strip_rawdata function

Strip rawdata from a generalized additive model

This function removes all individual participant data from a generalized additive model object, while keeping aggregated quantities. The resulting object can be shared without exposing individual participant data.

strip_rawdata(model, path = NULL, save_ranges = TRUE, ...)

## S3 method for class 'list'
strip_rawdata(model, path = NULL, save_ranges = TRUE, ...)

## S3 method for class 'gamm'
strip_rawdata(model, path = NULL, save_ranges = TRUE, ...)

## S3 method for class 'bam'
strip_rawdata(model, path = NULL, save_ranges = TRUE, ...)

## S3 method for class 'gam'
strip_rawdata(model, path = NULL, save_ranges = TRUE, ...)

Arguments

• model: A model fitted using mgcv::gam, mgcv::bam, mgcv::gamm, or gamm4::gamm4.
• path: Optional path in which to save the object as a .rds file.
• save_ranges: Logical specifying whether to save the ranges of each variable used by the model. For numeric variables this amounts to the minimum and maximum, and for factors all levels are saved. The values will be in the list element var.summary of the returned object.
• ...: Other arguments (not used).

Returns

Model object with individual participant data removed.

Details

Thin plate regression splines (bs='tp' and bs='ts') and Duchon splines bs='ds'

are currently not supported, since for these splines mgcv

requires the unique values of the explanatory variables for each smooth term for the predict

method to work. Future updates to this package will fix this.

Methods (by class)

• strip_rawdata(list): Strip rawdata from list object returned by gamm4
• strip_rawdata(gamm): Strip rawdata from gamm object
• strip_rawdata(bam): Strip rawdata from gam object
• strip_rawdata(gam): Strip rawdata from gam object

Examples

library(metagam)
library(mgcv)

## Create 5 datasets
set.seed(1234)
datasets <- lapply(1:5, function(x) gamSim(scale = 5, verbose = FALSE))

## Fit a GAM in each dataset, then use strip_rawdata() to remove
## individual participant data
models <- lapply(datasets, function(dat){
  ## This uses the gam() function from mgcv
  model <- gam(y ~ s(x0, bs = "cr") + s(x1, bs = "cr") + s(x2, bs = "cr"), data = dat)
  ## This uses strip_rawdata() from metagam
  strip_rawdata(model)
})

## Next, we meta-analyze the models.
## It is often most convenient to analyze a single term at a time. We focus on s(x1).
meta_analysis <- metagam(models, terms = "s(x1)", grid_size = 30)

## We can print some information
summary(meta_analysis)

## We can plot the meta-analytic fit together with the individual fits
plot(meta_analysis)
plot(meta_analysis, ci = "pointwise")

## We can also compute p-values and simultaneous confidence intervals, by setting the nsim argument.
## For details, see the separate vignette.
## Not run:

  meta_analysis <- metagam(models, terms = "s(x0)", grid_size = 30, nsim = 1000)
  summary(meta_analysis)
  plot(meta_analysis, ci = "both")
  plot(meta_analysis, ci = "simultaneous")
## End(Not run)