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Strip rawdata from a generalized additive model

Source: R/strip_rawdata.R
strip_rawdata.Rd

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.

Usage

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

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

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

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

# S3 method for 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).

Value

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)
#> Meta-analysis of GAMs from  cohorts, using method FE.
#> 
#> Smooth terms analyzed: s(x1).
#> 
#> 
#> 

## 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.
if (FALSE) {
  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")
}