A thin wrapper around gam, however, some arguments are
prespecified:
family=poisson(), offset=data$offset and method="REML".
The first two can not be overriden. The method argument
can be specified as usual, but defaults to GCV.cp in gam.
pamm(
formula,
data = list(),
method = "REML",
...,
trafo_args = NULL,
engine = "gam"
)is.pamm(x)
# S3 method for pamm
print(x, ...)
# S3 method for pamm
summary(object, ...)
# S3 method for pamm
plot(x, ...)
A GAM formula, or a list of formulae (see formula.gam and also gam.models).
These are exactly like the formula for a GLM except that smooth terms, s, te, ti
and t2, can be added to the right hand side to specify that the linear predictor depends on smooth functions of predictors (or linear functionals of these).
A data frame or list containing the model response variable and
covariates required by the formula. By default the variables are taken
from environment(formula): typically the environment from
which gam is called.
The smoothing parameter estimation method. "GCV.Cp" to use GCV for unknown scale parameter and
Mallows' Cp/UBRE/AIC for known scale. "GACV.Cp" is equivalent, but using GACV in place of GCV. "REML"
for REML estimation, including of unknown scale, "P-REML" for REML estimation, but using a Pearson estimate
of the scale. "ML" and "P-ML" are similar, but using maximum likelihood in place of REML. Beyond the
exponential family "REML" is the default, and the only other option is "ML".
Further arguments passed to engine.
A named list. If data is not in PED format, as_ped
will be called internally with arguments provided in trafo_args.
Character name of the function that will be called to fit the
model. The intended entries are either "gam" or "bam"
(both from package mgcv).
Any R object.
An object of class pamm as returned by pamm.
# NOT RUN {
ped <- tumor[1:100, ] %>%
as_ped(Surv(days, status) ~ complications, cut = seq(0, 3000, by = 50))
pam <- pamm(ped_status ~ s(tend) + complications, data = ped)
summary(pam)
## Alternatively
pamm(
ped_status ~ s(tend) + complications,
data = tumor[1:100, ],
trafo_args = list(formula = Surv(days, status)~complications))
# }
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