CGr {ECG}R Documentation

Creates a CGr object

Description

Builds a CGr (center of gravity) object for a series of replicated observations.

Usage

CGr(data, from=min(data$x), to=max(data$x), columns, 	responseFraction = 0.50, 
useConstantDelta=FALSE, fixedResponseFraction=0.5, useFixedResponseFraction=FALSE, 
replaceOutliers=TRUE, responseLowerLimit=min(data[, columns]), 
responseUpperLimit=max(data[, columns]), alpha=0.05, 
kp=if(length(columns)<=1) qnorm(1-alpha/2) else 
	qt(1-alpha/2, length(columns)-1), 
signifDigits=2, useRobustStatistics = TRUE, ...)

Arguments

data

a data frame structure containing (date, x, y1, ..., yn) columns, it may contain some other columns.

from

a numeric value with the initial value of x to search for a local minimum.

to

a numeric value with the final value of x to search for a local minimum.

columns

a vector of indexes of the columns to be considered in the profile.

responseFraction

numeric, fraction of the maximum height to be considered in the analysis,

useConstantDelta

boolean flag, if true constant increment in the x values is assumed, otherwise the difference is computed for each increment of x.

fixedResponseFraction

a numeric with the fraction of hieght to be used as a reference to normilize.

useFixedResponseFraction

a logical value, if true then it uses the value of f.fixed to normalize all the computations, otherwise it uses the values of extrapolation sequence of fractions to normalize.

replaceOutliers

a logic value, if true then it uses the value of responseLowerLimit and responseUpperLimit to replace outlier values. Default value is TRUE.

responseLowerLimit

a real value to be used as the default to replace outlier values lower than expected, its default value is 0.

responseUpperLimit

a real value to be used as the default to replace outlier values larger than expected, its default value is 1.

alpha

a real value, define the level of significance for building confidence interval.

kp

a real value, it defines the coverage factor to be used to estimate the expanded uncertainty. It is build based on the level of significance alpha and assumes a T distribution of the error terms with the degrees of freedom equals to the number of columns provided minus one, its default value is qnorm(1-alpha/2) for one column otherwise qt(1-alpha/2, length(columns)-1).

signifDigits

number of significant digits used to display the result.

useRobustStatistics

a logical value, if true then median and mad are used to estimate location and dispersion otherwise the mean and standard deviation are used.

...

additional parameters.

Value

x

numeric, the estimated value

u

numeric, the estimated uncertainty associated to x

moments

numeric vector, the estimated mean, variance, skweness and kurtosis

input

list, contains the current input parameters, including the default values additional parameters passed through ... are not included.

frame

list, contains the reference values of the analysis. This information is used to build a verbosed version of its plot. The content of the list is: y.x.band.min the local maximum found in the lower region of the analysis region.
y.x.band.max the local maximum found it the upper region of the analysis region.
x.min.y the value of x where the local minumum y occurrs.
x.max the value of x where the local maximum y.x.max occurs.
x.min the value of x where the locel maximum y.x.min occurs.
y.x.max the maximum height in the upper region of the analysis.
y.x.min the maximum height in the lower region of the analysis.
h the value of the index of x associated with f.i fraction of the data in the lower region of analysis.
k the value of the index of x associated with f.i fraction of the data in the upper region of analysis.
x.h the value of x associated with f.i fraction of the data in the lower region of analysis.
x.k the value of x associated with f.i fraction of the data in the upper region of analysis.

used.data.points the number of datapoints of x used to obtain the estimates, this is equal to k-h+1.

Author(s)

H. Gasca-Aragon

Examples

require(ECG)

N<- 1000
set.seed(12345)
d1<- 1-sin(seq(1:(5/2*N))/N*pi-pi*3/4)+rnorm(5/2*N, 0, 0.01)
d2<- 1-sin(seq(1:(5/2*N))/N*pi-pi*3/4)+rnorm(5/2*N, 0, 0.01)
dat<- data.frame(x=1:length(d1), 
	y1=100*(d1-min(d1))/(max(d1)-min(d1)),
	y2=100*(d2-min(d2))/(max(d2)-min(d2))
)

CGres<- CGr(dat, columns=c(2,3))
CGres

[Package ECG version 0.5.2 Index]