R: New Experimental Design Based Subsampling Methods for Big Data

# New Experimental Design Based Subsampling Methods for Big Data

## Documentation for package ‘NeEDS4BigData’ version 1.0.1

- DESCRIPTION file

## Help Pages

ALoptimalGLMSub | A- and L-optimality criteria based subsampling under Generalised Linear Models
AoptimalGauLMSub | A-optimality criteria based subsampling under Gaussian Linear Models
AoptimalMCGLMSub | A-optimality criteria based subsampling under measurement constraints for Generalised Linear Models
Electric_consumption | Electric consumption data
GenGLMdata | Generate data for Generalised Linear Models
GenModelMissGLMdata | Generate data for Generalised Linear Models under model misspecification scenario
LCCsampling | Local case control sampling for logistic regression
LeverageSampling | Basic and shrinkage leverage sampling for Generalised Linear Models
modelMissLinSub | Subsampling under linear regression for a potentially misspecified model
modelMissLogSub | Subsampling under logistic regression for a potentially misspecified model
modelMissPoiSub | Subsampling under Poisson regression for a potentially misspecified model
modelRobustLinSub | Model robust optimal subsampling for A- and L- optimality criteria under linear regression
modelRobustLogSub | Model robust optimal subsampling for A- and L- optimality criteria under logistic regression
modelRobustPoiSub | Model robust optimal subsampling for A- and L- optimality criteria under Poisson regression
One_million_songs | One million songs data
plot_AMSE | Plotting AMSE outputs for the samples under model misspecification
plot_Beta | Plotting model parameter outputs after subsampling
Skin_segmentation | Skin segmentation data