search for: obs1

Let's say I have a within-subject experiment with 2 observables, obs1 and ob2 and 2 independent factors, fac1 and fac2. I can do summary( aov( obs1~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) summary( aov( obs2~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) to test the 2 observables separately. > summary( fit<-manova( cbind(obs1,obs2)~fac1*fac2 + Error(Subject/(...

...taframe into two vectors. I can't seem to figure out how to do that properly, or if I'm suffering from a different misunderstanding.... ====================================== The following code creates the box plot, but the points are not added ====================================== > Obs1 Blue Green Red Gold Orange 1 77902 32911 117543 18245 NA 2 77294 32204 114927 18377 NA 3 75737 31484 115265 18529 NA 4 73366 31130 112371 14748 NA 5 77061 33601 118113 16910 16360 6 75177 32383 113825 14417 15492 7 76766 35697 124304 16318 16100 8 80378 36374 1280...

Hi, newbie question. I have a data-frame with 3 named columns: Name, Obs1, Obs2. The Name column members are made of alphanumeric characters: T1, T2, T3 etc. I would like to acess only that subset of the data-frame with Name == T44. X <- dataframe[dataframe$Name=='T44'] does not work. Any ideas on how to do this? I'm sure I'm missing a simple concept h...

Hi, I have Forecast Class and Observed Class in a data matrix as below. > Sample1 FCT OBS 1 1 5 2 2 4 3 3- 3+ 4 3 3 5 3+ 3- 6 4 2 7 5 1 I want to find the difference between Observed and Forecast Classes. How can I get this done? I tried to following to convert the 1 through 5 classes, to 1 through 7 for both OBS and FCT column. > Sample1$OBS2 <- Sample1$OBS

Hello, I want to do fisher test for the rows in data file which has value less than 5 otherwise chi square test .The p values from both test should be stored in one resulted file. but there is some problem with bold if statement. I don't know how implement this line properly. x = cbind(obs1,obs2,exp1,exp2) a = matrix(c(0,0,0,0), ncol=2, byrow =TRUE) #matrix with initialized values for (i in 1: length(x[,1])) { *if((x[i,1] || x[i,2] || x[i,3] || x[i,4]) < 5)* { a[1,1] <- x[i,1]; a[1,2] <- x[i,2]; a[...

...onot need to conduct the above manipulation. Keep them. #And also keep all the other observations whose donot have the same x and y values. #So the final result should be x y date prior var5 1 1.1 2 2008-12-01 A 1 5 1.3 1.8 2007-05-06 C 5 2 1.3 1.8 2007-05-20 N 2 #obs1 and obs3 have same x and y; Their date difference is 5, so we will select the earlist obs1 But we notice that its prior value is N, while another is A, so we also need to replace N using A. #obs2,obs4 and obs5 have same x and y; Obs4 and obs5 has a date difference 4<=8days, so earlies obs 5 will...

...part1 (obs2 and obs3) and part2 (obs4,obs5, and obs6). For obs2 and obs3, their date difference is less than 7, so we donot need to delete any of them. For obs4,obs5, and obs6,we can see that obs6 should be deleted becuase its date is over 7 dyas longer than obs4. So the final dataset should obs1,obs2,obs3,obs4, and obs5. I have a lot of observations in my dataset, so i hope to do this automatically. Any ideas on this? Thanks. -- ----------------- Jane Chang Queen's [[alternative HTML version deleted]]

hi, all I inherited a data set with format like the following: para.A para.B para.C observation.1 observation.2 observation.3 .... Essentially, the same experiment (with same parameters) are conducted 3 times, with corresponding observations. What are the efficient ways of re-arranging this data set in a one row per observation such that: para.A para.B para.C observation.1 para.A

Dear R Users, I have some data that is very similar in form to a 3D image - ie univariate data on a regular 3D grid. I keep this as a 3D numeric array in R with attributes describing the sampling points along the 3 dimensions. I would like to interpolate this onto a new regular 3D grid that I specify (eg by supplying 3 vectors corresponding to the new grid locations on each of the 3

...VFN3.VFN 0560: 34 08 00 48 05 4e 56 47 41 20 4e 56 48 41 20 41 4..H.NVGA NVHA A 0570: 4d 44 41 20 44 49 44 36 20 44 49 44 37 20 44 49 MDA DID6 DID7 DI 0580: 44 38 20 45 42 41 53 20 43 50 53 50 20 45 45 43 D8 EBAS CPSP EEC 0590: 50 20 45 56 43 50 20 58 42 41 53 20 4f 42 53 31 P EVCP XBAS OBS1 05a0: 20 4f 42 53 32 20 4f 42 53 33 20 4f 42 53 34 20 OBS2 OBS3 OBS4 05b0: 4f 42 53 35 20 4f 42 53 36 20 4f 42 53 37 20 4f OBS5 OBS6 OBS7 O 05c0: 42 53 38 20 00 48 05 41 54 4d 43 08 50 54 4d 43 BS8 .H.ATMC.PTMC 05d0: 08 41 54 52 41 08 50 54 52 41 08 50 4e 48 4d 20 .ATRA.PTRA.PNHM 0...