search for: pc7

...cx.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, residencies); diff -r 662dbf6ee71c tools/libxc/xenctrl.h -...

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 yes but it does not !!! can someone...

...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" + "pc6 : [%20"PRIu64" ms]\n" + &qu...

...d 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=TRUE,tol...

...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 0.961720 0.97...

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 didn...

..., 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 PC10...

...mary(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 0.9200...

...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]]

...;> >>>> 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 Pro...

...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 >...

...ot; contains some seasonal GDP with some independent variables. I want to fit a nonlinear least square using restricted maximum likelihood estimation. There exists a AR(4) correlation structure (only the 4th AR) in the error term. So I enter the following commands: > gnls(ldgdp~+pc2+pc3+pc4+pc5+pc7+t,data=lx.sea,correlation=corARMA(p=4,q=0)) It results not only the 4th AR but also the first 3 order AR in the equation. So how can I fit my model with only the 4th AR?? Are there any other function in R allow me to do that?? Thanks for the help and look forward to your reply. Yours, Vincent -...

...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 273 12(1,L1) S5 6372 278 28585 5210 10605 664 259 13(1...

...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 avg.dma...

...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)

...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 0.789...

...esidency [ 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 : 0 affected_cpus : 0 cpuinfo frequency...

...esidency [ 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 : 0 affected_cpus : 0 cpuinfo frequency...

...> 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 >&gt...

Hi All: I am running prcomp on a very large array, roughly [500000, 3650]. The array itself is 16GB. I am running on a Unix machine and am running ?top? at the same time and am quite surprised to see that the application memory usage is 76GB. I have the ?tol? set very high (.8) so that it should only pull out a few components. I am surprised at this memory usage because prcomp uses the SVD