View source: R/strip_rawdata.R
strip_rawdata | R Documentation |
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, ...)
model |
A model fitted using |
path |
Optional path in which to save the object as a |
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 |
... |
Other arguments (not used). |
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.
Model object with individual participant data removed.
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
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)
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