A thin wrapper around `gam`

, however, some arguments are
prespecified:
`family=poisson()`

and `offset=data$offset`

.
These two can not be overwritten. In many cases it will also be advisable to
set `method="REML"`

.

```
pamm(formula, data = list(), ..., 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, ...)
```

## Arguments

- formula
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).

- data
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.

- ...
Further arguments passed to `engine`

.

- trafo_args
A named list. If data is not in PED format, `as_ped`

will be called internally with arguments provided in `trafo_args`

.

- engine
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`

).

- x
Any R object.

- object
An object of class `pamm`

as returned by `pamm`

.

## Examples

```
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)
#>
#> Family: poisson
#> Link function: log
#>
#> Formula:
#> ped_status ~ s(tend) + complications
#>
#> Parametric coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -8.0233 0.2554 -31.412 <2e-16 ***
#> complicationsyes 0.2497 0.3424 0.729 0.466
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Approximate significance of smooth terms:
#> edf Ref.df Chi.sq p-value
#> s(tend) 4.144 5.04 8.312 0.138
#>
#> R-sq.(adj) = -0.0245 Deviance explained = 3.79%
#> UBRE = -0.79476 Scale est. = 1 n = 1937
## Alternatively
pamm(
ped_status ~ s(tend) + complications,
data = tumor[1:100, ],
trafo_args = list(formula = Surv(days, status)~complications))
#>
#> Family: poisson
#> Link function: log
#>
#> Formula:
#> ped_status ~ s(tend) + complications
#>
#> Estimated degrees of freedom:
#> 1.75 total = 3.75
#>
#> UBRE score: -0.8570005
```