search for: trans1

...t;\t", na.strings="NA", dec=".", strip.white=TRUE) attach(Dataset) require (nlme) with(Dataset, table(runnb, id, grp)) b.lvls <- table(Dataset$runnb) nb <- length(b.lvls) fit <- vector(mode="list", nb) for(i in 1:nb) fit[[i]]<- lme (trans1 ~ Index1 + grp, random = ~ 1 | id / grp , data = Dataset, na.action = "na.exclude") This (above) worked OK only I am having memory problems. I have a gig of RAM set at --sdi --max-mem-size=512M (complete version below) I am wondering if run...

Suppose this is my list of transactions: set.seed(200) tran=random.transactions(100,3) inspect(tran) items transactionID 1 {item80} trans1 2 {item8, item20} trans2 3 {item28} trans3 I want to get the 'transpose' of the data, i.e. transactionID items 1 {trans2} item8 2 {trans2} item20 3 {trans3} item28 4 {trans1} item80 I tried converting tran into a matrix, then transpose it...

Hello all, I have problem with setting up random effects. I have a model: y=x1+x2+x1*x2+z1+z1*x2 where x1, x2, x1*x2 are fixed effects and z1, z1*x2 are random effects (crossed effects) I use library(nlme) 'lme' function. My question is: how I should set up random effects? I did lme(y~x1+x2+x1:x2, data=DATA, random=~z1+z1:x2, na.action='na.omit') but it did not work.

...I have 6 sites, each measured 5 times over the past year. During each sampling time, I counted the occurrences of different benthic components (coral, dead coral, sand, etc.) over 5 transects in each site site time transect coral deadcoral sand rubble ..... S1 time1 trans1 10 15 10 4 S1 time1 trans2 5 4 10 6 S1 time1 trans3 10 2 5 7 . . . S5 time5 trans5 6 3 1 6...

...Subject: [R] package "arules" - 'transpose' of the transactions To: r-help@r-project.org Date: Monday, December 20, 2010, 8:41 AM Suppose this is my list of transactions: set.seed(200) tran=random.transactions(100,3) inspect(tran)   items    transactionID 1 {item80}        trans1 2 {item8,    item20}        trans2 3 {item28}        trans3 I want to get the 'transpose' of the data, i.e.   transactionID  items 1 {trans2}        item8 2 {trans2}        item20 3 {trans3}        item28 4 {trans1}        item80 I tried converting tran into a matrix, then transpose it...

I have a new problem: Register RBP is used in a function foo. (I am not allocating RBP to any virtual register, the instances of RBP in function foo are in the machine code when my register allocator starts.) Function foo calls function bar. Register RBP is not saved across the call, though it is live after the call. Function bar includes a virtual register. The code that I'm using to

...t defs1_offset, prolog_offset, action_offset, action_index; extern int onestate[500], onesym[500]; extern int onenext[500], onedef[500], onesp; extern int current_mns, current_max_rules; extern int num_rules, num_eof_rules, default_rule, lastnfa; extern int *firstst, *lastst, *finalst, *transchar, *trans1, *trans2; extern int *accptnum, *assoc_rule, *state_type; extern int *rule_type, *rule_linenum, *rule_useful; extern int current_state_type; extern int variable_trailing_context_rules; extern int numtemps, numprots, protprev[50], protnext[50], prottbl[50]; extern int protcomst[50], firstprot, lastp...

Hello, I have a multi-year dataset (see below) with date, a data value and a flag for the data value. I want to find the monthly median for each month in this dataset and then plot it. If anyone has suggestions they would be greatly apperciated. It should be noted that there are some dates with no values and they should be removed. Thanks Emily > print ( str(data$flow$daily) )

...096 9365.30 9341.82 99% 333.40 211.97 63% 8 4096 9397.64 8472.35 90% 312.21 182.51 58% 1 16384 8843.82 8725.50 98% 315.06 312.29 99% 2 16384 7978.70 9176.16 115% 280.34 317.62 113% 4 16384 8906.91 9251.15 103% 312.85 226.13 72% 8 16384 9385.36 9401.39 100% 341.53 207.53 60% - TCP_RR sessions size trans1 trans2 norm1 norm2 50 1 63021.83 93003.57 147% 2360.36 1972.08 83% 100 1 54599.87 93724.43 171% 2042.64 2119.50 103% 250 1 86546.75 119738.71 138% 2414.80 2508.14 103% 50 64 59785.19 97761.24 163% 2264.59 2136.85 94% 100 64 56028.20 103233.18 184% 2063.65 2260.91 109% 250 64 99116.82 113948.86 11...

...096 9365.30 9341.82 99% 333.40 211.97 63% 8 4096 9397.64 8472.35 90% 312.21 182.51 58% 1 16384 8843.82 8725.50 98% 315.06 312.29 99% 2 16384 7978.70 9176.16 115% 280.34 317.62 113% 4 16384 8906.91 9251.15 103% 312.85 226.13 72% 8 16384 9385.36 9401.39 100% 341.53 207.53 60% - TCP_RR sessions size trans1 trans2 norm1 norm2 50 1 63021.83 93003.57 147% 2360.36 1972.08 83% 100 1 54599.87 93724.43 171% 2042.64 2119.50 103% 250 1 86546.75 119738.71 138% 2414.80 2508.14 103% 50 64 59785.19 97761.24 163% 2264.59 2136.85 94% 100 64 56028.20 103233.18 184% 2063.65 2260.91 109% 250 64 99116.82 113948.86 11...