summary.cozigam {COZIGAM} | R Documentation |
Produces various useful summaries of a fitted COZIGAM.
## S3 method for class 'cozigam': summary(object, dispersion = NULL, ...) ## S3 method for class 'summary.cozigam': print(x,digits = max(3, getOption("digits") - 3), signif.stars = getOption("show.signif.stars"), ...)
object |
A fitted cozigam object produced by the cozigam function. |
dispersion |
A known dispersion parameter value. Set it to NULL ,
the dispersion parameter is set to an
estimate or default value from the COZIGAM (e.g. 1 for Poisson). |
x |
A summary.cozigam object produced by the summary.cozigam function. |
digits |
The number of significant digits to use when printing. |
signif.stars |
Logical. If TRUE , "significance stars"
are printed for each coefficient. |
... |
Other arguments. |
print.summary.cozigam
smartly formats the coefficients, standard errors, etc.
and additionally labels coefficients with "significance stars" if signif.stars
is TRUE
.
summary.cozigam
produces a list of summary information
for a fitted cozigam
object.
p.coeff |
An array of the strictly parametric estimates, including those of the linear constraints parameters. |
p.t |
An array of the t-ratios, i.e. estimates in the p.coeff divided by their standard errors. |
p.pv |
An array of p-values for the null hypothesis that the corresponding parameter is zero. Calculated with reference to the t distribution with the estimated residual degrees of freedom for the model fit if the dispersion parameter has been estimated, but otherwise with reference to the standard normal distribution. |
m |
The number of smooth terms in the model. |
chi.sq |
An array of test statistics for assessing the significance of model smooth terms.
If b_i is the parameter vector for the i th smooth term, and this term has
estimated covariance matrix V_i then the statistic is b_i'V_i^{k-}b_i,
where V_i^{k-} is the rank k pseudo-inverse of V_i, and k is
estimated rank of V_i. |
s.pv |
An array of approximate p-values for the null hypotheses that each smooth term is zero.
Be warned, these are only approximate values. In the case of known dispersion parameter,
they are obtained by comparing the above chi.sq statistic to the chi-squared
distribution with k degrees of freedom, where k is the estimated rank of
V_i. If the dispersion parameter is unknown (in which case it will have been estimated)
the statistic is compared to an F distribution with k upper d.f. and lower d.f.
given by the residual degrees of freedom for the model.Typically the p-values will be
somewhat too low, because they are conditional on the smoothing parameters,
which are usually uncertain, but note that the statistic can also have low power if the
rank, k , is too high relative to the EDF (Estimated Degrees of Freedom) of the term. |
se |
An array of standard error estimates for all parameter estimates. |
edf |
An array of estimated degrees of freedom for the model terms. |
residual.df |
The estimated residual degrees of freedom. |
n |
The number of data. |
family |
The family used. |
formula |
The original GAM formula. |
dispersion |
The estimated (or given) scale parameter. |
cov.unscaled |
The estimated covariance matrix of the parameters, divided by the scale parameter. |
cov.scaled |
The estimated covariance matrix of the parameters. |
p.table |
The significance table for the parameter estimates. |
s.table |
The significance table for smooths. |
Hai Liu and Kung-Sik Chan
set.seed(11) n <- 600 x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1) x3 <- runif(n, 0, 1) f <- testfn(x1, x2)*4-mean(testfn(x1, x2)*4) + f0(x3)/2-mean(f0(x3)/2) sig <- 0.5 mu0 <- f + 3 y <- mu0 + rnorm(n, 0, sig) alpha0 <- -2.2 delta0 <- 1.2 p0 <- .Call("logit_linkinv", alpha0 + delta0 * mu0, PACKAGE = "stats") z <- rbinom(rep(1,n), 1, p0) y[z==0] <- 0 res <- cozigam(y~s(x1,x2)+s(x3), conv.crit.out = 1e-4, family = gaussian) summary(res)