| Usage: |
glmfit(x,y{,opt})
|
| Input: |
| x | n x p matrix, the predictor variables.
|
| y | n x 1 vector, the response variables.
(In the case of replicated data, the number of
replications should be given in opt.wx and y should
contain the sums of all responses for a replication.)
|
| 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.code | text string, the short code for the model (e.g.
"bilo" for logit or "noid" for ordinary PLM).
|
| 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 in linear predictor.
If not given, set to 0.
|
| opt.name | string, prefix for the output. If not given, "glm"
is used.
|
| opt.title | string, title for the output. If not given, a default
is set.
|
| opt.xvars | p x 1 string vector, variable names for the output.
Note, that only up to 11 characters are used.
|
| Output: |
| glmfitOutput or opt.name+"Output" | display, containing estimation result in the left
panel and a plot of the index x*b{+off} vs. y
(vs. y./wx for binomials) and a plot of th index
vs. the link function. |
| glmfitout or opt.name+"out" | string vector, global, containing estimation result
in the left panel as text. |
| glmfit or opt.name | list, global variable with the components: |
| glmfit.b | p x 1 vector, estimated coefficients. |
| glmfit.bv | p x p matrix, estimated covariance matrix for b. |
| glmfit.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). |