Bootstrap test for comparing GLM vs. GPLM. The hypothesis E[y|x,t] = G(x*b + t*g + c) is tested against the alternative E[y|x,t] = G(x*b + m(t)). This routine offers a convenient interface for GPLM estimation and testing. A preparation of data is performed (inclusive sorting).
gplmcore fits a generalized partially linear model E(y|x,t) = G(x*b + m(t)). This is the core routine for GPLM estimation. It assumes that all input variables are given in the right manner. No preparation of data is performed. A more convenient way to estimate a GPLM is to call the function gplmes
gplmest fits a generalized partially linear model E[y|x,t] = G(x*b + m(t)). This routine offers a convenient interface for GPLM estimation. A preparation of data is performed (inclusive sorting).
gplminit checks the validity of input and performs the initial calculations for an GPLM fit (inclusive sorting). The output is ready to be used with gplmcore.
gplmopt defines a list with optional parameters in gplm functions. The list is either created or new options are appended to an existing list. Note that gplmopt does accept _any_ values for the parameters without validity.
replicdata reduces a matrix x to its distinct rows and gives the number of replications of each rows in the original dataset. An optional second matrix y can be given, the rows of y are summed up accordingly. replicdata does in fact the same as discrete but provides an additional index vector to id