inflection-package: Finds the Inflection Point of a Curve

Description

Implementation of methods Extremum Surface Estimator (ESE), Extremum Distance Estimator (EDE) and their iterative versions BESE and BEDE in order to identify the inflection point of a curve.

Arguments

Details

The x,y data should be numeric vectors of length at least 4 without missing values. Main functions for a convex/concave curve are: ese(x,y,0) for ESE method, see ese & iterative version bese for BESE ede(x,y,0) for EDE method, see ede & iterative version bede for BEDE findiplist(x,y,0) for both ESE and EDE methods, see findiplist

From version 1.3.5 it is also available the implementation of Unit Invariant Knee (UIK) method for the determination of elbow or knee point of a curve. That is extremely useful in a wide range of analyses every time we want to find the optimal number of 'components' from a scree plot (PCA: components, Archetypal Analysis: archetypes, Factor A.: factors, Cluster A..: clusters and so on). In most of the cases uasage is uik(x,y), with x the vector of components, archetypes, clusters, factors or whatever and y the relevant vector Sum of Squared Errors (SSE), see uik and d2uik.

The methods are defined at [1] & [2] while a detailed reproduction of all Tables for [1] exist in a vignette. All relevant Tables exist as data, for example to load Table 1 of [1] we just use data("table_01").

References

[1]Demetris T. Christopoulos (2014). Developing methods for identifying the inflection point of a convex/concave curve. arXiv:1206.5478v2 [math.NA]. https://arxiv.org/pdf/1206.5478v2.pdf

[2]Demetris T. Christopoulos (2016). On the efficient identification of an inflection point.International Journal of Mathematics and Scientific Computing, (ISSN: 2231-5330), vol. 6(1). https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf

[3]Demetris T. Christopoulos (2016). Introducing Unit Invariant Knee (UIK) As an Objective Choice for Elbow Point in Multivariate Data Analysis Techniques (March 1, 2016). https://www.ssrn.com/abstract=3043076

Examples

Run this code

# NOT RUN {
#
## Lets create some convex/concave data based on the Fisher-Pry model
##by using 1001 not equal spaced abscissas with data right asymmetry
N=20001;
## Case I: not noisy data
#
set.seed(20190628);
x=sort(runif(N,0,15));y=5+5*tanh(x-5);
#
# t1=Sys.time();
# tese=ese(x,y,0,doparallel = TRUE);#...simple run of ESE
# t2=Sys.time();print(as.POSIXlt(t2, "GMT")-as.POSIXlt(t1, "GMT"),quote=F);
## Time difference of 6.023056 secs
# tese;
##       j1   j2      chi
## ESE 4573 8272 4.834904
tede=ede(x,y,0);tede;#...simple run of EDE
##       j1   j2      chi
## EDE 4418 8867 5.000198
edeci(x,y,0);#...Run EDE and compute 95% Chebyshev c.i.
##       j1   j2      chi k  chi-5*s chi+5*s
## EDE 4418 8867 5.000198 5 4.994605 5.00579
#
# t1=Sys.time();
# eseit=bese(x,y,0,doparallel = TRUE);#...Bisection ESE (BESE)
# t2=Sys.time();
# print(as.POSIXlt(t2, "GMT")-as.POSIXlt(t1, "GMT"),quote=F);
## Time difference of 6.262982 secs
#eseit$iplast #...last estimation for inflection point
## [1] 5.000773
#eseit$iters #...all iterations done...
#        n            a         b      ESE
# 1  20001 0.0001931784 14.999900 4.835303
# 2   3668 4.4606627093  5.647031 5.053847
# 3   1567 4.6878642635  5.262619 4.975242
# 4    737 4.8696049280  5.154673 5.012139
# 5    376 4.9229470803  5.064312 4.993629
# 6    181 4.9684872106  5.038649 5.003568
# 7     82 4.9806225684  5.015416 4.998019
# 8     35 4.9924177257  5.009629 5.001023
# 9     20 4.9960624950  5.002740 4.999401
# 10    11 4.9980399851  5.001968 5.000004
#
t1=Sys.time();
edeit=bede(x,y,0);#...Bisection EDE (BEDE)
t2=Sys.time();
print(as.POSIXlt(t2, "GMT")-as.POSIXlt(t1, "GMT"),quote=F);
# Time difference of 0.073102 secs
edeit$iplast #...last estimation for inflection point
## [1]  4.999941
edeit$iters #...all iterations done
##        n            a         b      EDE
## 1  20001 0.0004635123 14.999801 5.000198
## 2   4450 4.1996716394  5.799961 4.999816
## 3   2129 4.5614927786  5.438346 4.999920
## 4   1182 4.7512642248  5.249931 5.000597
## 5    656 4.8580908542  5.143351 5.000721
## 6    365 4.9175169028  5.082218 4.999868
## 7    229 4.9524894403  5.047898 5.000194
## 8    135 4.9723928177  5.027658 5.000025
## 9     77 4.9835896748  5.016056 4.999823
## 10    46 4.9904827878  5.009340 4.999912
## 11    28 4.9945713510  5.005444 5.000008
## 12    15 4.9968973151  5.003045 4.999971
## 13    11 4.9978968780  5.001986 4.999941
# t1=Sys.time();
# A=findiplist(x,y,0,doparallel=TRUE);#...Run both ESE & EDE at once...
# t2=Sys.time();print(as.POSIXlt(t2, "GMT")-as.POSIXlt(t1, "GMT"),quote=F);
## Time difference of 5.143354 secs
#A
##       j1   j2      chi
## ESE 4573 8272 4.834904
## EDE 4418 8867 5.000198
## Let's plot some interesting approximately results.
# plot(x,y,type="l",xaxt="n",lwd=2);axis(1,at=seq(0,x[N]));
# lines(c(x[1],x[A[1,2]]),c(y[1],y[A[1,2]]),col="green",lty=2);
# lines(c(x[N],x[A[1,1]]),c(y[N],y[A[1,1]]),col="blue",lty=2);
# lines(c(x[1],x[N]),c(y[1],y[N]),col="black",lty=2);
# abline(v=A[,3],col=c('blue','red'),lty=2);
# points(x[A[1,1:2]],y[A[1,1:2]], type = "p",pch = 19,col="blue",font=2);
# points(x[A[2,1:2]],y[A[2,1:2]], type = "p",pch = 19,col="red",font=2);
# text(A[1,3]-0.5,0,expression(chi[S]),font=2,col='blue');
# text(A[2,3]+0.5,0,expression(chi[D]),font=2,col='red');
# grid();
#
### Case II: noisy data
#
set.seed(20190628);
x=sort(runif(N,0,15));
r=0.1;y=5+5*tanh(x-5)+rnorm(N,0,0.05);
#
# t1=Sys.time();
# tese=ese(x,y,0,doparallel = TRUE);#...simple run of ESE
# t2=Sys.time();
# print(as.POSIXlt(t2, "GMT")-as.POSIXlt(t1, "GMT"),quote=F);
## Time difference of 4.878437 secs
#tese
##       j1   j2      chi
## ESE 4684 8412 4.936269
tede=ede(x,y,0);tede;#...simple run of EDE
#       j1   j2      chi
# EDE 4190 8856 4.909071
edeci(x,y,0);#...Run EDE and compute 95% Chebyshev c.i.
##       j1   j2      chi k  chi-5*s  chi+5*s
## EDE 4190 8856 4.909071 5 4.659396 5.158746
# t1=Sys.time();
# eseit=bese(x,y,0,doparallel = TRUE);#...Bisection ESE (BESE)
# t2=Sys.time();print(as.POSIXlt(t2, "GMT")-as.POSIXlt(t1, "GMT"),quote=F);
## Time difference of 6.288019 secs
#eseit$iplast#...last estimation for inflection point
## [1] 4.94072
#eseit$iters#...all iterations done...
##       n            a         b      ESE
## 1 20001 0.0004635123 14.999801 4.936269
## 2  3729 4.4285049755  5.538487 4.983496
## 3  1475 4.7297708667  5.413794 5.071782
## 4   932 4.7838335764  5.097607 4.940720
#
t1=Sys.time();
edeit=bede(x,y,0);#...Bisection EDE (BEDE)
t2=Sys.time();
print(as.POSIXlt(t2, "GMT")-as.POSIXlt(t1, "GMT"),quote=F);
## Time difference of 0.01700807 secs
edeit$iplast#...last estimation for inflection point
## [1] 5.071782
edeit$iters#...all iterations done
##       n            a         b      EDE
## 1 20001 0.0004635123 14.999801 4.909071
## 2  4667 4.2343777127  5.704010 4.969194
## 3  1938 4.7297708667  5.413794 5.071782
#
# t1=Sys.time();
# A=findiplist(x,y,0,doparallel=TRUE);#...Run both ESE & EDE at once...
# t2=Sys.time();
# print(as.POSIXlt(t2, "GMT")-as.POSIXlt(t1, "GMT"),quote=F);
## Time difference of 5.037386 secs
#A
##       j1   j2      chi
## ESE 4684 8412 4.936269
## EDE 4190 8856 4.909071
#
## Let's plot some interesting approximately results.
# plot(x,y,type="l",xaxt="n",lwd=2);axis(1,at=seq(0,x[N]));
# lines(c(x[1],x[A[1,2]]),c(y[1],y[A[1,2]]),col="green",lty=2);
# lines(c(x[N],x[A[1,1]]),c(y[N],y[A[1,1]]),col="blue",lty=2);
# lines(c(x[1],x[N]),c(y[1],y[N]),col="black",lty=2);
# abline(v=A[,3],col=c('blue','red'),lty=2);
# points(x[A[1,1:2]],y[A[1,1:2]], type = "p",pch = 19,col="blue",font=2);
# points(x[A[2,1:2]],y[A[2,1:2]], type = "p",pch = 19,col="red",font=2);
# text(A[1,3]-0.5,0,expression(chi[S]),font=2,col='blue');
# text(A[2,3]+0.5,0,expression(chi[D]),font=2,col='red');
# grid();
## Close device
#dev.off()
##
## Load data used for Tables of [1]
data("table_01")
dh=table_01
plot(dh,pch=19,cex=0.1)
A=findiplist(dh$x,dh$y,0) 
A
abline(v=A[1,3],col='blue')
abline(v=A[2,3],col='red')
##
# }

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Description

Arguments

Details

References

See Also

Examples