/fam

Estimation of Sparse Functional Additive Models with Adaptive Group LASSO

Primary LanguageR

Estimation of Sparse Functional Additive Models with Adaptive Group LASSO

The folder consists of simulations and analysis for both tecator data and air pollutant data.

1.File - smoothing.R: file consists of codes for two functions

GCV.fit: implement generalized cross-validation for smoothing spline,

est.smooth: employ smoothing spline for estimation.

File - simulation ssFAM.R: This file consists of codes for our simulation studies.

Folder - "tecator analysis"

3.a: tecator.txt: the tecator data, its description is available on http://lib.stat.cmu.edu/datasets/tecator;

3.b: matlab code for PACE: files used to implment pace for both training data and test data;

3.c: traintec.csv and testtec.csv: they contain the results from the matlab code used to implment pace for both training data and test data;

3.d: Tecator analysis.R: data analysis for tecator data. Different estimation methods are compared.

Folder - "pm 2.5 analysis"

4.a: pm25_2000.csv: the functional predictors in the training data and test data, which is obtained from data cleaning.R,

4.b: matlab code for pace: files used to implment pace for both training data and test data;

4.c: train.mat and test.mat: they contain the results from the matlab code used to implment pace for both training data and test data;

4.d: airpollution.R: data analysis for air pollutant data. Different estimation methods are compared.