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**
POLS 6386 MEASUREMENT THEORY
Fifth Assignment
Due 25 February 2003**

- The aim of this problem is to show you how to double-center a
matrix in
**R**and extract the eigenvalues and eigenvectors of the matrix.

Download the**# The "#" is a comment statement -- # double_center.r -- Double-Center Program that is, it tells the R compiler # that what follows on this line are # The Data Are assumed to be Squared Distances -- Data Must comments, not commands! # Be Transformed to Squared Distances Below # T <- matrix(scan("D:/R_Files/colors.txt",0),ncol=14,byrow=TRUE) This reads in the Color Matrix Data -- Note that you have to tell it how many columns! colornames <- read.csv("D:/R_Files/color_coords.txt",header=T,row.names=1) Same as Homework 4 attach(colornames) Same as Homework 4 TT <- T Copy matrix T into matrix TT nrow <- length(T[,1]) Counts number of rows ncol <- length(T[1,]) Counts number of columns xrow <- NULL Initializes Vector xcol <- NULL Initializes Vector matrixmean <- 0 Initialize to zero # # Transform the Matrix This transforms the similarities data from a range of # 0 - 100 to a range of 0 - 4 in squared distances i <- 0 while (i < nrow) { Note that this loops nrow times because it starts at 0! i <- i + 1 Increment the counter or it will loop forever! j <- 0 Initialize column index to 0 while (j < ncol) { Note that this loops ncol times because it starts at 0! j <- j + 1 Increment the counter or it will loop forever! TT[i,j] <- ((100 - T[i,j])/50)**2 Here is the Transformation } } T <- TT Copy matrix TT into matrix T # # Set Row and Column Vectors to zero i <- 0 These two loops simply initialize the vectors to zero while (i < nrow) { i <- i + 1 xrow[i] <- 0.0 } i <- 0 while (i < ncol) { i <- i + 1 xcol[i] <- 0.0 } # Compute Row Means and Matrix Mean -- Set TT(,) to Zeroes i <- 0 Set counter to zero while (i < nrow) { Loop over Rows i <- i + 1 Increment the counter or it will loop forever j <- 0 Set column counter to zero while (j < ncol) { Loop over Columns to get Row Means j <- j + 1 Increment the counter or it will loop forever xrow[i] <- xrow[i]+T[i,j]/ncol Add up elements in the row (add across columns) matrixmean <- matrixmean + T[i,j]/(nrow*ncol) This gives us the matrix mean TT[i,j] <- 0 Initialize matrix TT to zero } } # Compute Column Means j <- 0 Set counter to zero while (j < ncol) { Loop over Columns j <- j + 1 Increment the counter or it will loop forever i <- 0 Set row counter to zero while (i < nrow) { Loop over Rows to get Column Means i <- i + 1 Increment the counter or it will loop forever xcol[j] <- xcol[j]+T[i,j]/nrow Add up elements in the column (add across rows) } } # Double-Center the Matrix This Double-Centers the Matrix i <- 0 Set counter to zero while (i < nrow) { Loop over Rows i <- i + 1 Increment the counter or it will loop forever j <- 0 Set column counter to zero while (j < ncol) { Loop over Columns j <- j + 1 Increment the counter or it will loop forever TT[i,j] <- (T[i,j]-xrow[i]-xcol[j]+matrixmean)/(-2) TT is the Double-Centered Matrix } } End of Double-Center Code ev <- eigen(TT) Compute the Eigenvalues and Eigenvectors DIMNEW.1 <- ev$vec[,1] Put the First Eigenvector in DIMNEW.1 DIMNEW.2 <- ev$vec[,2] Put the Second Eigenvector in DIMNEW.2 plot(DIMNEW.1,DIMNEW.2,type="n",main="The Color Circle From Double Centering", xlab="First Dimension",ylab="Second Dimension", xlim=c(-1.0,1.0),ylim=c(-1.0,1.0)) Plot Commands text(DIMNEW.1,DIMNEW.2,labels=row.names(colornames),adj=0)****R**program and two example files below and place them in the same directory on your disk. Note that you will have to use**Epsilon**to change the path statements in**double_center.r**.

R Program to Perform Double-Centering

Ekman Color Similarities Matrix

Color Coordinates With Row Names Used in Homework 4

Run**double_center.r**and you should see:

- Turn in the Graph.

- In
**R**type:

**eigenvalue <- abs(ev$val)**

**eigenvalue**

and you should see:

These are the**[1] 7.928536e+00 5.197332e+00 1.763696e+00 1.495726e+00 6.338461e-01 [6] 4.120299e-01 1.660689e-01 1.266656e-01 7.386280e-02 1.666023e-02 [11] 5.380350e-03 9.296853e-15 1.069314e-01 1.897294e-01**of the double-centered matrix (technically, the absolute values of the eigenvalues). Paste these into*eigenvalues***Excel**and make a plot like that shown in Home Work Number 3, problem 2.b. Turn in this plot with the appropriate title (e.g., "Eigenvalues of Double-Centered Color Data").

- We are going to test Shepard's Universal Law by assuming that the
color data are actually generated by an exponential loss function. To do this:

- Make a copy of
**double_center.r**--**double_center_2.r**.

- Bring
**double_center_2.r**up in**Epsilon**.

- Change the line "TT <- T" that comes right after the line
"attach(colornames)" to:
**TT <- ifelse(T <= 5,5,T) If T[i,j] £ 5 set T[i,j] = 5, else if T[i,j] > 5 leave as is (replace with itself)** - Right below this line insert the line:

**T <- TT**

These two commands replace any entries in "colors.txt" that are less than 5 with 5 because the natural log of zero is negative infinity!

- Replace the line "TT[i,j] <- ((100 - T[i,j])/50)**2" with:

**TT[i,j] <- ((-1.0)*log(T[i,j]/100))**2**

- Make a copy of

- Turn in the Graph.
- In this problem we are going to use
**KYST**to replicate the analysis done by Herbert F. Weisberg and Jerrold G. Rusk in their 1970 paper "Dimensions of Candidate Evaluation,"**American Political Science Review****,**64 (Dec. 1970): 1167-1185. Download the following data file:

1968 Election Data

The 1968 Election Data file contains some variables besides the thermometer scores that we will use in future homeworks. The variables are:**idno respondent id number partyid strength of party id -- 0 to 6 income raw income category incomeq income quintile -- 1 to 5 race 0 = white, 1 = black sex 0 = man, 1 = woman south 0 = north, 1 = south education 1=HS, 2=SC, 3=College age age in years uulbj lbj position urban unrest uuhhh humphrey pos urban unrest uunixon nixon position urban unrest uuwallace wallace pos urban unrest uuself self placement urban unrest vnmlbj lbj pos vietnam vnmhhh hhh pos vietnam vnmnixon nixon pos vietnam vnmwallace wallace pos vietnam vnmself self placement vietnam voted 1=voted, 5=did not vote votedfor who voted for -- 1 = humphrey, 2= nixon, 3=wallace wallace wallace therm humphrey humphrey thermometer nixon nixon thermometer mccarthy mccarthy thermometer reagan reagan thermometer rockefeller rockefeller thermometer lbj lbj thermometer romney romney thermometer kennedy robert kennedy thermometer muskie muskie thermometer agnew agnew thermometer lemay "bombs away with Curtis LeMay" thermometer**- Make a copy of
**OLS68B.DAT**--**therm68.txt**-- and use**Epsilon**to remove all the variables*except the 12 Thermometer Scores*. Turn in the**Epsilon**macro you used to create**therm68.txt**.

- Read the data into
**R**with the command (be sure you use the appropriate path statement!):

**T <- matrix(scan("D:/R_Files/therm68.txt",0),ncol=12,byrow=TRUE)**

The range of the 1968 Feeling Thermometers was 0 to 97 -- 98 and 99 were used as missing values. You need to tell**R**that 98 and 99 are missing. To do this, use the following command:

To compute the correlation matrix use the command:**TT <- ifelse(T==98 | T==99,NA,T) If T[i,j] = 98 or 99 set TT[i,j] = NA (missing data) else if set TT[i,j] = T[i,j]**

**R <- cor(TT,use="pairwise.complete.obs")**

The "pairwise.complete.obs" tells**R**to throw away missing data*pair-wise*,**not***list-wise*(that is, the whole row of data!). Now type:

**R**

and you should something like this:

You need this correlation matrix to duplicate what Weisberg and Rusk did with**[,1] [,2] [,3] ....etc [1,] 1.00000000 -0.3108400 0.01690949 ....etc .... etc etc etc ...****KYST**. Use**Epsilon**to copy the correlation matrix into your**KYST**control card file. Note that you will have to reassemble the matrix using**Epsilon**and note that because of the pecularities of**R**, the second column of the matrix hasYou will have to stick a zero at the end of each correlation in the second column to get everything to work. Your*one less digit than the other columns*!!!**KYST**control card file should look something like this:

Report the Stress Values for 1, 2, and 3 dimensions and use**TORSCA PRE-ITERATIONS=3 DIMMAX=3,DIMMIN=1 PRINT HISTORY,PRINT DISTANCES COORDINATES=ROTATE ITERATIONS=50 REGRESSION=DESCENDING DATA,LOWERHALFMATRIX,DIAGONAL=PRESENT,CUTOFF=-2.00 1968 FEELING THERMOMETER CORRELATION MATRIX 12 1 1 (14X,12F12.8) Wallace 1.00000000 -0.31084000 0.01690949 ... etc etc ...etc etc ... COMPUTE STOP****R**to graph the results in two dimensions.

- Produce a Shepard Diagram for the two dimensional solution (the
horizontal axis is the actual correlation data -- "DATA" in the
**KYST**output file -- and the vertical axis is the estimated distances -- the "DIST" column in the**KYST**output file).

- Make a plot of the first two eigenvectors of the correlation matrix
using
**R**(see problem 1 for how to do this -- be certain*everything*is properly labeled!).

- Make a plot of the eigenvalues of the correlation matrix (see problem
1).

- In
**R**create the following 12 by 12 matrices (the**t(ev$vec)**is the transposed 12 by 12 matrix of eigenvectors and the symbol "%*%" is the matrix multiplication command):

**hello <- diag(12)**

ZZ <- t(ev$vec) %*% ev$vec

Are these two matrices -- hello and ZZ -- the same?

- In
**R**create the following matrices:

**diag(hello) <- ev$val**

RWORK <- ev$vec %*% hello %*% t(ev$vec)

What is**RWORK**? Be Specific!

- Make a copy of
- In this problem we are going to treat the morse code dataset as an
problem. Download the*unfolding***KYST**control card file:

Morse Code Data in Unfolding Format

- Run
**KYST**and note that you will end up withpoints! Point number 1 is "A"*72*point number 37 is also "A"! (Ignore the tokens in*and***KYST**!! They will be off on this problem because there is no "0" -- zero!) Graph the 72 points*with the appropriate labels*and appropriate titles in**R**.

- Produce a Shepard Diagram for the two dimensional solution (the
horizontal axis is the actual correlation data -- "DATA" in the
**KYST**output file -- and the vertical axis is the estimated distances -- the "DIST" column in the**KYST**output file).

- Run