updating vignette

Author: edwindj

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: errorlocate
 Type: Package
 Title: Locate Errors with Validation Rules
-Version: 1.1.1
+Version: 1.1.2
 Authors@R: c(person("Edwin", "de Jonge", email = "[email protected]", role = c("aut", "cre"), comment=c(ORCID="0000-0002-6580-4718")),
              person("Mark", "van der Loo", email = "[email protected]", role = c("aut")))
 Description: Errors in data can be located and removed using validation rules from package 
@@ -21,7 +21,7 @@ Suggests:
     covr,
     knitr,
     rmarkdown
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Encoding: UTF-8
 Collate: 
     'MipRules.R'

NAMESPACE

@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(as.character,dnf)
+S3method(as.character,mip_rule)
 S3method(as.data.frame,errorlocation)
 S3method(as.expression,dnf)
 S3method(print,dnf)

R/MipRules.R

@@ -1,6 +1,6 @@
 #' Create a mip object from a validator object
 #'
-#' Create a mip object from [validator()] object.
+#' Create a mip object from [validate::validator()] object.
 #' This is a utility class that translates a validor object into a mixed integer problem that
 #' can be solved.
 #' Most users should use [locate_errors()] which will handle all translation and execution

R/errorlocate-package.r

@@ -3,7 +3,7 @@
 #' Find errors in data given a set of validation rules.
 #' The `errorlocate` helps to identify obvious errors in raw datasets.
 #'
-#' It works in tandem with the package [validate()].
+#' It works in tandem with the package `validate`.
 #' With `validate` you formulate data validation rules to which the data must comply.
 #' For example:
 #'

R/errorlocation.R

@@ -12,11 +12,11 @@
 #'
 #'
 #' Current implementation assumes that errors are record based. The error locations can be retrieved
-#' using the method [values()] and are a matrix of
+#' using the method [validate::values()] and are a matrix of
 #' rows and columns, with the same dimensions are the `data.frame` that was checked.
 #' For errors that are purely column based, or dataset based, errorlocations will return a matrix with all
 #' rows or cells set to `TRUE`.
-#' The [values()] return `NA` for missing values.
+#' The [validate::values()] return `NA` for missing values.
 
 #' @section Fields:
 #'

R/linear.R

@@ -10,7 +10,7 @@ LOGS <- c("log", "log1p", "log10", "log2")
 #' categorical and conditional rules to be used in finding errors. Other rule types
 #' are ignored during error finding.
 #' @export
-#' @param x [validator()] object containing data validation rules
+#' @param x [validate::validator()] object containing data validation rules
 #' @param ... not used
 #' @return `logical` indicating which rules are (purely) linear.
  #' @family rule type

R/mip_lpsolve.R

@@ -6,7 +6,7 @@
 #' @param  rules mip rules
 #' @param objective function
 #' @param eps accuracy for equality/inequality
-#' @param ... additional [lp.control()] parameters that are set for the mip problem
+#' @param ... additional [lpSolveAPI::lp.control()] parameters that are set for the mip problem
 translate_mip_lp <- function( rules
                             , objective=NULL
                             , eps = 1e-3

R/mip_rule.R

@@ -18,6 +18,7 @@ mip_rule <- function(a, op, b, rule, type, weight=Inf, ...){
            )
 }
 
+#' @export
 as.character.mip_rule <- function(x, ...){
   a <- paste0(x$a, "*", names(x$a), collapse= ' + ')
 
@@ -29,6 +30,7 @@ as.character.mip_rule <- function(x, ...){
   paste0(a, " ",x$op, " ", x$b, sep = "")
 }
 
+#' @export
 print.mip_rule <- function(x, ...){
   a <- paste0(x$a, "*", names(x$a), collapse= ' + ')
 

R/replace-errors.R

@@ -15,7 +15,7 @@
 #' imputation method. Suggested values from the error localization method may introduce an undesired bias.
 #'
 #' @param data data to be checked
-#' @param x [validator()] or `errorlocation` object.
+#' @param x [validate::validator()] or `errorlocation` object.
 #' If an `errorlocation` is already available (through [locate_errors()]) this
 #' is more efficient.
 #' @param ref optional reference data set

R/soft-rule.R

@@ -1,10 +1,14 @@
 #TODO rename to mip_*
 
 # convert statements of A == '1' into A + .delta_A == '1'
+# convert statements of a:a - b:b <= 0 into a:a - b:b - delta <= 0
 soft_cat_rule <- function(x, prefix=".delta_", name = x$rule,  ...){
   stopifnot(inherits(x, "mip_rule"))
   nm <- paste0(prefix, name, collapse = "")
   delta <- setNames(1L, nm)
+  neg <- min(sum(x$a[x$a < 0]), -1L)
+  delta <- ifelse(x$op == "==", 1L, neg)
+  delta <- setNames(delta, nm)
   x$a <- c(x$a, delta)
   x$type <- c(x$type, setNames("binary", nm))
   x