mixture regression parameters robustly...
mixLp_one : mixLp_one estimates the mixture regression parameters robu...
Function to Fit Mixture of Regressions
The main function of mining the latent relationship among variables.
Model selection function for low dimension data.
Cross validation (fold-5) function for high dimension data.
Sort by X Coordinates and Add Line to a Plot
plot_CTLE: Plot the mixture/single regression line(s) in a simply func...
Plot Fit and Mixing Probability of a mixtureReg Object
Plot a List of mixtureReg Objects
Adaptive lasso.
The main function of Robust Mixture Regression using five methods.
Class RobMixReg.
Simulate high dimension data for RBSL algorithm validation.
The simulation function for low/high dimensional space.
The simulation function for low dimensional space.
TLE: robust mixture regression based on trimmed likelihood estimation.
Finite mixture models are a popular technique for modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine. This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation. The implemented algorithms includes: CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. Reference: Chun Yu, Weixin Yao, Kun Chen (2017) <doi:10.1002/cjs.11310>. NeyKov N, Filzmoser P, Dimova R et al. (2007) <doi:10.1016/j.csda.2006.12.024>. Bai X, Yao W. Boyer JE (2012) <doi:10.1016/j.csda.2012.01.016>. Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) <arXiv:2005.11599>.