intubate (version 1.0.0)

pROC: Interfaces for pROC package for data science pipelines.

Description

Interfaces to pROC functions that can be used in a pipeline implemented by magrittr.

Usage

ntbt_auc(data, ...)
ntbt_ci(data, ...)
ntbt_ci.auc(data, ...)
ntbt_ci.coords(data, ...)
ntbt_ci.se(data, ...)
ntbt_ci.sp(data, ...)
ntbt_ci.thresholds(data, ...)
ntbt_multiclass.roc(data, ...)
ntbt_plot.roc(data, ...)
ntbt_roc(data, ...)

Arguments

data

data frame, tibble, list, ...

...

Other arguments passed to the corresponding interfaced function.

Value

Object returned by interfaced function.

Details

Interfaces call their corresponding interfaced function.

Examples

## Not run: 
# library(intubate)
# library(magrittr)
# library(pROC)
# 
# ## NOTE: pROC examples below use both formula and non-formula variants.
# ##       In examples for other packages, almost always only
# ##       the formula variant is shown, but in those cases also
# ##       the non-formula variants should work.
# 
# ## ntbt_auc: Compute the area under the ROC curve
# data(aSAH)
# 
# ## Original function to interface
# auc(outcome ~ s100b, data = aSAH)
# ## For non-formula variants, either:
# ## 1) need to attach
# attach(aSAH)
# auc(outcome, s100b)
# detach()
# ## or use $
# auc(aSAH$outcome, aSAH$s100b)
# 
# ## The interface puts data as first parameter
# ## NOTE: in this case the formula version fails, and I have found no
# ##       way to trick auc into accepting the formula (so far).
# ##       Maybe (only maybe) there is a problem with auc, as formula
# ##       variant may not be used, so it was probably not
# ##       reported as a bug before. The rest of the interfaced
# ##       functions seem to work fine.
# ## ntbt_auc(data = aSAH, outcome ~ s100baSAH)
# ## The non-formula variant works fine
# ntbt_auc(aSAH, outcome, s100b)
# 
# ## so it can be used easily in a pipeline.
# #aSAH %>%
# #  ntbt_auc(outcome ~ s100baSAH)
# aSAH %>%
#   ntbt_auc(outcome, s100b)
# 
# 
# ## ntbt_ci: Compute the confidence interval of a ROC curve
# ## Original function to interface
# ci(outcome ~ s100b, data = aSAH)
# ## For non-formula variants, either:
# ## 1) need to attach
# attach(aSAH)
# ci(outcome, s100b)
# detach()
# ## or use $
# ci(aSAH$outcome, aSAH$s100b)
# 
# ## The interface puts data as first parameter
# ntbt_ci(aSAH, outcome ~ s100b)
# ntbt_ci(aSAH, outcome, s100b)
# 
# ## so it can be used easily in a pipeline.
# aSAH %>%
#   ntbt_ci(outcome ~ s100b)
# aSAH %>%
#   ntbt_ci(outcome, s100b)
# 
# 
# ## ci.auc: Compute the confidence interval of the AUC
# ## Original function to interface
# ci.auc(outcome ~ s100b, data = aSAH)
# ## For non-formula variants, either:
# ## 1) need to attach
# attach(aSAH)
# ci.auc(outcome, s100b)
# detach()
# ## or use $
# ci.auc(aSAH$outcome, aSAH$s100b)
# 
# ## The interface puts data as first parameter
# ntbt_ci.auc(aSAH, outcome ~ s100b)
# ntbt_ci.auc(aSAH, outcome, s100b)
# 
# ## so it can be used easily in a pipeline.
# aSAH %>%
#   ntbt_ci.auc(outcome ~ s100b)
# aSAH %>%
#   ntbt_ci.auc(outcome, s100b)
# 
# 
# ## ntbt_ci.coords: Compute the confidence interval of arbitrary coordinates
# ## Original function to interface
# set.seed(1)
# ci.coords(outcome ~ s100b, data = aSAH, x="best", input = "threshold", 
#           ret=c("specificity", "ppv", "tp"))
# set.seed(1)
# ci.coords(aSAH$outcome, aSAH$s100b, x="best", input = "threshold", 
#           ret=c("specificity", "ppv", "tp"))
# ## For non-formula variants, either:
# ## 1) need to attach
# attach(aSAH)
# set.seed(1)
# ci.coords(outcome, s100b, x="best", input = "threshold", 
#           ret=c("specificity", "ppv", "tp"))
# detach()
# ## or use $
# set.seed(1)
# ci.coords(aSAH$outcome, aSAH$s100b, x="best", input = "threshold", 
#           ret=c("specificity", "ppv", "tp"))
# 
# ## The interface puts data as first parameter
# set.seed(1)
# ntbt_ci.coords(aSAH, outcome ~ s100b, x="best", input = "threshold", 
#                ret=c("specificity", "ppv", "tp"))
# set.seed(1)
# ntbt_ci.coords(aSAH, outcome, s100b, x="best", input = "threshold", 
#                ret=c("specificity", "ppv", "tp"))
# 
# ## so it can be used easily in a pipeline.
# set.seed(1)
# aSAH %>%
#   ntbt_ci.coords(outcome ~ s100b, x="best", input = "threshold", 
#                  ret=c("specificity", "ppv", "tp"))
# set.seed(1)
# aSAH %>%
#   ntbt_ci.coords(outcome, s100b, x="best", input = "threshold", 
#                  ret=c("specificity", "ppv", "tp"))
# 
# 
# ## ntbt_ci.se: Compute the confidence interval of sensitivities at given specificities
# ## Original function to interface
# set.seed(1)
# ci.se(outcome ~ s100b, data = aSAH)
# ## For non-formula variants, either:
# ## 1) need to attach
# attach(aSAH)
# set.seed(1)
# ci.se(outcome, s100b)
# detach()
# ## or use $
# set.seed(1)
# ci.se(aSAH$outcome, aSAH$s100b)
# 
# ## The interface puts data as first parameter
# set.seed(1)
# ntbt_ci.se(aSAH, outcome ~ s100b)
# set.seed(1)
# ntbt_ci.se(aSAH, outcome, s100b)
# 
# ## so it can be used easily in a pipeline.
# set.seed(1)
# aSAH %>%
#   ntbt_ci.se(outcome ~ s100b)
# set.seed(1)
# aSAH %>%
#   ntbt_ci.se(outcome, s100b)
# 
# 
# ## ntbt_ci.sp: Compute the confidence interval of specificities at given sensitivities
# ## Original function to interface
# set.seed(1)
# ci.sp(outcome ~ s100b, data = aSAH)
# ## For non-formula variants, either:
# ## 1) need to attach
# attach(aSAH)
# set.seed(1)
# ci.sp(outcome, s100b)
# detach()
# ## or use $
# set.seed(1)
# ci.sp(aSAH$outcome, aSAH$s100b)
# 
# ## The interface puts data as first parameter
# set.seed(1)
# ntbt_ci.sp(aSAH, outcome ~ s100b)
# set.seed(1)
# ntbt_ci.sp(aSAH, outcome, s100b)
# 
# ## so it can be used easily in a pipeline.
# set.seed(1)
# aSAH %>%
#   ntbt_ci.sp(outcome ~ s100b, x="best", input = "threshold", 
#              ret=c("specificity", "ppv", "tp"))
# set.seed(1)
# aSAH %>%
#   ntbt_ci.sp(outcome, s100b, x="best", input = "threshold", 
#              ret=c("specificity", "ppv", "tp"))
# 
# 
# ## ntbt_ci.thresholds: Compute the confidence interval of thresholds
# ## Original function to interface
# set.seed(1)
# ci.thresholds(outcome ~ s100b, data = aSAH)
# ## For non-formula variants, either:
# ## 1) need to attach
# attach(aSAH)
# set.seed(1)
# ci.thresholds(outcome, s100b)
# detach()
# ## or use $
# set.seed(1)
# ci.thresholds(aSAH$outcome, aSAH$s100b)
# 
# ## The interface puts data as first parameter
# set.seed(1)
# ntbt_ci.thresholds(aSAH, outcome ~ s100b)
# set.seed(1)
# ntbt_ci.thresholds(aSAH, outcome, s100b)
# 
# ## so it can be used easily in a pipeline.
# set.seed(1)
# aSAH %>%
#   ntbt_ci.thresholds(outcome ~ s100b)
# set.seed(1)
# aSAH %>%
#   ntbt_ci.thresholds(outcome, s100b)
# 
# 
# ## ntbt_multiclass.roc: Multi-clmulticlass.roc Multi-class AUCass AUC
# ## Original function to interface
# multiclass.roc(gos6 ~ s100b, data = aSAH, levels = c(3, 4, 5))
# ## For non-formula variants, either:
# ## 1) need to attach
# attach(aSAH)
# multiclass.roc(gos6, s100b, levels = c(3, 4, 5))
# detach()
# ## or use $
# multiclass.roc(aSAH$gos6, aSAH$s100b, levels = c(3, 4, 5))
# 
# ## The interface puts data as first parameter
# ntbt_multiclass.roc(aSAH, gos6 ~ s100b, levels = c(3, 4, 5))
# ntbt_multiclass.roc(aSAH, gos6, s100b, levels = c(3, 4, 5))
# 
# ## so it can be used easily in a pipeline.
# aSAH %>%
#   ntbt_multiclass.roc(gos6 ~ s100b, levels = c(3, 4, 5))
# aSAH %>%
#   ntbt_multiclass.roc(gos6, s100b, levels = c(3, 4, 5))
# 
# 
# ## ntbt_plot.roc: Plot a ROC curve
# ## Original function to interface
# plot.roc(outcome ~ s100b, data = aSAH, type="b", pch=21, col="blue", bg="grey")
# ## For non-formula variants, either:
# ## 1) need to attach
# attach(aSAH)
# plot.roc(outcome, s100b, type="b", pch=21, col="blue", bg="grey")
# detach()
# ## or use $
# plot.roc(aSAH$outcome, aSAH$s100b, type="b", pch=21, col="blue", bg="grey")
# 
# ## The interface puts data as first parameter
# ntbt_plot.roc(aSAH, outcome ~ s100b, type="b", pch=21, col="blue", bg="grey")
# ntbt_plot.roc(aSAH, outcome, s100b, type="b", pch=21, col="blue", bg="grey")
# 
# ## so it can be used easily in a pipeline.
# aSAH %>%
#   ntbt_plot.roc(outcome ~ s100b, type="b", pch=21, col="blue", bg="grey")
# aSAH %>%
#   ntbt_plot.roc(outcome, s100b, type="b", pch=21, col="blue", bg="grey")
# 
# 
# ## ntbt_roc: Build a ROC curve
# ## Original function to interface
# roc(outcome ~ s100b, data = aSAH, type="b", pch=21, col="blue", bg="grey")
# ## For non-formula variants, either:
# ## 1) need to attach
# attach(aSAH)
# roc(outcome, s100b, type="b", pch=21, col="blue", bg="grey")
# detach()
# ## or use $
# roc(aSAH$outcome, aSAH$s100b, type="b", pch=21, col="blue", bg="grey")
# 
# ## The interface puts data as first parameter
# ntbt_roc(aSAH, outcome ~ s100b, type="b", pch=21, col="blue", bg="grey")
# ntbt_roc(aSAH, outcome, s100b, type="b", pch=21, col="blue", bg="grey")
# 
# ## so it can be used easily in a pipeline.
# aSAH %>%
#   ntbt_roc(outcome ~ s100b, type="b", pch=21, col="blue", bg="grey")
# aSAH %>%
#   ntbt_roc(outcome, s100b, type="b", pch=21, col="blue", bg="grey")
# ## End(Not run)