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MaelaLupocran-robot
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‎DESCRIPTION‎

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Package: AQuality
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Type: Package
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Title: Water and Measurements Quality
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Version: 1.3
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Date: 2025-03-29
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Version: 1.4
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Date: 2025-04-13
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Authors@R: c(person("Maela", "Lupo", role = c("aut","cre"),
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email = "maela.lupo@gmail.com"),
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person("Andrea", "Porpatto", role = "aut"),
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Rosa Marzullo [aut],
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Alfredo Rigalli [aut]
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Maintainer: Maela Lupo <maela.lupo@gmail.com>
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Description: The functions proposed in this package allows to evaluate the process of measurement of the chemical components of water numerically or graphically. TSSS(), ICHS and datacheck() functions are useful to control the quality of measurements of chemical components of a sample of water. If one or more measurements include an error, the generated graph will indicate it with a position of the point that represents the sample outside the confidence interval. The function CI allows to evaluate the possibility of contamination of a water sample after being obtained.
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Description: The functions proposed in this package allows to evaluate the process of measurement of the chemical components of water numerically or graphically. TSSS(), ICHS and datacheck() functions are useful to control the quality of measurements of chemical components of a sample of water. If one or more measurements include an error, the generated graph will indicate it with a position of the point that represents the sample outside the confidence interval. The function CI() allows to evaluate the possibility of contamination of a water sample after being obtained. Validation() is a function that allows to calculate the quality parameters of a technique for the measurement of a chemical component.
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License: GPL-2
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NeedsCompilation: no
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Packaged: 2025-03-30 09:41:34 UTC; casa
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Packaged: 2025-04-13 11:48:24 UTC; tresalamos
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Depends: R (>= 3.5.0)
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Repository: CRAN
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Date/Publication: 2025-03-30 10:50:02 UTC
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Date/Publication: 2025-04-13 12:30:01 UTC

‎MD5‎

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bec88062c2cd18ac9823e16d5109aac1 *DESCRIPTION
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91480228c9aa40edd45f5574f6028710 *NAMESPACE
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2ea9fef9febcdb992140f0029567c597 *DESCRIPTION
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106a026c8f3fbf2e05dc7efadd535996 *NAMESPACE
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57df71854f9d20e5bb81f569a2caff7a *R/CI.R
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103809aeed282972635851f5d94ee7bb *R/ICHS.R
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b361026a09a12321b21905a1f51ba9d4 *R/TSSS.R
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193f55a5e650ad49ac4122b2f4a1991a *R/datacheck.R
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0d3b90c89de973dd5ec9330ff94603b8 *build/partial.rdb
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479a8916ab93c34c2b463d6b7c220752 *R/validation.R
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2196502b5c4feb9990ef4ae3f6b02c9a *build/partial.rdb
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479c3a95e2a30ebc729c2dde9a2d121f *data/dataCI.rda
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3252ea586bda89faf3613e3a441be801 *data/dataICHS.rda
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af9db732e4e639fc6b77050344522716 *data/dataTSSS.rda
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5a33512290287cf3b024e6d31410bbae *man/AQuality-package.Rd
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86a962d93671145a7d7ab79d2ddf47e5 *data/datavalidation.rda
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ae254744b408d4bb883090352caeb8d7 *man/AQuality-package.Rd
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6d1d95bb7965d86260052a57ee5c0bc5 *man/CI.Rd
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61085bb171843e2721d8955a25a1891a *man/ICHS.Rd
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26dcb6adb22e16e5e0c421bacacfd0ef *man/TSSS.Rd
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ffa289e5dccf531512e103da990db0bc *man/dataCI.Rd
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cf60e866e7dca298ec30051bb20d227a *man/dataICHS.Rd
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dbdcdc1f871db6481122c7488ae6ecc9 *man/dataTSSS.Rd
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36b2c82eda02db128a3fd2dca23242c1 *man/datacheck.Rd
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d7a0fbb318b5ec496fc508877b075301 *man/datavalidation.Rd
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6600d5937c2b8288595a11f9b599ae6c *man/validation.Rd

‎NAMESPACE‎

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export("CI", "ICHS", "TSSS","datacheck")
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export("CI", "datacheck", "ICHS", "TSSS", "validation")
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import(stats,graphics)

‎R/validation.R‎

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validation <-
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function(datavalidation,numest=NULL,measurementunit=NULL,techniquename=NULL,date=Sys.Date(),graph=FALSE){
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if(numest=='NULL'){
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stop('argument numest was avoided')
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}
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#linear regression with data from day=1
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curveday1<-lm(abs~concentration,data=datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),])
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rcurveday1<-cor.test(datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),]$abs,datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),]$concentration)
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Rsquare<- rcurveday1$estimate
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day1<-datavalidation[datavalidation$day==1,]
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meanabs<-mean(day1$abs)
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meanconcentration<-mean(day1$concentration)
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sdabs<-sd(day1$abs)
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sdconcentration<-sd(day1$concentration)
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sumabs<-c()
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for(i in 1:nrow(day1)){
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sumabs<-c(sumabs,(day1[i,3]-meanabs)^2)
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}
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sumabs<-sum(sumabs)
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sumconcentration<-c()
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for(i in 1:nrow(day1)){
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sumconcentration<-c(sumconcentration,(day1[i,2]-meanconcentration)^2)
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}
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sumconcentration<-sum(sumconcentration)
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SE<-sqrt(sumabs/(nrow(day1)-2))/sqrt(sumconcentration)
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if(graph=='TRUE'){
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fcurveday1<-function(x){curveday1$coefficients[1]+curveday1$coefficients[2]*x}
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plot(0,0,type="n",xlim=c(min(datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),]$concentration),max(datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),]$concentration)),ylim=c(min(datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),]$abs),max(datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),]$abs)))
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plot(fcurveday1,min(datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),]$concentration),max(datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),]$concentration),lwd=2,col="blue")
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points(datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),]$concentration,datavalidation[datavalidation$day==1 & (datavalidation$tube=="s" | datavalidation$tube=="b"),]$abs,pch=20,col="red",cex=1.5)
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}
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#sensitivity calculation
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sensitivity=SE*100/curveday1$coefficients[2]
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# detection limit calculation
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LOD=mean(datavalidation[datavalidation$day==1 & datavalidation$tube=="b",]$abs)+3.3*sd(datavalidation[datavalidation$day==1 & datavalidation$tube=="b",]$abs)
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LOD= (LOD -curveday1$coefficients[1])/curveday1$coefficients[2]
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# cuantification limit calculation
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LOQ=mean(datavalidation[datavalidation$day==1 & datavalidation$tube=="b",]$abs)+10*sd(datavalidation[datavalidation$day==1 & datavalidation$tube=="b",]$abs)
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LOQ= (LOQ -curveday1$coefficients[1])/curveday1$coefficients[2]
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# veracidad calculation
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V= abs(((mean(datavalidation[datavalidation$day==1 & datavalidation$tube=="qc",]$abs)-curveday1$coefficients[1])/curveday1$coefficients[2])-mean(datavalidation[datavalidation$day==1 & datavalidation$tube=="qc",]$concentration))*100/mean(datavalidation[datavalidation$day==1 & datavalidation$tube=="qc",]$concentration)
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# repetitivity calculation (CVr%)
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CVr= sd(datavalidation[datavalidation$day==1 & datavalidation$tube=="qc",]$abs)*100/mean(datavalidation[datavalidation$day==1 & datavalidation$tube=="qc",]$abs)
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#intermediate precision calculation (CVR%)
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CVR= sd(c(datavalidation[datavalidation$day==1 & datavalidation$tube=="qc",]$abs,datavalidation[datavalidation$day==2 & datavalidation$tube=="qc",]$abs,datavalidation[datavalidation$day==3 & datavalidation$tube=="qc",]$abs))*100/mean(c(datavalidation[datavalidation$day==1 & datavalidation$tube=="qc",]$abs,datavalidation[datavalidation$day==2 & datavalidation$tube=="qc",]$abs,datavalidation[datavalidation$day==3 & datavalidation$tube=="qc",]$abs))
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# summary
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writeLines(paste("Validation parameters technique: ", techniquename))
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writeLines(paste("slope: ", round(curveday1$coefficients[2],digits=2)))
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writeLines(paste("Linearity, R square = ", (trunc(Rsquare*1000))/1000))
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writeLines(paste("Sensitivity ", (trunc(sensitivity*100))/100, " %"))
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writeLines(paste("LOD ", (trunc(LOD*100))/100, measurementunit))
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writeLines(paste("LOQ ", (trunc(LOQ*100))/100, measurementunit))
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writeLines(paste("Accuracy ", 100-(trunc(V*100))/100, "%"))
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writeLines(paste("CVr% :intraassay repetitivity ", (trunc(CVr*100))/100, "%"))
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writeLines(paste("CVR% :interassay repetitivity ", (trunc(CVR*100))/100, "%"))
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writeLines(paste("linear range: ", (trunc(LOQ*100))/100, "-", max(day1$concentration), measurementunit))
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writeLines(paste("Uncertainty: Urel(%): ", trunc(2*sqrt((CVR/sqrt(3))^2+(V/sqrt(3))^2)*100)/100, "%"))
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}

‎build/partial.rdb‎

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‎data/datavalidation.rda‎

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‎man/AQuality-package.Rd‎

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\packageTitle{AQuality}
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}
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\description{The package allows you tu evaluate graphically the quality of measurements of water components}
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\details{The package includes four functions: TSSS(), ICHS(), datacheck() and CI(). The TSSS()function allows evaluating the quality of a set of measurement of water components, which correlate with total soluble solids. On the other hand, the ICHS() function allows evaluating the quality of a set of measurement of water components, which correlate with conductivity. The function CI allows to evaluate the possibility of contamination of a water sample after being obtained. The function datacheck indicates de registers of a database that do not match simultaneously correlation of mass summation of chemical components with total soluble solids and correlation of charge summation of chemical components with conductivity}
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\details{The package includes five functions: TSSS(), ICHS(), datacheck(), validation() and CI(). The TSSS()function allows evaluating the quality of a set of measurement of water components, which correlate with total soluble solids. On the other hand, the ICHS() function allows evaluating the quality of a set of measurement of water components, which correlate with conductivity. The function CI() allows to evaluate the possibility of contamination of a water sample after being obtained. The function datacheck indicates de registers of a database that do not match simultaneously correlation of mass summation of chemical components with total soluble solids and correlation of charge summation of chemical components with conductivity. The function validation() allows to calculate the quality parameters of a technique for the measurement of a chemical component.}
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\author{
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\packageAuthor{AQuality}
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Maintainer: Maela Lupo email: maela.lupo@gmail.com}
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\keyword{package}
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\keyword{package}

‎man/datavalidation.Rd‎

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\name{datavalidation}
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\alias{datavalidation}
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\docType{data}
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\title{
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Data Sets~~
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}
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\description{
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Data.frame with data for calculating the quality parameters of a technique for the measurement of a chemical component of water with the validation() function. The data.frame includes concentration and absorbance measurement of standards of the calibration curve and a solution of known concentration, called quality control (qc). The data.frame includes measurements of the absorbance of the tube at three different days.
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Column 1: name of the tube. The tubes b, s1, s2,s3 and s4 represent different concentrations of a calibration curve.
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Column 2: concentration: the concentration of each tube, expressed in micrograms.
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Column 3: abs: absorbance of each tube which were measured spectrophotometrically.
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Column 4: day: the day when the measurement was done.
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}
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\usage{data("datavalidation")}
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\format{
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A data frame with 46 measurement of the following variables.
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\describe{
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\item{\code{tube}}{a character vector}
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\item{\code{concentration}}{a numeric vector}
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\item{\code{abs}}{a numeric vector}
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\item{\code{day}}{a numeric vector}
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}
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}
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\examples{
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# Including data.frame: datavalidation in workspace.
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data("datavalidation")
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# Column names of data.frame: datavalidation
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names(datavalidation)
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# Data set type of columns of data.frame: datavalidation.
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str(datavalidation)
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# Calculation of quality parameters of the Total Kjeldhal Nitrogen (tkn) measurement technique
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validation(datavalidation,numest=4,measurementunit='ug',techniquename='tkn',graph=TRUE)
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}
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\keyword{datasets}

‎man/validation.Rd‎

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\name{validation}
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\alias{validation}
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\title{
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Quality parameters of a measurement technique
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}
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\description{
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Calculate the quality parameters of a technique for the measurement of a chemical component.
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}
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\usage{
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validation(datavalidation, numest = NULL, measurementunit = NULL,
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techniquename = NULL, date = Sys.Date(), graph = FALSE)
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}
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\arguments{
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\item{datavalidation}{
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Data.frame with data for points of a calibration curve of five points, including a reagent blank and four standards. The data.frame also includes a solution of known concentration called quality control (qc). The columns include name of the tube, concentration, absorbance and day of measurement.
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}
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\item{numest}{
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The number of standards solution used for the calibration curve. This number do not include the reagen t blank.
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}
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\item{measurementunit}{
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The unit of measurement of the concentration.
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}
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\item{techniquename}{
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The name of the technique whose quality parameters are calculated.
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}
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\item{date}{
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The date when the procedure is performed.
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}
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\item{graph}{
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Graph argument allows to plot the calibration curve.
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}
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}
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\details{
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The validation() function calculates a set of quality parameters of a technique for the measurement of a chemical component. The function needs the values of a property (absorbance in the example) of standard solutions with different known concentration of one chemical component of water, measured in three different days. The function also needs the values of the property of a solution with a known concentration of the component, different from the standards of the calibration curve, which is called quality control solution (qc).
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}
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\value{
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The datavalidation() function returns a graph of the calibration curve if the argument graph takes the value TRUE. The function calculates and returns a list with the values of the slope of the calibration curve, the detection limit (LOD), the quantification limit (LOQ), correlation coefficient, sensitivity, accuracy, intraassay repetitivity, interassayrepetitivity, linear range and uncertainty.
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}
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\author{
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Maela Lupo, Alfredo Rigalli
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}
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% \keyword{methods}
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% \keyword{misc}

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