njit-etd2003-081 - New Jersey Institute of Technology
njit-etd2003-081 - New Jersey Institute of Technology njit-etd2003-081 - New Jersey Institute of Technology
293 >> normal = [48 49 50 51 52 53 54 55]; >> names(normal,:) ans = ba ja rajf rajm ka ma ro so 10. To remove these rows from the ratings matrix, type the following. >> rsubset = cpdata; >> nsubset = names; >> nsubset(normal,:) = [ ]; >> rsubset(normal,:) = [ ]; >> size(rsubset) ans = 47 15 11. To practice, repeat the analysis using the variable rsubset as the new data matrix and nsubset as the string matrix of labels. The Component Variances (Third Output) 12. The third output, variances, is a vector containing the variance explained by the corresponding column of newdata. >> variances
294 variances = 3.4083 1.2140 1.1415 0.9209 0.7533 0.6306 0.4930 0.3180 0.1204 13. One can easily calculate the percent of the total variability explained by each principal component. >> percent_explained = 100*variances/sum(variances) percent_explained = 37.8699 13.4886 12.6831 10.2324 8.3698 7.0062 5.4783 3.5338 1.3378 14. A "Scree" plot is a pareto plot of the percent variability explained by each principal component. >> pareto(percent_explained) >> xlabel('Principal Component') >> ylabel('Variance Explained (%)') We can see that the first three principal components explain roughly two thirds of the total variability in the standardized ratings.
- Page 271 and 272: APPENDIX A EXERCISE PHYSIOLOGY A.1
- Page 273 and 274: 244 A.3 Figure Out Your Target Hear
- Page 275 and 276: APPENDIX B ANALYSIS PROGRAM LISTING
- Page 277 and 278: 248 4) Click on file, close to exit
- Page 279 and 280: 250 • TN 11
- Page 281 and 282: 252 B.1.2 Partial Coherence Between
- Page 283 and 284: 254
- Page 285 and 286: 256 Block Diagram !rime of record K
- Page 287 and 288: 258
- Page 289 and 290: 260 B.2.2 Time — Frequency Analys
- Page 291 and 292: 262 This program provides the STFT
- Page 293 and 294: 264 G(:j+1)=G(:,j+1)/(2*sum(G(:j+1)
- Page 295 and 296: 266 T=(length(Signa)/sample)/(Times
- Page 297 and 298: 268 subplot(3, 1,3), plot(T,E); xla
- Page 299 and 300: 270 4. The program creates five out
- Page 301 and 302: 272 B.2.3.4 Program to Generate Sym
- Page 303 and 304: 274 ylabel('frequency'); title('Ins
- Page 305 and 306: 276 The program will run and output
- Page 307 and 308: 278 axis([0 1 0 2]); grid on; xlabe
- Page 309 and 310: 280 vagal=sum(TFDs(HFC,1:k)); symto
- Page 311 and 312: 282 plot(J,symtopar); %plot(A,symto
- Page 313 and 314: 284 4. Remove the constant levels a
- Page 315 and 316: 286 Make sure the agreement is quit
- Page 317 and 318: 288 B.2.6 Principal Components Anal
- Page 319 and 320: 290 Columns 12 through 15 'LF_pcoh_
- Page 321: 292 I= 1.0000 -0.0000 -0.0000 -0.00
- Page 325 and 326: 296 B.2.7 Cluster Analysis Program
- Page 327 and 328: 298 end [R,C]=size(Data); if length
- Page 329 and 330: 300 B.2.8 Cross-correlation Program
- Page 331 and 332: 302 C.3 Partial coherence of HR and
- Page 333 and 334: 304 [13] Madwed, J., and R. Cohen.
- Page 335 and 336: 306 [41] Mallat, S. G., "A Theory f
- Page 337: [70] Tazebay, M.V., R.T. Saliba and
293<br />
>> normal = [48 49 50 51 52 53 54 55];<br />
>> names(normal,:)<br />
ans =<br />
ba<br />
ja<br />
rajf<br />
rajm<br />
ka<br />
ma<br />
ro<br />
so<br />
10. To remove these rows from the ratings matrix, type the following.<br />
>> rsubset = cpdata;<br />
>> nsubset = names;<br />
>> nsubset(normal,:) = [ ];<br />
>> rsubset(normal,:) = [ ];<br />
>> size(rsubset)<br />
ans =<br />
47 15<br />
11. To practice, repeat the analysis using the variable rsubset as the new data matrix<br />
and nsubset as the string matrix <strong>of</strong> labels.<br />
The Component Variances (Third Output)<br />
12. The third output, variances, is a vector containing the variance explained by the<br />
corresponding column <strong>of</strong> newdata.<br />
>> variances
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