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Load a Prebuilt WSS Prediction Model

Source: R/prebuilt_wss.R
load_prebuilt_wss_model.Rd

Some prebuilt reference sets ship a fitted joinet model (a two-layer elastic net) that predicts all of that reference set's module scores from a small set of covariates, without needing to measure the full proteomic panel. See predict_WSS() to predict from this model directly.

Usage

load_prebuilt_wss_model(prebuilt = "core_AD_plasma_biomarkers")

Arguments

prebuilt

Name of a prebuilt reference set. See list_prebuilt_wss() for available options.

Value

A list with components model (the fitted joinet object), outcomes (the module names it predicts), x_cols (the required predictor columns, in order), and variant; or NULL if the given reference set has no associated prediction model.

See also

predict_WSS(), list_prebuilt_wss()

Examples

model_info <- load_prebuilt_wss_model()
model_info$variant
#> [1] "Biomarkers_Age_Sex"
model_info$x_cols
#>  [1] "Age"                     "Gender"                 
#>  [3] "PlasmaPTau181"           "PlasmaAB142P"           
#>  [5] "PlasmaAB140P"            "PlasmaABRatio"          
#>  [7] "PlasmapTau217"           "PlasmapTau217_AB42Ratio"
#>  [9] "PlasmaGFAP"              "PlasmaNfL"              
head(model_info$outcomes)
#> [1] "Merged.M17"        "Merged.M7"         "Merged.M4"        
#> [4] "PlasmaPTau181.M26" "Merged.M8"         "Merged.M1"