There are two ways to provide input to the readme function:
as the list object as returned by the undergrad function
using the undergradlist parameter, or through the individual
parameters corresponding to the list elements. Any non-null parameter
will override the corresponding list element. If unmodified, the
function will use the default parameters for VA.
- undergradlist
- A list, as defined above, having an element for
each parameter below. Specify parameter values either through the
list or through the individual arguments. Non-null individual
arguments will supersede list values.
- features
- A positive integer specifying the number of words to
be subset from all words for estimation at each iteration. For the
choice of features. Default=16. (This option is
nsymp in VA).
- formula
- By default, the formula specifies all the
possible features (see undergrad()). To modify it, the new formula
must be specified as
formula=cbind(WORD.the+WORD.formula)~TRUTH
- n.subset
- A positive integer specifying the total number of
draws of different subsets of features. Default=300.
- prob.wt
- A positive integer or a vector of weights that
determines how likely a feature should be when selecting subsets of
features. When prob.wt is a vector, it must be a vector of
probabilities that sum up to 1 with length equal to the total number
of features. When prob.wt=1, binomial weights which are
proportion to the inverse of variances of the each reported binary
feature variable. When prob.wt=0, all features will be
selected with equal probability. Default=1.
- boot.se
- A Logical value. If TRUE, bootstrap standard
errors of the CSMF are estimated. This option typically takes a lot
of computing time. The default is FALSE.
- nboot
- A positive integer. If boot.se=TRUE, it
specifies the number of bootstrapping samples taken to estimate the
standard errors of CSMF. The default is 300.
- printit
- Logical value. If TRUE, the progress of the
estimation procedure is printed on the screen.
Gary King
2011-03-02