
Prepare response variable data for nested ManyEcoEvo dataset - out of sample predictions only
Source:R/prepare_response_variables_yi.R
prepare_response_variables_yi.RdPrepare response variable data for nested ManyEcoEvo dataset - out of sample predictions only
Usage
prepare_response_variables_yi(
ManyEcoEvo,
estimate_type = character(1L),
param_table = NULL
)Arguments
- ManyEcoEvo
Complete ManyEcoEvo dataset containing nested datasets for each different analysis and exclusion set dataset
- estimate_type
A character string of length 1, equal to either "yi", "y25", "y50", "y75", indicating what type of estimates are being prepared.
- param_table
A table of parameters \(mean, sd\) for most response variables used by analysts. This tibble is pulled from the named object exported by
ManyEcoEvo::. but can be overwritten with the users's ownparam_tabledataset.
Details
Operates on nested list-columns of data. The function back-transforms the response variables from the link to the response scale for each dataset in the ManyEcoEvo dataset. The back-transformed data is stored in a list-column called back_transformed_data. It is useful for when wanting to conduct a meta-analysis on the response scale, e.g. for the Eucalyptus count data.
estimate_type is used to specify the type of estimate to be standardised and parsed to back_transform_response_vars_yi()
See also
back_transform_response_vars_yi(). To be called prior to generate_yi_subsets().
Other targets-pipeline functions:
generate_rating_subsets(),
prepare_ManyEcoEvo(),
prepare_ManyEcoEvo_yi(),
prepare_review_data()
Other Multi-dataset Wrapper Functions:
apply_VZ_exclusions(),
compute_MA_inputs(),
generate_exclusion_subsets(),
generate_outlier_subsets(),
generate_rating_subsets(),
generate_yi_subsets(),
make_viz(),
meta_analyse_datasets(),
prepare_ManyEcoEvo(),
prepare_ManyEcoEvo_yi(),
prepare_response_variables(),
summarise_analysis_types(),
summarise_conclusions(),
summarise_model_composition(),
summarise_reviews(),
summarise_sorensen_index(),
summarise_variable_counts()