make_newdata {pammtools}

Construct a data frame suitable for prediction

Description

This functions provides a flexible interface to create a data set that can be plugged in as newdata argument to a suitable predict function (or similar). The function is particularly useful in combination with one of the add_* functions, e.g., add_term, add_hazard, etc.

Usage

make_newdata(x, ...)

## Default S3 method:
make_newdata(x, ...)

## S3 method for class 'ped'
make_newdata(x, ...)

## S3 method for class 'fped'
make_newdata(x, ...)

Arguments

x

A data frame (or object that inherits from data.frame).

...

Covariate specifications (expressions) that will be evaluated by looking for variables in x. Must be of the form z = f(z) where z is a variable in the data set and f a known function that can be usefully applied to z. Note that this is also necessary for single value specifications (e.g. age = c(50)). For data in PED (piece-wise exponential data) format, one can also specify the time argument, but see "Details" an "Examples" below.

Details

Depending on the type of variables in x, mean or modus values will be used for variables not specified in ellipsis (see also sample_info). If x is an object that inherits from class ped, useful data set completion will be attempted depending on variables specified in ellipsis. This is especially useful, when creating a data set with different time points, e.g. to calculate survival probabilities over time (add_surv_prob) or to calculate a time-varying covariate effects (add_term). To do so, the time variable has to be specified in ..., e.g., tend = seq_range(tend, 20). The problem with this specification is that not all values produced by seq_range(tend, 20) will be actual values of tend used at the stage of estimation (and in general, it will often be tedious to specify exact tend values). make_newdata therefore finds the correct interval and sets tend to the respective interval endpoint. For example, if the intervals of the PED object are (0,1], (1,2] then tend = 1.5 will be set to 2 and the remaining time-varying information (e.g. offset) completed accordingly. See examples below.

Examples

# General functionality
tumor %>% make_newdata()
tumor %>% make_newdata(age=c(50))
tumor %>% make_newdata(days=seq_range(days, 3), age=c(50, 55))
tumor %>% make_newdata(days=seq_range(days, 3), status=unique(status), age=c(50, 55))
# mean/modus values of unspecified variables are calculated over whole data
tumor %>% make_newdata(sex=unique(sex))
tumor %>% group_by(sex) %>% make_newdata()

# Examples for PED data
ped <- tumor %>% slice(1:3) %>% as_ped(Surv(days, status)~., cut = c(0, 500, 1000))
ped %>% make_newdata(age=c(50, 55))

# if time information is specified, other time variables will be specified
# accordingly and offset calculated correctly
ped %>% make_newdata(tend = c(1000), age = c(50, 55))
ped %>% make_newdata(tend = unique(tend))
ped %>% group_by(sex) %>% make_newdata(tend = unique(tend))

# tend is set to the end point of respective interval:
ped <- tumor %>% as_ped(Surv(days, status)~.)
seq_range(ped$tend, 3)
make_newdata(ped, tend = seq_range(tend, 3))