drawIsoquants: draw series of isoquants

In mdsOpt: Searching for Optimal MDS Procedure for Metric and Interval-Valued Data

draw series of isoquants

Description

function draw series of isoquants (a contour line drawn through the set of points at which the same quantity of output is produced while changing the quantities of two or more inputs)

Usage

drawIsoquants(x,y=NULL,number=6,steps=NULL)

Arguments

x	two dimensional point (center)
y	optional - second point, used for calculations of step size if steps is null
number	number of isoquants
steps	distance between following isoquants starting from x, if length of this arguments is lower than number argument last item is repeated

Value

This is a plotting function, thus does not return any value

Author(s)

Marek Walesiak [e-mail], Andrzej Dudek [e-mail]

Department of Econometrics and Computer Science, Wroclaw University of Economics and Business, Poland

References

Walesiak, M., (2016), Visualization of Linear Ordering Results for Metric Data with the Application of Multidimensional Scaling, Ekonometria, 2(52), 9-21. Available at: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.15611/ekt.2016.2.01")}.

Walesiak, M. (2017), The application of multidimensional scaling to measure and assess changes in the level of social cohesion of the Lower Silesia region in the period 2005-2015, Ekonometria, 3(57), 9-25. Available at: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.15611/ekt.2017.3.01")}.

Walesiak, M., Dudek, A. (2017), Selecting the Optimal Multidimensional Scaling Procedure for Metric Data with R Environment, STATISTICS IN TRANSITION new series, September, Vol. 18, No. 3, pp. 521-540.

Examples

#Example 1
library(mdsOpt)
library(smacof)
library(clusterSim)
data(data_lower_silesian)
z<-data.Normalization(data_lower_silesian, type="n1")
d<-dist.GDM(z, method="GDM1")
res <- smacofSym(delta=d,ndim=2,type="interval")
print("Objects configuration", quote=FALSE)
plot(res, plot.type="confplot")
r1<-res$conf[nrow(z),1]
r2<-res$conf[nrow(z),2]
r3<-res$conf[nrow(z)-1,1]
r4<-res$conf[nrow(z)-1,2]
arrows(r1,r2,r3,r4,length=0.1,col="black")
res_up<-as.matrix(dist(res$conf,method="euclidean"))
drawIsoquants(res$conf[nrow(z)-1,],steps=max(res_up)/6)
# or 
# drawIsoquants(res$conf[nrow(z)-1,],steps=c(0.3,0.2),number=8)

#Example 2
library(mdsOpt)
library(smacof)
library(clusterSim)
data(data_lower_silesian)
z<-data.Normalization(data_lower_silesian, type="n1")
d<-dist.GDM(z, method="GDM1")
res<-smacofSym(delta=d,ndim=2,type="interval")
res1<-res$conf
#write.table(res1,"conf_2d.csv",dec=",",sep=";",col.names=NA,row.names=TRUE)
alfa<- 1.05*pi
a<- cos(alfa)
b<- -sin(alfa)
c<- sin(alfa)
d<- cos(alfa)
D<-array(c(a,b,c,d), c(2,2))
#res1<-read.csv2("conf_2d.csv", header=TRUE, row.names=1)
res1<-as.matrix(res1)
res2<-res1
plot(res2, xlab="Dimension 1",ylab="Dimension 2",main="",asp=1)
points(res2[1:31,],pch=1,font=2)
text(res2[c(1:31),],pos=3,cex=0.7,row.names(z[c(1:31),]))
r1<-res2[nrow(z),1]
r2<-res2[nrow(z),2]
r3<-res2[nrow(z)-1,1]
r4<-res2[nrow(z)-1,2]
arrows(r1,r2,r3,r4,length=0.1,col="black")
res_up<-as.matrix(dist(res2,method="euclidean"))
drawIsoquants(res2[nrow(z)-1,],steps=max(res_up)/6)

mdsOpt documentation built on May 29, 2024, 10:20 a.m.