Commit 2516d9b1 by Andreas Leha

### new: reference as generated by roxygen2

parent 7e418b43
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/confmat.R \name{ACCfromConfusion} \alias{ACCfromConfusion} \title{Calculate Accurcy Given the Confusion Matrix} \usage{ ACCfromConfusion(x) } \arguments{ \item{x}{2x2 matrix or data.frame. Typically the output of \code{link[base]{table}}} } \value{ numerical. the accuracy } \description{ Calculate Accurcy Given the Confusion Matrix } \examples{ ## draw data n <- 50 truth <- sample(c("control", "case"), size = n, replace = TRUE) prediction <- sample(c("control", "case"), size = n, replace = TRUE) ## confusion matrix table(truth, prediction) ACCfromConfusion(table(truth, prediction)) } \author{ Dr. Andreas Leha }
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/confmat.R \name{NPVfromConfusion} \alias{NPVfromConfusion} \title{Calculate NPV Given the Confusion Matrix} \usage{ NPVfromConfusion(x, case = 2, predictionsin = "rows") } \arguments{ \item{x}{2x2 matrix or data.frame. Typically the output of \code{link[base]{table}}} \item{case}{class label of the cases.} \item{predictionsin}{character in \code{c("rows", "cols")}.} } \value{ numerical. the NPV } \description{ Calculate NPV Given the Confusion Matrix } \examples{ ## draw data n <- 50 truth <- sample(c("control", "case"), size = n, replace = TRUE) prediction <- sample(c("control", "case"), size = n, replace = TRUE) ## confusion matrix table(truth, prediction) ## predictions in columns NPVfromConfusion(table(truth, prediction), case = "case", predictionsin = "cols") ## predictions in rows NPVfromConfusion(table(prediction, truth), case = "case", predictionsin = "cols") } \author{ Dr. Andreas Leha }
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/confmat.R \name{PERFfromConfusion} \alias{PERFfromConfusion} \title{Calculate Performance Given the Confusion Matrix} \usage{ PERFfromConfusion(x, case = 2, predictionsin = "rows") } \arguments{ \item{x}{2x2 matrix or data.frame. Typically the output of \code{link[base]{table}}} \item{case}{class label of the cases.} \item{predictionsin}{character in \code{c("rows", "cols")}.} } \value{ data.frame. the accuracy, sensitivity, specificity, PPV, NPV } \description{ Calculate Performance Given the Confusion Matrix } \examples{ ## draw data n <- 50 truth <- sample(c("control", "case"), size = n, replace = TRUE) prediction <- sample(c("control", "case"), size = n, replace = TRUE) ## confusion matrix table(truth, prediction) ## predictions in columns PERFfromConfusion(table(truth, prediction), case = "case", predictionsin = "cols") ## predictions in rows PERFfromConfusion(table(prediction, truth), case = "case", predictionsin = "cols") } \author{ Dr. Andreas Leha }
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/confmat.R \name{PPVfromConfusion} \alias{PPVfromConfusion} \title{Calculate Positive Predictive Value (PPV) Given the Confusion Matrix} \usage{ PPVfromConfusion(x, case = 2, predictionsin = "rows") } \arguments{ \item{x}{2x2 matrix or data.frame. Typically the output of \code{link[base]{table}}} \item{case}{class label of the cases.} \item{predictionsin}{character in \code{c("rows", "cols")}.} } \value{ numerical. the PPV } \description{ Calculate Positive Predictive Value (PPV) Given the Confusion Matrix } \examples{ ## draw data n <- 50 truth <- sample(c("control", "case"), size = n, replace = TRUE) prediction <- sample(c("control", "case"), size = n, replace = TRUE) ## confusion matrix table(truth, prediction) ## predictions in columns PPVfromConfusion(table(truth, prediction), case = "case", predictionsin = "cols") ## predictions in rows PPVfromConfusion(table(prediction, truth), case = "case", predictionsin = "cols") } \author{ Dr. Andreas Leha }
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/confmat.R \name{SENSfromConfusion} \alias{SENSfromConfusion} \title{Calculate Sensitivity Given the Confusion Matrix} \usage{ SENSfromConfusion(x, case = 2, predictionsin = "rows") } \arguments{ \item{x}{2x2 matrix or data.frame. Typically the output of \code{link[base]{table}}} \item{case}{class label of the cases.} \item{predictionsin}{character in \code{c("rows", "cols")}.} } \value{ numerical. the sensitivity } \description{ Calculate Sensitivity Given the Confusion Matrix } \examples{ ## draw data n <- 50 truth <- sample(c("control", "case"), size = n, replace = TRUE) prediction <- sample(c("control", "case"), size = n, replace = TRUE) ## confusion matrix table(truth, prediction) ## predictions in columns SENSfromConfusion(table(truth, prediction), case = "case", predictionsin = "cols") ## predictions in rows SENSfromConfusion(table(prediction, truth), case = "case", predictionsin = "cols") } \author{ Dr. Andreas Leha }
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/confmat.R \name{SPECfromConfusion} \alias{SPECfromConfusion} \title{Calculate Specificity Given the Confusion Matrix} \usage{ SPECfromConfusion(x, case = 2, predictionsin = "rows") } \arguments{ \item{x}{2x2 matrix or data.frame. Typically the output of \code{link[base]{table}}} \item{case}{class label of the cases.} \item{predictionsin}{character in \code{c("rows", "cols")}.} } \value{ numerical. the specificity } \description{ Calculate Specificity Given the Confusion Matrix } \examples{ ## draw data n <- 50 truth <- sample(c("control", "case"), size = n, replace = TRUE) prediction <- sample(c("control", "case"), size = n, replace = TRUE) ## confusion matrix table(truth, prediction) ## predictions in columns SPECfromConfusion(table(truth, prediction), case = "case", predictionsin = "cols") ## predictions in rows SPECfromConfusion(table(prediction, truth), case = "case", predictionsin = "cols") } \author{ Dr. Andreas Leha }
man/binclaperf.Rd 0 → 100644
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/pinclaperf-package.R \docType{package} \name{binclaperf} \alias{binclaperf} \alias{binclaperf-package} \title{binclaperf} \description{ binclaperf }
man/confmat2df.Rd 0 → 100644
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/confmat.R \name{confmat2df} \alias{confmat2df} \title{Format Matrix as data.frame} \usage{ confmat2df(x) } \arguments{ \item{x}{2x2 matrix.} } \value{ data.frame with proper column and row names } \description{ Format Matrix as data.frame } \examples{ ## draw data n <- 50 truth <- sample(c("control", "case"), size = n, replace = TRUE) prediction <- sample(c("control", "case"), size = n, replace = TRUE) ## confusion matrix table(truth, prediction) confmat2df(table(truth, prediction)) } \author{ Dr. Andreas Leha }
man/pROCaucdf.Rd 0 → 100644
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/roc.R \name{pROCaucdf} \alias{pROCaucdf} \title{Extract Youden from pROC Object} \usage{ pROCaucdf(roc) } \arguments{ \item{roc}{roc objcet from \code{\link[pROC]{roc}}} } \value{ data.frame with 1 row of columns sensitivities, specificities, thresholds } \description{ Extract Youden from pROC Object } \examples{ ## draw data n <- 50 truth <- sample(c("control", "case"), size = n, replace = TRUE) prediction <- runif(n = n) ## generate roc object without ci rocobj <- pROC::roc(truth, prediction) rocobj pROCaucdf(rocobj) ## generate roc object with ci rocobj <- pROC::roc(truth, prediction, ci = TRUE) rocobj pROCaucdf(rocobj) } \author{ Dr. Andreas Leha }
man/pROCdf.Rd 0 → 100644
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/roc.R \name{pROCdf} \alias{pROCdf} \title{Extract data.frame From pROC Object} \usage{ pROCdf(roc) } \arguments{ \item{roc}{roc objcet from \code{\link[pROC]{roc}}} } \value{ data.frame with columns sensitivities, specificities, thresholds } \description{ Extract data.frame From pROC Object } \examples{ ## draw data n <- 50 truth <- sample(c("control", "case"), size = n, replace = TRUE) prediction <- runif(n = n) rocobj <- pROC::roc(truth, prediction) rocobj pROCdf(rocobj) } \author{ Dr. Andreas Leha }
man/pROCyouden.Rd 0 → 100644
 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/roc.R \name{pROCyouden} \alias{pROCyouden} \title{Extract Youden from pROC Object} \usage{ pROCyouden(roc) } \arguments{ \item{roc}{roc objcet from \code{\link[pROC]{roc}}} } \value{ data.frame with 1 row of columns sensitivities, specificities, thresholds } \description{ Extract Youden from pROC Object } \examples{ ## draw data n <- 50 truth <- sample(c("control", "case"), size = n, replace = TRUE) prediction <- runif(n = n) rocobj <- pROC::roc(truth, prediction) rocobj pROCyouden(rocobj) } \author{ Dr. Andreas Leha }
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