View source: R/bootstrap_MRF.R
bootstrap_MRF | R Documentation |
This function runs MRFcov
models multiple times to capture uncertainty
in parameter esimates. The dataset is shuffled and missing
values (if found) are imputed in each bootstrap iteration.
bootstrap_MRF(
data,
n_bootstraps,
sample_seed,
symmetrise,
n_nodes,
n_cores,
n_covariates,
family,
sample_prop,
spatial = FALSE,
coords = NULL
)
data |
Dataframe. The input data where the |
n_bootstraps |
Positive integer. Represents the total number of bootstrap samples
to test. Default is |
sample_seed |
Numeric. Used as the seed value for generating bootstrap replicates, allowing users to generate replicated datasets on different systems. Default is a random seed |
symmetrise |
The method to use for symmetrising corresponding parameter estimates
(which are taken from separate regressions). Options are |
n_nodes |
Positive integer. The index of the last column in |
n_cores |
Integer. The number of cores to spread the job across using
|
n_covariates |
Positive integer. The number of covariates in |
family |
The response type. Responses can be quantitative continuous ( |
sample_prop |
Positive probability value indicating the proportion of rows to sample from
|
spatial |
Logical. If |
coords |
A two-column |
MRFcov
models are fit via cross-validation using
cv.glmnet
. For each model, the data
is bootstrapped
by shuffling row observations and fitting models to a subset of observations
to account for uncertainty in parameter estimates.
Parameter estimates from the set of bootstrapped models are summarised
to present means and confidence intervals (as 95 percent quantiles).
A list
containing:
direct_coef_means
: dataframe
containing mean coefficient values taken from all
bootstrapped models across the iterations
direct_coef_upper90
and direct_coef_lower90
: dataframe
s
containing coefficient 95 percent and 5 percent quantiles taken from all
bootstrapped models across the iterations
indirect_coef_mean
: list
of symmetric matrices
(one matrix for each covariate) containing mean effects of covariates
on pairwise interactions
mean_key_coefs
: list
of matrices of length n_nodes
containing mean covariate coefficient values and their relative importances
(using the formula x^2 / sum (x^2)
taken from all bootstrapped models across iterations. Only coefficients
with mean relative importances >0.01
are returned. Note, relative importance are only
useful if all covariates are on a similar scale.
mod_type
: A character stating the type of model that was fit
(used in other functions)
mod_family
: A character stating the family of model that was fit
(used in other functions)
poiss_sc_factors
: A vector of the square-root mean scaling factors
used to standardise poisson
variables (only returned if family = "poisson"
)
MRFcov
, MRFcov_spatial
,
cv.glmnet
data("Bird.parasites")
# Perform 2 quick bootstrap replicates using 70% of observations
bootedCRF <- bootstrap_MRF(data = Bird.parasites,
n_nodes = 4,
family = 'binomial',
sample_prop = 0.7,
n_bootstraps = 2)
# Small example of using spatial coordinates for a spatial CRF
Latitude <- sample(seq(120, 140, length.out = 100), nrow(Bird.parasites), TRUE)
Longitude <- sample(seq(-19, -22, length.out = 100), nrow(Bird.parasites), TRUE)
coords <- data.frame(Latitude = Latitude, Longitude = Longitude)
bootedSpatial <- bootstrap_MRF(data = Bird.parasites, n_nodes = 4,
family = 'binomial',
spatial = TRUE,
coords = coords,
sample_prop = 0.5,
n_bootstraps = 2)
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