Description Usage Arguments Details Value
The do.rf function runs the random forest portion of the code
1 2 3 4 5 |
trap.data |
A data frame containing human cases, mosquito infection rate (if applicable), and independent variables for prediction of human cases and/or mosquito infection rates |
dep.var |
The field name containing the dependent variable to analyze |
independent.vars |
The field names of the independent variables to include in the analysis Variables not listed here but present in the data frame will be excluded from the analysis. |
results.path |
The base path in which to place the modeling results. Some models will create sub-folders for model specific results |
do.spatial |
Whether or not to do spatial crossvalidation (can be time-consuming) |
create.ci.null |
Whether or not to generate null model results |
label |
A label for the analysis run |
response.type |
Whether data should be treated as continuous (mosquito rates, number of cases) or binary (0 or 1). |
exploratory |
Whether to identify the best model (exploratory = TRUE), or whether to run the random forest with all input independent variables (exploratory = FALSE) |
input.seed |
A seed for the random number generator to ensure that the results are repeatable |
temporal.field |
The field containing the temporal units |
spatial.field |
The field containing the spatial units |
quantile.model |
Whether (1) or not (0) to use a quantile random forest for the final model output. All other calculations and model fitting use the standard randomForest package. |
display.messages |
Whether or not update messages should be output |
Majority of code was writen in wnv_hlpr.R; January - June 2018. Transferred to this file & modified beginning in March 2019. Code adapted to use common inputs with the ArboMAP code of Davis & Wimberly
A list containing:
MODEL | the random forest prediction model |
TEMPORAL.ACCURACY | an accuracy assessment using leave one year out cross-validation |
SPATIAL.ACCURACY | an accuracy assessment based on leave one location out cross-validation |
TEMPORAL.NULL | Null model results based on estimates across time |
SPATIAL.NULL | Null model results based on estimates across space |
RETAINED.VARS | Variables retained in the final prediction model |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.