WNOMJLEWIS -- W-NOMINATE
Program 
NOMSTART_JLEWIS.DAT -- W-NOMINATE
Bootstrap Control Card File for 104th Senate
SEN104KH.ORD -- 104th Senate
roll call dataNOMSTART_JLEWIS.DAT looks like this:
SEN104KH.ORD
NOMINAL MULTIDIMENSIONAL UNFOLDING OF 104TH SENATE
919 1 22
2 36 11 Number Dimensions, Number Characters to Read from Header, Number of Bootstrap Trials
15.0000 0.5000
0.0250 20
(36A1,15000I1)
(1x,I4,36A1,1X,4i4,51f7.3)
(I4,1X,36A1,80F10.4)
It is identical to the NOMSTART.DAT used for question 5.e-g of
Homework 6 except for the "11" (colored red) in the fourth line of the file. This is the number of
bootstrap trials. Normally it is set to 1001 but we will use 101 in this problem.Put the files in the same directory and run WNOMJLEWIS. You will get 7 output files -- fort.26, fort.56, NOM21.DAT, NOM23.DAT, NOM31.DAT, NOM33.DAT, and NOM36.DAT. The NOM21.DAT - NOM36.DAT files are explained on the W-NOMINATE Page.
FORT.26 are the parametric bootsrapped legislator coordinates. The file will look something like this:
1 1049990999 0USA 10000CLINTON -0.9160 -0.3336 -0.8668 -0.2853 0.1039 0.2095 1.0000 0.1436 0.1436 1.0000
2 1041470541 0ALABAMA 10000HEFLIN -0.3923 -0.2917 -0.3546 -0.4203 0.0510 0.1876 1.0000 0.7794 0.7794 1.0000
3 1049465941 0ALABAMA 20000SHELBY 0.5858 -0.3131 0.6871 -0.3176 0.1162 0.0603 1.0000 0.2238 0.2238 1.0000
4 1041490781 0ALASKA 20000MURKOWSKI 0.6600 -0.1009 0.7623 -0.0338 0.1138 0.1090 1.0000 0.0790 0.0790 1.0000
5 1041210981 0ALASKA 20000STEVENS 0.4199 -0.3162 0.5695 -0.3110 0.1645 0.1222 1.0000 0.1487 0.1487 1.0000
etc etc etc
98 1044930873 0WASHING 10000MURRAY -0.9235 -0.1667 -0.8936 -0.1832 0.0555 0.0916 1.0000 -0.1436 -0.1436 1.0000
99 104 136656 0WEST VI 10000BYRD, ROBER -0.6504 -0.5292 -0.6249 -0.6165 0.0465 0.1304 1.0000 0.1703 0.1703 1.0000
100 1041492256 0WEST VI 10000ROCKEFELLER -0.8080 -0.0714 -0.7819 -0.0967 0.0416 0.1585 1.0000 0.6202 0.6202 1.0000
101 1044930925 0WISCONS 10000FEINGOLD -0.8300 0.5577 -0.7844 0.6024 0.0546 0.0564 1.0000 0.6076 0.6076 1.0000
102 1041570325 0WISCONS 10000KOHL -0.6772 0.5656 -0.6449 0.7452 0.0395 0.1934 1.0000 0.5894 0.5894 1.0000
103 1041471068 0WYOMING 20000SIMPSON 0.3605 -0.4309 0.5124 -0.4402 0.1725 0.1322 1.0000 0.4797 0.4797 1.0000
104 1041563368 0WYOMING 20000THOMAS 0.7165 0.3480 0.7850 0.3535 0.0824 0.1281 1.0000 0.5629 0.5629 1.0000
The legislator coordinates are are the first two columns after the name of the legislator (shown
in red). For example, former President Clinton's coordinates are -0.9160 and -0.3336.- Run 101 bootstrap trials using WNOMJLEWIS (change
011 to 101 in the
NOMSTART_JLEWIS.DAT file and run the program). E-Mail me the NOM21.DAT
file from the run.
- Use R to plot the legislators in two dimensions
from the FORT.26 file. Use
"D" for Non-Southern Democrats, "S" for Southern Democrats, "R" for Republicans, and
"P" for President Clinton. This graph should be in the same format as the one
you did for question 2.f of Homework 5.
- The sixth and seventh columns of numbers (shown in blue) are the bootstrapped standard
errors for the first and second dimensions, respectively. The last four columns are the Pearson
correlations between the bootstrapped first and second dimension coordinates computed across the
bootstrap trials. Because we are only working with two dimensions just the correlation between
the first and second dimension estimates is relevant (shown in Purple).
- We are going to make a graph like the ones shown in Figures 4.4 and 4.5 of my book. Download the
following R program:
Plot_Bootstrap_New.r --
R Program to Plot Parametric Bootstrap Output, FORT.26
Here is what Plot_Bootstrap_New.r looks like:# # Plot_Bootstrap_New.r -- Program reads parametric bootstrap files posted # at http://voteview.org/Lewis_and_Poole.htm # and plots the legislator ideal points and the # standard errors # # Remove all objects just to be safe # rm(list=ls(all=TRUE)) # library(MASS) library(stats) library(ellipse) You Will Need to Download and Install this Library # # Set up to read Parametric Bootstrap File # rc.file <- "c:/ucsd_homework_7/fort.26" # # The variable fields and their widths # rc.fields <- c("counter","cong","id","state","dist","lstate","party", "eh1","eh2","name","wnom1","wnom2","wnom1bs","wnom2bs", "se1","se2","r11","r12","r21","r22") # # Note -- For some files the field widths will be (e.g., H108_BS_1000_2.DAT): # (5,3,5,2,2,7,4,1,1,11,8,7,7,7,7,7,7) # rc.fieldWidths <- c(4,4,5,2,2,7,4,1,1,11,10,10,10,10,10,10,10,10,10,10) # # Read the vote data from fwf (FIXED WIDTH FORMAT -- FWF) # TT <- read.fwf(file=rc.file,widths=rc.fieldWidths,as.is=TRUE,col.names=rc.fields) party <- TT[,7] state <- TT[,4] wnom1 <- TT[,11] wnom2 <- TT[,12] std1 <- TT[,15] std2 <- TT[,16] corr12 <- TT[,18] # nrow <- length(TT[,1]) ncol <- length(TT[1,]) # plot(TT[,11],TT[,12],type="n",asp=1, main="", xlab="", ylab="", xlim=c(-1.0,1.0),ylim=c(-1.0,1.0),font=2) points(wnom1[party == 100 & state >= 40 & state <= 51],wnom2[party == 100 & state >= 40 & state <= 51],pch='S',col="red") points(wnom1[party == 100 & state == 53],wnom2[party == 100 & state == 53],pch='S',col="red") points(wnom1[party == 100 & state == 54],wnom2[party == 100 & state == 54],pch='S',col="red") points(wnom1[party == 100 & (state < 40 | state > 54)],wnom2[party == 100 & (state < 40 | state > 54)],pch='D',col="red") points(wnom1[party == 100 & state == 52],wnom2[party == 100 & state == 52],pch='D',col="red") points(wnom1[party == 200],wnom2[party == 200],pch='R',col="blue") # Main title mtext("104th Senate From W-NOMINATE\nWith Bootstrapped Standard Errors",side=3,line=1.50,cex=1.2,font=2) # x-axis title mtext("Liberal - Conservative",side=1,line=2.75,cex=1.2) # y-axis title mtext("Social/Lifestyle Issues",side=2,line=2.5,cex=1.2) # # # This code does the cross-hairs for the standard errors. If the # correlation is greater than .15 between the two dimensions, # the 95% confidence ellipse is shown # for (i in 1:nrow) { # # These two statements do the cross-hairs # lines(c(wnom1[i],wnom1[i]),c(wnom2[i]-1.96*std2[i],wnom2[i]+1.96*std2[i]),col="gray") lines(c(wnom1[i]-1.96*std1[i],wnom1[i]+1.96*std1[i]),c(wnom2[i],wnom2[i]),col="gray") # # This if statement does the ellipse # if (abs(corr12[i]) > .15){ lines(ellipse(x=corr12[i],scale=c(std1[i],std2[i]), centre=c(wnom1[i],wnom2[i])), col="gray") } } #Run this program and turn in the plot. It should look similar to this:
- FORT.56 are the average Optimal Classification coordinates generated
by running the Optimal Classification Program on every
bootstrap draw. The file will look something
like this:
1 1049990999 0USA 10000CLINTON -0.3160 -0.7979 -0.8467 -0.2906 0.5711 0.5775 0.1089 0.2070 1.0000 -0.8322 -0.8322 1.0000 2 1041470541 0ALABAMA 10000HEFLIN -0.2219 0.0278 0.0011 -0.2066 0.3139 0.3403 0.1975 0.2220 1.0000 -0.9512 -0.9512 1.0000 3 1049465941 0ALABAMA 20000SHELBY 0.6878 -0.0150 0.7162 -0.1751 0.0633 0.1796 0.0529 0.0582 1.0000 -0.3841 -0.3841 1.0000 4 1041490781 0ALASKA 20000MURKOWSKI 0.7285 -0.0996 0.7283 -0.0236 0.0781 0.0929 0.0741 0.0445 1.0000 -0.5790 -0.5790 1.0000 5 1041210981 0ALASKA 20000STEVENS 0.6308 -0.1836 0.6209 -0.2080 0.0682 0.0844 0.0639 0.0763 1.0000 -0.4706 -0.4706 1.0000 etc etc etc 98 1044930873 0WASHING 10000MURRAY -0.8772 -0.0015 -0.9068 -0.1126 0.0506 0.1520 0.0378 0.0919 1.0000 0.0408 0.0408 1.0000 99 104 136656 0WEST VI 10000BYRD, ROBER -0.6919 -0.5972 -0.6128 -0.3697 0.0959 0.2762 0.0448 0.1300 1.0000 -0.7018 -0.7018 1.0000 100 1041492256 0WEST VI 10000ROCKEFELLER -0.7673 -0.0990 -0.7713 -0.1318 0.0585 0.0952 0.0554 0.0842 1.0000 0.2288 0.2288 1.0000 101 1044930925 0WISCONS 10000FEINGOLD -0.9161 0.3985 -0.8439 0.3498 0.0901 0.1056 0.0457 0.0875 1.0000 0.7287 0.7287 1.0000 102 1041570325 0WISCONS 10000KOHL -0.6915 0.2468 -0.7519 0.4653 0.1107 0.2343 0.0859 0.0404 1.0000 -0.2221 -0.2221 1.0000 103 1041471068 0WYOMING 20000SIMPSON 0.5253 -0.1192 0.5113 -0.0559 0.0463 0.1029 0.0417 0.0743 1.0000 -0.3008 -0.3008 1.0000 104 1041563368 0WYOMING 20000THOMAS 0.7193 0.1231 0.7215 0.1730 0.0458 0.1302 0.0434 0.1130 1.0000 -0.4295 -0.4295 1.0000The average legislator coordinates are are the third and fourth columns after the name of the legislator (shown in red). For example, former President Clinton's coordinates are -0.8467 and -0.2906.
Use R to plot the legislators in two dimensions from the FORT.56 file. Use "D" for Non-Southern Democrats, "S" for Southern Democrats, "R" for Republicans, and "P" for President Clinton. This graph should be in the same format as the one you did for question 2.f of Homework 5.
- Write an Epsilon keyboard macro as a text file that
combines the legislator coordinates from FORT.26 with those from FORT.56.
Assume that the macro begins with FORT.26 in the top window, FORT.56 in
the second window, and the combined file in the third window (see
question 1.a of Homework 5). Leave the header on
each record. Turn in a listing of the macro and a neatly formatted listing of the file.
- Let A be the matrix of legislator coordinates from FORT.26 after
subtracting off the column means, and let
B be the matrix of legislator coordinates from FORT.56 after subtracting
off the column means. Note that subtracting
off the column means of both matrices centers both at the origin, (0.0, 0.0). Solve for the
orthogonal procrustes rotation matrix, T,
for B. Namely, we want to minimize:
L(T) = tr(A - BT)(A - BT)'
The solution is:
T = VU' where
A'B = ULV'
where ULV' is the Singular Value Decomposition of A'B (see Borg and Groenen, pp. 430-432).
In R you can perform the decompostion with the svd command. For example:
C <- t(A)%*%B
svddecomp <- svd(C)
svddecomp$u has the matrix U
svddecomp$v has the matrix V
svddecomp$d has the diagonal of L
Note that you can check your work as we discussed in class by doing the following:
D <- diag(svddecomp$d)
U <- svddecomp$u
V <- svddecomp$v
ABCHECK <- U%*%D%*%t(V)
errorcheck <- sum((C-ABCHECK)^2)
Solve for T and turn in a neatly formatted listing. Compute the Pearson r-squares between the corresponding columns of A and B before and after rotating B.
Go to the IDEAL beta website and download rollcall_0.3.3.zip. (If the site is down rollcall_0.3.3.zip is here.) Install the package from the Install Package From Local Zip File in the Packages drop-down menu in R.
Download the R programs below along with the Rdata file for the 104th Senate roll calls and put them in the same directory:
idealKeith.r -- Program to
run IDEAL on the 104th Senate.
S104.Rdata -- R
Data file for 104th Senate#
# idealKeith.r -- Implements Simon's IDEAL in R
#
rm(list=ls(all=TRUE))
#
library(rollcall)
load("C:/ucsd_homework_7/s104.Rdata")
rc <- rollcall(s104)
csts <- constrain.legis(rc,x=list("KENNEDY, ED"=-1, "HELMS"=1),d=1) This Sets up the Constraints
kpideal <- ideal(rc, priors=csts, startvals=csts, store.item=TRUE)
sumkpideal <- summary(kpideal,include.beta=TRUE)
write.table(sumkpideal$x.quant,"c:/ucsd_homework_7/tab_simon2_104a.txt")
write.table(sumkpideal$beta.quant,"c:/ucsd_homework_7/tab_simon3_104a.txt")
#
The tab_simon2_104a.txt file has the esimated legislator ideal points in a format similar to
those from MCMCPack that you estimated for
question 1 of Homework 5. The file should look something like
this:"Posterior Mean" "2.5%" "97.5%"
"HEFLIN" -0.260539161797188 -0.324277175440612 -0.199695576187364
"SHELBY" 1.23019206655678 1.06776517589545 1.36349750197643
"MURKOWSKI" 1.63087942782254 1.40849022832179 1.84561135364064
"STEVENS" 1.04610545982508 0.896182928917575 1.17542884158099
"KYL" 2.37619418505450 2.08958357054717 2.75349078670169
etc etc etc
"ROCKEFELLER" -1.10508436917944 -1.25041868040856 -0.982633707475099
"FEINGOLD" -1.03161420524957 -1.14931288681370 -0.923294768025014
"KOHL" -0.792982659833952 -0.884197325699718 -0.720465242777936
"SIMPSON" 0.912732416007879 0.815117422171576 1.02451977202073
"THOMAS" 1.76901626257382 1.58540354678476 1.96376294419763
- Write a macro similar to the one you used in
question 1.a. and 1.b. of Homework 5 to make a nicely formatted
file of the legislator coordinates. Turn in a copy of this macro.
- Replicate question 1.c. of Homework 5. Write
an R program that graphs the rank
ordering from Optimal Classification (horizontal axis) against
the IDEAL medians (vertical axis).
Label the axes appropriately and label a few of the Senators including Campbell (D-CO)
and Campbell (R-CO).
- Report the correlation between the OC rank ordering
and the IDEAL medians.
- Use Epsilon to combine the file you created in (a) with that from
question 1.a. and 1.b. of Homework 5 and include in that file the
2.5% and 97.5% quantiles corresponding to the medians of both procedures. Report the Pearson
correlation between the lengths of the "confidence intervals" for the two Bayesian procedures and also
report the Pearson correlation between the IDEAL medians and
MCMCPack medians.
- (20 Points Extra Credit) Write an R program that reads the
above file and produces a two dimensional plot of the legislator ideal points where the horizontal
dimension coordinates are the IDEAL medians and the vertical dimension
coordinates are the MCMCPack medians. Put cross-hairs through the points
with lengths equal to the corresponding distance between the 2.5% and 97.5% quantiles. Plot Northern
Democrats with "D" tokens, Southern Democrats with "S" tokens, and Republicans with "R" tokens. Note
that you might want to divide all of the MCMCPack quantile values by 2
because IDEAL has Kennedy/Helms at -1/+1 and MCMCPack
has Kennedy/Helms at -2/+2.
Sen102kh.ord -- ASCII
Data file for 102nd Senate (used with PERFL.EXE)
Sen102kh.dta -- Stata
Data file for 102nd Senate (used with keith2.r)
S102.Rdata -- IDEAL
Data file for 102nd Senate (used with idealKeith.r)and place them in the appropriate directories where you have their associated programs.
- Run Optimal Classification (PERFL.EXE) in one dimension on
SEN102KH.ORD. There were 550 roll calls in the 102nd Senate. Note that the
Optimal Classification Program Page has detailed
instructions on how to set up PERFSTRT.DAT. Turn in a copy of PERFSTRT.DAT and
PERF21.DAT.
- Run keith2.r to generate sen102kh.rda and then run keithMCMC.r to get the
Senator medians and 2.5% and 97.5% quantiles. Combine the rank ordering from
Optimal Classification with the tab2.txt output from
MCMCPack and graph the rank ordering (horizontal axis) against
the Bayesian MCMC medians (vertical axis) as you did for Question 2.c of
Homework 5.
- Report the Pearson correlation between the OC rank ordering
and the MCMCPack medians.
- Graph the rank
ordering from Optimal Classification (horizontal axis) against
the IDEAL medians (vertical axis).
- Report the correlation between the OC rank ordering
and the IDEAL medians.
- Report the Pearson correlation between the lengths of the "confidence intervals"
(2.5%, 97.5%) for the two Bayesian procedures and also
report the Pearson correlation between the IDEAL medians and
MCMCPack medians.
- Turn in a copy of the HOUSYM3.DAT output file.
- Turn in a plot of the eigenvalues. Use R to do the plot.
- Use Epsilon to enter the following
commands on top of your agreement score output file:
TORSCA PRE-ITERATIONS=3 DIMMAX=3,DIMMIN=1 COORDINATES=ROTATE ITERATIONS=50 REGRESSION=DESCENDING DATA,LOWERHALFMATRIX,DIAGONAL=PRESENT,CUTOFF=.01 104TH U.S. SENATE AGREEMENT SCORES 104 1 1 (36X,104F4.0) *********agreement score file********* COMPUTE STOP
Be sure to put the COMPUTE and STOP lines on the bottom of the agreement score file.
Run KYST on this file and report the STRESS values for one, two, and three dimensions.
- Use Epsilon to combine the one-dimensional KYST
coordinates with the OC rank orderings, the
MCMCPack medians, and the IDEAL medians with
the headers on the file. Turn in a neatly formatted listing of this file. Compute the 4 by 4
matrix of Pearson correlations and turn in a neatly formatted and clearly labeled table
of these correlations.
- Any thoughts about what the above table of correlations tells us about this enterprise as a cumulative
science?