mdsOpt (version 0.7-6)

drawIsoquants: 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)

Value

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

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

Author

Marek Walesiak , Andrzej Dudek

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: 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: 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)