Fits a linear model to survey data in each node obtained by recursively partitioning the data. The splitting variables and splits selected are obtained using a procedure which adjusts for complex sample design features used to obtain the data. Likewise the model fitting algorithm produces design-consistent coefficients to the least squares linear model between the dependent and independent variables. The first stage of the design is accounted for in the provided variance estimates. The main function returns the resulting binary tree with the linear model fit at every endnode as an R object of class "rpms". The package provides a number of functions and methods for this rpms class.
|Author||daniell toth [aut, cre]|
|Date of publication||2016-10-21 21:56:52|
|Maintainer||daniell toth <email@example.com>|
CE: CE Consumer expenditure data (first quarter of 2014)
survLm_model: Fit a linear model using data collected from a complex sample
Wtest: Wtest (Wald Test for sample weights)