Usage: |
myfit = glmest(code,x,y{,opt})
|
Input: |
| code | text string, the short code for the model (e.g.
"bilo" for logit or "noid" for ordinary PLM).
|
| x | n x p matrix, the predictor variables.
|
| y | n x 1 vector, the response variables.
|
| opt | optional, a list with optional input.
"glmopt" can be used to set up this parameter.
The order of the list elements is not important.
|
| opt.weights | string, type of weights. Can be "frequency"
for replication counts, or "prior" (default)
for prior weights in weighted regression.
|
| opt.wx | scalar or n x 1 vector, frequency or prior
weights. If not given, set to 1.
|
| opt.off | scalar or n x 1 vector, offset. Can be used for
constrained estimation. If not given, set to 0.
|
| opt.shf | integer, if exists and =1, some output is produced
which indicates how the iteration is going on.
|
| opt.norepl | integer, if exists and =1, the data are assumed to
have no replications in x. Otherwise, the data
are searched for replications to fasten the
algorithm.
|
| opt.miter | integer, maximal number of iterations. The default
is 10.
|
| opt.cnv | scalar, convergence criterion. The default is 0.0001.
|
| opt.fscor | integer, if exists and =1, a Fisher scoring is
performed (instead of the default Newton-Raphson
procedure). This parameter is ignored for
canonical links.
|
| opt.pow | scalar, power for power link. If not given,
set to 0.
|
| opt.nbk | scalar, extra parameter k for negative binomial
distribution. If not given, set to 1 (geometric
distribution).
|
Output: |
| myfit.b | p x 1 vector, estimated coefficients. |
| myfit.bv | p x p matrix, estimated covariance matrix
for coefficients. |
| myfit.stat | list with components as computed by glmstat:
serror (standard errors of coefficients),
tvalue (t-values for coefficients),
pvalue (p-values for coefficients),
df (degrees of freedom),
deviance (deviance),
pearson (generalized pearson's chi^2),
loglik (log-likelihood),
dispersion (estimated dispersion =pearson/df),
r2 ((pseudo) coefficient of determination),
adr2 (adjusted (pseudo) coefficient of determination),
aic (Akaike's AIC criterion),
bic (Schwarz' BIC criterion), and
it (number of iterations needed),
ret (return code,
0 if everything went o.k.,
1 if maximal number of iterations reached,
-1 if missing values have been encountered),
nr (number of replications found in x). |