From b07904d64f81ea04bb89442f11c5cd17b11cadbb Mon Sep 17 00:00:00 2001 From: edhell Date: Fri, 27 Jan 2023 10:33:05 -0300 Subject: [PATCH] README typos --- R/json_files.R | 2 +- README.Rmd | 10 +++++----- README.md | 18 +++++++++--------- 3 files changed, 15 insertions(+), 15 deletions(-) diff --git a/R/json_files.R b/R/json_files.R index 991fe46..74ee2cb 100644 --- a/R/json_files.R +++ b/R/json_files.R @@ -535,7 +535,7 @@ calculateFitStat <- function(validadedf, if (nrow(data[[i]] != 0)) { - predictions <- ROCR::prediction(data[[i]][,2],data[[i]][,1], label.ordering = label.ordering) + predictions <- ROCR::prediction(data[[i]][,2],data[[i]][,1], label.ordering = label.ordering) accuracyRoc <- ROCR::performance(predictions, 'acc') tpr_fpr <- ROCR::performance(predictions,"tpr","fpr") diff --git a/README.Rmd b/README.Rmd index 75cf5e2..ddda8ae 100644 --- a/README.Rmd +++ b/README.Rmd @@ -232,16 +232,16 @@ diags <- diagnosticsJson(validadedf = scoreddf[scoreddf$partition == 3,], path = path) ## safely ignore warning, knitr bug -## since R sasctl can't automatically generate an score code, you can use this -## with a sample, which has to be edited manually to the correct. -## for this example you can get the score code sample here: https://gitlab.sas.com/edhell/mm-r-model +## since R sasctl can't automatically generate an score code (yet), you can use +## this function to create a sample, which has to be edited manually to work properly + create_scoreSample(path, openFile = FALSE) -# + +## writing other files write_in_out_json(hmeq[,-1], input = TRUE, path = path) -## we don't need the partition info write_in_out_json(scoreddf[-4], input = FALSE, path = path) write_fileMetadata_json(scoreCodeName = "scoreCode.R", diff --git a/README.md b/README.md index c5d90ac..d06aab4 100644 --- a/README.md +++ b/README.md @@ -12,10 +12,10 @@ 2. [Installation](#installation) 3. [Session](#session) 4. [Examples](#examples) - - [PMML to SAS Example](#pmml-to-sas-example) - - [A native R model example](#a-native-r-model-example) - - [vPOST and vGET convenient - functions](#vpost-and-vget-convenient-functions) + - [PMML to SAS Example](#pmml-to-sas-example) + - [A native R model example](#a-native-r-model-example) + - [vPOST and vGET convenient + functions](#vpost-and-vget-convenient-functions) 5. [Model Management helpers](#model-management-helpers) ## Overview @@ -214,16 +214,16 @@ diags <- diagnosticsJson(validadedf = scoreddf[scoreddf$partition == 3,], path = path) ## safely ignore warning, knitr bug -## since R sasctl can't automatically generate an score code, you can use this -## with a sample, which has to be edited manually to the correct. -## for this example you can get the score code sample here: https://gitlab.sas.com/edhell/mm-r-model +## since R sasctl can't automatically generate an score code (yet), you can use +## this function to create a sample, which has to be edited manually to work properly + create_scoreSample(path, openFile = FALSE) -# + +## writing other files write_in_out_json(hmeq[,-1], input = TRUE, path = path) -## we don't need the partition info write_in_out_json(scoreddf[-4], input = FALSE, path = path) write_fileMetadata_json(scoreCodeName = "scoreCode.R",