topmod {BMS}

Topmodel Object

Description

Create or use an updateable list keeping the best x models it encounters (for advanced users)

Usage

topmod(
  nbmodels,
  nmaxregressors = NA,
  bbeta = FALSE,
  lengthfixedvec = 0,
  liks = numeric(0),
  ncounts = numeric(0),
  modelbinaries = matrix(0, 0, 0),
  betas = matrix(0, 0, 0),
  betas2 = matrix(0, 0, 0),
  fixed_vector = matrix(0, 0, 0)
)

Arguments

nbmodels

The maximum number of models to be retained by the topmod object

nmaxregressors

The maximum number of covariates the models in the topmod object are allowed to have

bbeta

if bbeta=TRUE, then first and second moments of model coefficients are stored in addition to basic model statistics (Note: if bbeta<0 then only the first moments are saved)

lengthfixedvec

The length of an optional fixed vector adhering to each model (for instance R-squared, etc). If lengthfixedvec=0 then no additonal fixed vector will be stored.

liks

optional vector of log-likelihoods to initialize topmod object with (length must be <=nbmodels) - see example below

ncounts

optional vector of MCMC frequencies to initialize topmod object with (same length as liks) - see example below

modelbinaries

optional matrix whose columns detail model binaries to initialize topmod object with (same nb columns as liks, nb rows as nmaxregressors) - see example below

betas

optional matrix whose columns are coefficients to initialize topmod object with (same dimensions as modelbinaries) - see example below

betas2

optional matrix whose columns are coefficients' second moments to initialize topmod object with (same dimensions as modelbinaries) - see example below

fixed_vector

optional matrix whose columns are a fixed vector initialize topmod object with (same ncol as modelbinaries) - see example below

Details

A 'topmod' object (as created by topmod) holds three basic vectors: lik (for the (log) likelihood of models or similar), bool() for a hexcode presentation of the model binaries (cf. bin2hex) and ncount() for the times the models have been drawn.
All these vectors are sorted descendantly by lik, and are of the same length. The maximum length is limited by the argument nbmodels.

If tmo is a topmod object, then a call to tmo$addmodel (e.g. tmo$addmodel(mylik=4,vec01=c(T,F,F,T)) updates the object tmo by a model represented by vec01 (here the one including the first and fourth regressor) and the marginal (log) likelihood lik (here: 4).

If this model is already part of tmo, then its respective ncount entry is incremented by one; else it is inserted into a position according to the ranking of lik.

In addition, there is the possibility to save (the first moments of) coefficients of a model (betas) and their second moments (betas2), as well as an arbitrary vector of statistics per model (fixed_vector).

is.topmod returns TRUE if the argument is of class 'topmod'

Value

a call to topmod returns a list of class "topmod" with the following elements:

addmodel(mylik, vec01, vbeta=numeric(0), vbeta2=numeric(0), fixedvec=numeric(0))

function that adjusts the list of models in the 'topmod' object (see Details). mylik is the basic selection criterion (usually log likelihood), vec01 is the model binary (logical or numeric) indicating which regressors are included.
vbeta is a vector of length equal to sum(vec01), contianing only the non-zero coefficients (only accounted for if bbeta!=FALSE). vbeta2 is a similar vector of second moments etc. (only accounted for if bbeta=TRUE); fixedvec is an arbitrary vector of length lengthfixedvec (see above)

lik()

A numeric vector of the best models (log) likelihoods, in decreasing order

bool()

A character vector of hexmode expressions for the model binaries (cf. bin2hex), sorted by lik()

ncount()

A numeric vector of MCMC frequencies for the best models (i.e. how often the respective model was introduced by addmodel)

nbmodels

Returns the argument nbmodel

nregs

Returns the argument nmaxregressors

bool_binary()

Returns a matrix whose columns present the models conforming to lik() in binary form

betas()

a matrix whose columns are the coefficients conforming to bool_binary() (Note that these include zero coefficients due to non-inclusion of covariates); Note: if bbeta=FALSE this returns an empty matrix

betas2()

similar to betas , for the second moments of coefficients Note: if bbeta<=0, this returns an empty matrix

fixed_vector()

The columns of this matrix return the fixed_vector statistics conforming to lik() (see Details); Note: if lengthfixedvec=0 this returns an empty matrix

Note

topmod is rather intended as a building block for programming; it has no direct application for a user of the BMS package.

See Also

the object resulting from bms includes an element of class 'topmod'

Check http://bms.zeugner.eu for additional help.

Examples

#standard use
  tm= topmod(2,4,TRUE,0) #should keep a  maximum two models
  tm$addmodel(-2.3,c(1,1,1,1),1:4,5:8) #update with some model
  tm$addmodel(-2.2,c(0,1,1,1),1:3,5:7) #add another model
  tm$addmodel(-2.2,c(0,1,1,1),1:3,5:7) #add it again -> adjust ncount
  tm$addmodel(-2.5,c(1,0,0,1),1:2,5:6) #add another model
  
  #read out
  tm$lik()
  tm$ncount()
  tm$bool_binary()
  tm$betas()
  
  is.topmod(tm)
  
  #extract a topmod oobject only containing the second best model
  tm2=tm[2]
  
  
  
  #advanced: should return the same result as
  #initialize
  tm2= topmod(2,4,TRUE,0, liks = c(-2.2,-2.3), ncounts = c(2,1), 
          modelbinaries = cbind(c(0,1,1,1),c(1,1,1,1)), betas = cbind(0:3,1:4), 
          betas2 = cbind(c(0,5:7),5:8)) 

  #update 
  tm$addmodel(-2.5,c(1,0,0,1),1:2,5:6) #add another model
  
  #read out
  tm$lik()
  tm$ncount()
  tm$bool_binary()
  tm$betas()