search for: pc6

Hi, i''m sort of testing a configuration and things are not working sa i planned. i have the following network diagram: PC1 to 7 cnneced on the same ethernet hub. PC1 PC2 PC 3 PC4 PC5 PC6 on network 192.168.5.0 PC6 and PC7 on network 192.168.1.0 so PC6 work as a router. in addition, PC6 is connected to both network on the same device eth0. now on PC6, put a tbf on dev eth0 root with a rate 100ko/s send data from PC1 to PC7. is the data shaped by the tbf ? i''d say y...

...55,13 @@ cxpt->nr = sysctl.u.get_pmstat.u.getcx.nr; cxpt->last = sysctl.u.get_pmstat.u.getcx.last; cxpt->idle_time = sysctl.u.get_pmstat.u.getcx.idle_time; + cxpt->pc2 = sysctl.u.get_pmstat.u.getcx.pc2; cxpt->pc3 = sysctl.u.get_pmstat.u.getcx.pc3; cxpt->pc6 = sysctl.u.get_pmstat.u.getcx.pc6; cxpt->pc7 = sysctl.u.get_pmstat.u.getcx.pc7; cxpt->cc3 = sysctl.u.get_pmstat.u.getcx.cc3; cxpt->cc6 = sysctl.u.get_pmstat.u.getcx.cc6; + cxpt->cc7 = sysctl.u.get_pmstat.u.getcx.cc7; unlock_2: xc_hypercall_bounce_post(xch, resid...

...ike: a b c d e f g v1 # # # # # # # v2 # # # # # # # v3 # # # # # # # v4 # # # # # # # v5 # # # # # # # v6 # # # # # # # I want to give the following column names to each data.frame: ("SDev", "PC1", "PC2", "PC3", "PC4", "PC5", "PC6") Works fine for one data.frame: column_names <- c("SDev", "PC1", "PC2", "PC3", "PC4", "PC5", "PC6") names( df1 ) <- column_names How is it to be done at once for all data.frames that the function objects(pattern = &...

...First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it. SAS,STATA,and SPSS have the same results, so i put them together. From their results, we see that the absolute values of coeffiecient are same, but PC1,PC2,PC4,PC5 and PC6 in R have the opposite sign on the coeffiecnts contrast with SAS, and PC4,PC5 in S-PLUS have the opposite sign on the coeffiecnts contrast with SAS. Curiously, I got the same results amont all these software using my another dataset. *R's results of PCA:* *PC1* *PC2* PC...

...residency [%20"PRIu64" ms]\n", cxstat->residencies[i]/1000000UL); } - printf("pc2 : [%020"PRIu64" ms]\n" - "pc3 : [%020"PRIu64" ms]\n" - "pc6 : [%020"PRIu64" ms]\n" - "pc7 : [%020"PRIu64" ms]\n", + printf("pc2 : [%20"PRIu64" ms]\n" + "pc3 : [%20"PRIu64" ms]\n" + &q...

...reduced number of components, i can evaluate % of variance explained, or am I missing something?? 8 variables in the data set > princ = prcomp(df[,-1],rotate="varimax",scale=TRUE) > summary(princ) Importance of components: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Standard deviation 1.381 1.247 1.211 0.994 0.927 0.764 0.6708 0.4366 Proportion of Variance 0.238 0.194 0.183 0.124 0.107 0.073 0.0562 0.0238 Cumulative Proportion 0.238 0.433 0.616 0.740 0.847 0.920 0.9762 *1.0000* > princ = prcomp(df[,-1],rotate="varimax",scale=T...

...he summary of my PCA analysis. I can do it very painstaking with text for each row and make sure that everything aligns and so on but i wonder if there is a better method than that. My summary follows: m1 PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9Standard deviation 12.67140 8.252798 7.407514 3.71649 3.338228 1.974144 1.813919 1.416385 0.9981993Proportion of Variance 0.50054 0.212320 0.171060 0.04306 0.034740 0.012150 0.010260 0.006250 0.0031100Cumulative Proportion 0.50054 0.712870 0.883920 0.92698 0....

...de the *K =? *and *para=c(7,4,4,1,1,1)) . So, here k=6 i.e the no of Principal Components. and each pcs say , * ** pc1 number of non zero loading is 7 pc2 number of non zero loading is 4 pc3 number of non zero loading is 4 pc4 number of non zero loading is 1 pc5 number of non zero loading is 1 pc6 number of non zero loading is 1 *How can I know in which pc,s how many non zero loadings will be? Any code??? One answer can be cross validation but I did not find in the package. * ** *Thanks for your help* *Code:* library(elasticnet) > out2<-spca(pitprops,*K=6*,type="Gram",spar...

Hey, I need some help. I want to obtain a cross validation for a regression model (binary response) but I got an error with CVbinary. Well I did this: fit <- lm(resp ~ PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 + PC9+PC10+PC11+PC12+PC13+PC14+PC15+PC16+PC17+PC18+PC19+PC20+PC21+PC22+PC23+PC24+PC25+PC26+PC27+PC28, data = dexp.cp, family=binomial()) CVbinary(fit) Error in sample(nfolds, m, replace = TRUE) : invalid 'size' argument I cannot understand this error, I was googling it, but i...

...l(cov(Z), S, tol = 0.08) pZ <- prcomp(Z, tol = 0.1) summary(pZ) # only ~14 PCs (out of 32) I get for the last line, the summary.prcomp(.) call : > summary(pZ) # only ~14 PCs (out of 32) Importance of components: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Standard deviation 3.6415 2.7178 1.8447 1.3943 1.10207 0.90922 0.76951 0.67490 Proportion of Variance 0.4352 0.2424 0.1117 0.0638 0.03986 0.02713 0.01943 0.01495 Cumulative Proportion 0.4352 0.6775 0.7892 0.8530 0.89288 0.92001 0.93944 0.95439 PC9...

...;lists'. the second component is a list that comtains the following: > mylist <- churn[2] > class(mylist) [1] "list" > mylist PC1 PC2 PC3 PC4 PC5 PC6 Account_Length -8.930570e-03 2.383719e-03 -2.414908e-02 -1.004704e-01 9.941468e-01 2.867606e-02 Area_Code 1.543767e-02 -1.710566e-02 -6.258129e-03 9.945271e-01 1.008478e-01 -8.890155e-03 Intl_Plan -2.627796e-04 -1.809011e-04 -9.542...

...; > summary(pZ) # only ~14 PCs (out of 32) > > > > I get for the last line, the summary.prcomp(.) call : > > > > > summary(pZ) # only ~14 PCs (out of 32) > > Importance of components: > > PC1 PC2 PC3 PC4 PC5 PC6 > > PC7 PC8 > > Standard deviation 3.6415 2.7178 1.8447 1.3943 1.10207 0.90922 > 0.76951 > > 0.67490 > > Proportion of Variance 0.4352 0.2424 0.1117 0.0638 0.03986 0.02713 > 0.01943 > > 0.01495 > > Cumulative Proportion 0.4352 0.6775 0.7892 0.8530...

...8110 1.0000 Conditional 0.8681 0.4793 32 Constrained 0.0000 0.0000 0 Unconstrained 0.9429 0.5207 29 Inertia is variance Some constraints were aliased because they were collinear (redundant) Eigenvalues for unconstrained axes: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 0.16008 0.14733 0.12183 0.09054 0.07380 0.06971 0.05578 0.04215 (Showed only 8 of all 29 unconstrained eigenvalues) -- Nevil Amos Molecular Ecology Research Group Australian Centre for Biodiversity Monash University CLAYTON VIC 3800 Australia [[alternative HTML version deleted]...

...ut of 32) >>>> >>>> I get for the last line, the summary.prcomp(.) call : >>>> >>>>> summary(pZ) # only ~14 PCs (out of 32) >>>> Importance of components: >>>> PC1 PC2 PC3 PC4 PC5 PC6 >>>> PC7 PC8 >>>> Standard deviation 3.6415 2.7178 1.8447 1.3943 1.10207 0.90922 >>> 0.76951 >>>> 0.67490 >>>> Proportion of Variance 0.4352 0.2424 0.1117 0.0638 0.03986 0.02713 >>> 0.01943 >>>> 0.01495 >>&g...

...pZ <- prcomp(Z, tol = 0.1) > summary(pZ) # only ~14 PCs (out of 32) > > I get for the last line, the summary.prcomp(.) call : > > > summary(pZ) # only ~14 PCs (out of 32) > Importance of components: > PC1 PC2 PC3 PC4 PC5 PC6 > PC7 PC8 > Standard deviation 3.6415 2.7178 1.8447 1.3943 1.10207 0.90922 0.76951 > 0.67490 > Proportion of Variance 0.4352 0.2424 0.1117 0.0638 0.03986 0.02713 0.01943 > 0.01495 > Cumulative Proportion 0.4352 0.6775 0.7892 0.8530 0.89288 0.92001 0.93944 > 0.95439 &g...

...po CII Apo CIII Apo E Total Events 1(1,A1) PC1 32310 5947 55069 21856 66539 54589 321 2(1,B1) PC2 33772 2754 50452.5 20588 68813 45586 268 3(1,C1) PC3 25386 960 38045 12992 57239 28939.5 275 4(1,D1) PC4 23604 475 40056 14433 59969.5 19788 275 5(1,E1) PC5 13469 216 31967 8900 55937 13534 257 6(1,F1) PC6 11142.5 174.5 30025 9465 64848 11053 244 7(1,G1) PC7 1021 129.5 11405 3661 50841 2466 236 8(1,H1) S1 29339 7721.5 55777 14939 50864 11369 252 9(1,I1) S2 17315.5 2485 40010 10756.5 45471.5 5133 239 10(1,J1) S3 18440 1406 47276.5 15425 44740 2815.5 264 11(1,K1) S4 19011 756 55982.5 12801 38148 1265 2...

...round: I am looking at the ordination of abiotic stream variables between different sampling locations. abiot.pca=prcomp(all24[, c(10, 13:18)], retx=TRUE, center=TRUE, scale= TRUE) summary(abiot.pca) Importance of components: PC1 PC2 PC3 PC4 PC5 PC6 PC7 Standard deviation 1.5925 1.0814 1.0697 0.9536 0.76624 0.68444 0.43037 Proportion of Variance 0.3623 0.1671 0.1635 0.1299 0.08387 0.06692 0.02646 Cumulative Proportion 0.3623 0.5294 0.6928 0.8227 0.90662 0.97354 1.00000 loadings[,1:3] PC1 PC2 PC3...

...1.0000 Conditional 0.8681 0.4793 32 Constrained 0.0000 0.0000 0 Unconstrained 0.9429 0.5207 29 Inertia is variance Some constraints were aliased because they were collinear (redundant) Eigenvalues for unconstrained axes: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 0.16008 0.14733 0.12183 0.09054 0.07380 0.06971 0.05578 0.04215 (Showed only 8 of all 29 unconstrained eigenvalues)

...y(pZ) # only ~14 PCs (out of 32) >>> >>> I get for the last line, the summary.prcomp(.) call : >>> >>>> summary(pZ) # only ~14 PCs (out of 32) >>> Importance of components: >>> PC1 PC2 PC3 PC4 PC5 PC6 >>> PC7 PC8 >>> Standard deviation 3.6415 2.7178 1.8447 1.3943 1.10207 0.90922 >> 0.76951 >>> 0.67490 >>> Proportion of Variance 0.4352 0.2424 0.1117 0.0638 0.03986 0.02713 >> 0.01943 >>> 0.01495 >>> Cumulative Proportion 0....

...n [ 371] residency [ 495 ms] C3 : transition [ 35121] residency [ 230822 ms] pc2 : [ 0 ms] pc3 : [ 0 ms] pc6 : [ 0 ms] pc7 : [ 0 ms] cc3 : [ 0 ms] cc6 : [ 0 ms] cc7 : [ 0 ms] # xenpm get-cpufreq-para cpu id...