ICHS {AQuality}

Ionic Charge Summation

Description

Plots ionic charge summation as a function of conductivity.

Usage

ICHS(sample, data, conflevel = 0.95, pchdata = 19, coldata = "green", cexdata = 0.5,
 pchsample = 19, colsample = "red", cexsample = 3, xaxis = "CONDUCTIVITY", 
yaxis = "IONIC CHARGE SUMMATION", title = paste("Sample ", as.character(sample)),
 linetyprediction = 2, linewidthprediction = 1, linecolorprediction = 5)

Arguments

sample

Code of the sample whose quality you want to know.

data

Data.frame containing code of the database samples, conductivity, measurements of ionic water components.

conflevel

Significance level used in the predict function.

pchdata

Symbol used to graph all the data in the data.frame.

coldata

Color of the symbols of all the data in the data.frame.

cexdata

Symbol size of all data in the data frame.

pchsample

Symbol chosen to represent the point whose measurement quality is to be represented.

colsample

Color chosen to represent the point whose measurement quality is to be represented.

cexsample

Size of the symbol chosen to represent the point whose measurement quality is to be represented.

xaxis

X axis label.

yaxis

Y axis label.

title

Title of the graph including the code of the chosen sample.

linetyprediction

Linear model prediction line type.

linewidthprediction

Linear model prediction line thickness.

linecolorprediction

Linear model prediction line color.

Details

The ICHS() function performs a linear model using column 2 (conductivity) as the independent variable and the other components of water as dependent variables (columns 3 onwards). Based on the linear model, a data prediction interval is obtained with a certain confidence level (conflevel). Then, ICHS() graphs the values of the entire database and finally graphs as a point with different color, the sample whose measurement quality you want to observe.

Value

The ICHS() function returns a graph of the sum of ionic chemical components as a function of the measurement of conductivity for each sample. It contains the confidence interval indicated in a dotted line, and the sample under observation. If the point that represents the sample is within the region delimited by the lines of the confidence interval, it is presumed that there were no serious measurement errors of the components analyzed.

Author(s)

Maela Lupo, Andrea Porpatto, Alfredo Rigalli