Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: gplm

gplmbilo GPLM (logit) -- gplmbilo fits a generalized partially linear model where y|x,t is binomial distributed and E[y|x,t] and x*b + m(t) are linked via the logistic function (canonical link).
gplmbilobiased biased GLM (logit) -- gplmbilobiased computes the biased generalized linear model for the test of a GLM (logit) versus a GPLM (logit).
gplmbilobootstraptest Bootstrap test (using as. normality) GLM (logit) vs. GPLM (logit) -- by default gplmnoidtest tests the generalized "truly" linear model E[y|x,t] = G(x*b + t*g + c) versus the genralized partially linear model E[y|x,t] = G(x*b + m(t)), where G is the logistic link function. Optional, an alternative design matrix can be specified for t or parametric estimates for b and m can be given directly.
gplmbilotest Test (using as. normality) GLM (logit) vs. GPLM (logit) -- by default gplmnoidtest tests the generalized "truly" linear model E[y|x,t] = G(x*b + t*g + c) versus the genralized partially linear model E[y|x,t] = G(x*b + m(t)), where G is the logistic link function. Optional, an alternative design matrix can be specified for t or parametric estimates for b and m can be given directly.
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.
gplmnoid PLM -- gplmnoid fits a generalized partially linear model where y|x,t is normally distributed and E[y|x,t] = x*b + m(t) (canonical link).
gplmnoidbiased biased LM -- gplmnoidbiased computes the biased linear model for the test of a linear model versus a PLM. This is a fast routine using the command sker to obtain kernel estimates.
gplmnoidtest Test (using as. normality) linear vs. partially linear -- by default gplmnoidtest tests the "truly" linear model E[y|x,t] = x*b + t*g + c versus the partially linear model E[y|x,t] = x*b + m(t). Optional, an alternative design matrix can be specified for t or parametric estimates for b and m can be given directly.
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.

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 28.6.1999