gplmbootstraptest | 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 macro offers a convenient interface for GPLM estimation and testing. A preparation of data is performed (inclusive sorting). |
gplmcore | gplmcore fits a generalized partially linear model E(y|x,t) = G(x*b + m(t)). This is the core macro 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 gplmest. |
gplmest | gplmest fits a generalized partially linear model E[y|x,t] = G(x*b + m(t)). This macro offers a convenient interface for GPLM estimation. A preparation of data is performed (inclusive sorting). |
gplminit | 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. |
gplmmain | loads everything necessary for library gplm. |
gplmopt | gplmopt defines a list with optional parameters in gplm macros. 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. |
gplmout | gplmout creates a nice output display for gplm. |
gplmstat | gplmstat provides some statistics for a fitted GPLM. |
gplmtest | gplmtest verifies the GPLM macros. |
replicdata | 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 identify the reduced data with the original. It takes a little longer due an additonal sort. |