Use this function to return a list of tuning parameters to analyze your diagnostic test
Usage
dx(
data,
classlabels = c("Negative", "Positive"),
threshold_range = NA,
outcome_label = NA,
pred_varname,
true_varname,
setthreshold = 0.5,
poslabel = 1,
grouping_variables = NA,
prevalence = NA,
citype = "exact",
bootreps = 2000,
bootseed = 20191015,
doboot = FALSE,
direction = "auto",
...
)Arguments
- data
A tbl.
- classlabels
Labels for predicted variable. Needs to be 0, 1 order.
- threshold_range
Optional. A numeric vector of thresholds to loop over.
- outcome_label
Label for outcome (string)
- pred_varname
Column name containing AI prediction (string)
- true_varname
Column name containing AI reference standard (string)
- setthreshold
A numeric value representing the threshold used to identify AI prediction
- poslabel
Positive class. Variable should be coded as 0/1 with 1 being the event
- grouping_variables
Character vector of variable names to be summarized by. Variables are converted to factors if not already one.
- prevalence
Numeric value between 0 and 1, representing a target prevalence for additional NPV and PPV calculations.
- citype
Confidence interval type.
- bootreps
Number of bootstrap samples used to generate F1 score CI
- bootseed
Seed value to be used when calculating bootsraped CI's
- doboot
Logical. Generate bootstrap estimate of F1 confidence interval?
- direction
Direction for roc comparison. See ?pROC::roc
- ...
currently unused