Description Usage Arguments Details Value Author(s) References See Also Examples
Estimates a Wordfish model using Conditional Maximum Likelihood.
1 2 3 4 5 6 7 |
wfm |
a word frequency matrix |
dir |
set global identification by forcing |
control |
list of estimation options |
verbose |
produce a running commentary |
Fits a Wordfish model with document ideal points constrained to mean zero and unit standard deviation.
The control
list specifies options for the estimation process.
conv.check
is either 'll' which stops when the difference
in log likelihood between iterations is less than tol
, or 'cor'
which stops when one minus the correlation between the theta
s
from the current and the previous iterations is less
than tol
. sigma
is the standard deviation for the beta
prior in poisson form. startparams
is a list of starting values
(theta
, beta
, psi
and alpha
) or a
previously fitted Wordfish model for the same data.
verbose
generates a running commentary during estimation
The model has two equivalent forms: a poisson model with two sets of document and two sets of word parameters, and a multinomial with two sets of word parameters and document ideal points. The first form is used for estimation, the second is available for alternative summaries, prediction, and profile standard error calculations.
The model is regularized by assuming a prior on beta with mean zero and standard deviation sigma (in poisson form). If you don't want to regularize, set beta to a large number.
An object of class wordfish. This is a list containing:
dir |
global identification of the dimension |
theta |
document positions |
alpha |
document fixed effects |
beta |
word slope parameters |
psi |
word fixed effects |
docs |
names of the documents |
words |
names of words |
sigma |
regularization parameter for betas in poisson form |
ll |
final log likelihood |
se.theta |
standard errors for document position |
data |
the original data |
Will Lowe
Slapin and Proksch (2008) 'A Scaling Model for Estimating Time-Series Party Positions from Texts.' American Journal of Political Science 52(3):705-772.
plot.wordfish
, summary.wordfish
,
coef.wordfish
, fitted.wordfish
,
predict.wordfish
, sim.wordfish
1 2 3 | dd <- sim.wordfish()
wf <- wordfish(dd$Y)
summary(wf)
|
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