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Hello I have a data set like below: plate.id well.id Group HYB rlt1 1 P1 A1 Control SKOV3hyb 0.190 2 P1 A2 Control SKOV3hyb 0.210 3 P1 A3 Control SKOV3hyb 0.205 4 P1 A4 Control SKOV3hyb 0.206 5 P1 A5 Control SKOV3hyb 0.184 385 P1 A1 ovca SKOV3hyb 0.184 386 P1 A2 ovca SKOV3hyb 0.229 387

Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

Hello to the whole group. I am a newbie to R, but I got my way through and think it is a lot easier to handle than other software packages (far less clicks necessary). However, I have a problem with respect to the summary of regression results. The summary function gives sth like: Residuals: Min 1Q Median 3Q Max -0.46743 -0.09772 0.01810 0.11175 0.42252

Hi, I've this dataframe: V1 V5 V6 1 MOD13Q1_2000049 0.1723 A1 2 MOD13Q1_2000049 0.1824 B1 3 MOD13Q1_2000049 0.1824 C1 4 MOD13Q1_2000049 0.1774 A2 5 MOD13Q1_2000049 0.1953 B2 6 MOD13Q1_2000049 0.1824 C2 7 MOD13Q1_2000065 0.1921 A1 8 MOD13Q1_2000065 0.1938 B1 9 MOD13Q1_2000065 0.2009 C1 10 MOD13Q1_2000065 0.2035 A2 11 MOD13Q1_2000065 0.2157 B2 12

Hi all, I have been testing network throughput and latency and I was wondering if my measurements are as expected. For the test, I used Fedora 17 for both host and guest, using kernel 3.5.2-3.fc17.86_64. Pinging an external server on the LAN from the host, using a gigabit interface, the results are: # ping -c 10 172.16.1.1 PING 172.16.1.1 (172.16.1.1) 56(84) bytes of data. 64 bytes from

Hi all, I have been testing network throughput and latency and I was wondering if my measurements are as expected. For the test, I used Fedora 17 for both host and guest, using kernel 3.5.2-3.fc17.86_64. Pinging an external server on the LAN from the host, using a gigabit interface, the results are: # ping -c 10 172.16.1.1 PING 172.16.1.1 (172.16.1.1) 56(84) bytes of data. 64 bytes from

I found out the other day that crossprod() will take a single matrix argument; crossprod(x) notionally returns crossprod(x,x). The two forms do not return identical matrices: x <- matrix(rnorm(3000000),ncol=3) M1 <- crossprod(x) M2 <- crossprod(x,x) R> max(abs(M1-M2)) [1] 1.932494e-08 But what really surprised me is that crossprod(x) is slower than crossprod(x,x): R>

# I have a question about how factanal is calculating the regression factor # scores based on an oblique rotation (promax) of the factors. # # As is explained in the help file, regression factor scores are # obtained as # # hat f = Lambda' Sigma^-1 x # # However, according to Harman's "Modern Factor Analysis" (e.g. second # edition, pp. 351-352) the formula is # # hat f = Phi

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Dear List: the CODA and BOA packages for the analysis of MCMC output yield different results on two dignostic test of convergence: 1) Geweke's convergence diagnostic; 2) Heidelberger and Welch's convergence diagnostic. Does that imply that the CODA and BOA packages implement different ``flavors'' of the same test? I paste below an example. Geweke's test

Hello, it seems that the main results of survival analysis with package survival are shown only as side effects of the print method. If I compute e.g. a Kaplan-Meier estimate by > km.survdur<-survfit(s.survdur) then I can simply print the results by > km.survdur Call: survfit(formula = s.survdur) n events median 0.95LCL 0.95UCL 100.0 58.0 46.8 41.0 79.3 Is

Hi! Some background: I am trying to create an application that would encode video taken by USB camera using Theora and then send it to the client. I have almost succeeded, but I have one problem. When I grab video frames from the camera and encode them they form 4KB OGG pages, then I send them over TCP/IP to the client application. Since I want to achieve as small latency as possible I

Hi I have a follow up question, relating to subsetting to list items. After using the list and min(sapply()) method to adjust the length of the variables, I specify a dynamic regression equation using the variables in the list. My list looks like this: Dcr<- list(Dcre1=DCred1,Dcre2=DCred2,Dcre3=DCred3,Dbobc1=DBoBC1,Dbobc2=DBoBC2,Dbobc3=DBoBC3,...) By specifying the list items with names, I

Hello! Has anybody experienced network latency problems with combination of Windows 7, GPLPV drivers and RDP connection? Any Windows pop-up message(such as "command not found" error message in "Run command:" dialog, or dividing by zero in windows calc) causes a short freeze of RDP session and looks like that from dom0: PING 192.168.44.65 (192.168.44.65) 56(84) bytes of data.

Motivated by Deepayan's recent inquiries about the efficiency of the R 'quantile' function: http://tolstoy.newcastle.edu.au/R/devel/05/11/3305.html http://tolstoy.newcastle.edu.au/R/devel/06/03/4358.html I decided to try to revive an old project to implement a version of the Floyd and Rivest (1975) algorithm for finding quantiles with O(n) comparisons. I used

Hi, I have three questions concerning GLMMs. First, I ' m looking for a measure for the significance of the random variable in a glmm. I'm fitting a glmm (lmer) to telemetry-locations of 12 wildcat-individuals against random locations (binomial response). The individual is the random variable. Now I want to know, if the individual ("TIER") has a significant effect on the model

Dear useRs, I reran an analysis with bam (mgcv, version 1.7-17) originally conducted using an older version of bam (mgcv, version 1.7-11) and this resulted in the same estimates, but much lower standard errors (in some cases 20 times as low) and lower p-values. This obviously results in a larger set of significant predictors. Is this result expected given the improvements in the new version? Or

Dear All- I have a dataset that is comprised of the following: doy yr mon day hr hgt1 hgt2 hgt3 co21 co22 co23 sig1 sig2 sig3 dif flag 244.02083 2005 09 01 00 2.6 9.5 17.8 375.665 373.737 373.227 3.698 1.107 0.963 -0.509 PRE 244.0625 2005 09 01 01 2.6 9.5 17.8 393.66 384.773 379.466 15.336 11.033 5.76 -5.307 PRE 244.10417 2005 09 01 02 2.6 9.5 17.8 411.162 397.866 387.755 6.835 5.61 6.728

Hello all- I am a relatively new user of R and am working through a graduate course in Statistics that uses Minitab, SAS and some Matlab. I like using R but am having some trouble lining up the output from lda() to that of the other programs' results. The dataset below is a modified set of wine data from the Pinot Noir data set as an illustration of the 2 group LDA scenario. Mo Ba

Hello, I would like to use a permanova to analyze my repeated measures, multivariate data set using the package vegan (adonis function). I have several explanatory variables and many clinical biochemistry/hematological response variables (all continuous, non-normally distributed). My explanatory variables are: ID - 29 levels - individual subjects Treatment - 2 levels Sex - 2 levels Time -