similar to: has_and_belongs_to_many

Hello, I would be very grateful if someone could give me a hand with my split plot design problems. So here is my design : I am studying the crossed-effects of water (wet/dry) and mowing (mowed/not-mowed = nm) on plant height (PH) within 2 types of plant communities (Xerobromion and Mesobromion) : - Within each type of communities, I have localised 4 blocks - In each block, I have defined

We have a wireless network that uses 802.1x authentication, in which domain joined computers use their machine credentials to connect. Windows machines do this automatically, and until recently Linux computers could join using wicd, wpa-supplicant, and a simple script that would retrieve the machine password with tdbdump. ( specifically tdbdump -k SECRETS/MACHINE_PASSWORD/DOMAIN

i know this is not about R. After applying quantile regression with t=0.5,0.6 on the data set WBC( Wisconsin Breast Cancer)with 678 observations and 9 independent variables(inp1,inp2,...inp9) and 1 dependent variable(op) i have got the following results for beta values. when t=0.5(median regression) beta values b1=0.002641,b2=0.045746,b3=0.

[I am really sorry if it is double posted, I doubt me previous post could not reach forum due to some problem with net] Suppose I have a matrix : a = matrix(1:9, 3) >From this matrix, I construct 9 additional matrices : i = 1:9 bi = a * i Now combining all those 9 new matrices, I construct a final metrix as : c = b1 b4 b7 b2 b5 b8 b3 b6 b8 I want to automate this procedure for any

Dear R experts, I am having difficulty using loops productively and would like to please ask for advice. I have a dataframe of ids and groups. I would like to break down the dataframe into groups, find the unique sets of ids, then reassemble. My thought was to use a loop, but I have been unable to finish this loop in a logical way. I would like to find the unique ids for group 1, group 2,

Hi, I am hoping some one can help with this. I am using nlme to fit a random coefficients model. It ran for hours before returning Error: Singularity in backsolve at level 0, block 1 The model is > plavix.nlme<-nlme(PLX_NRX~loglike(PLX_NRX,PD4_42D,GAT_34D,VIS_42D,MSL_42D,SPE_ROL,XM2_DUM,THX_DUM,b0,b1,b2,b3,b4,b5,b6,b7,alpha), + data=data, + fixed=list(b0 +

Hi, I created the following example of a xyplot. The plot actually looks ok apart from that the x-axis is pretty squeezed/clinched. I tried aspect="fill" already but I want to have all 3 sub plots next to each other. I tried loads of different scaling varieties but with no significant result... library(lattice) N <- 6 d <-

Hi, You might need to check library(boot).? I have never used that before.? So, I can't comment much.? It is better to post on R-help list.? I had seen your postings on Nabble in the past.? Unfortunately those postings were not accepted in R-help.? You have to directly post at ? r-help at r-project.org after registering at: https://stat.ethz.ch/mailman/listinfo/r-help ?

Hello all, I would like to fit a mixed effects model, but my response is of the negative binomial (or overdispersed poisson) family. The only (?) package that looks like it can do this is glmm.ADMB (but it cannot run on Mac OS X - please correct me if I am wrong!) [1] I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do not provide this "family" (i.e. nbinom, or

Hi all, I'm using a certain  procedure to calculate the value of some variable(Bayes risk),B. So I got the values B1, B2, ........, B1000, each under certain input values and using a long procedure. Now, I want to put the values I got in a nummerical vector and find their minimum value. I think c( ) should work.For example if I have only 10 values I could have used

Hi, my data set is data.frame(id, yr, y, l, e, k). I would like to estimate Lee and Schmidts (1993, OUP) model in R. My colleague wrote SAS code as follows: ** procedures for creating dummy variables are omitted ** ** di# and dt# are dummy variables for industry and time ** data a2; merge a1 a2 a; by id yr; proc sysnlin maxit=100 outest=beta2; endogenous y; exogenous l e k

Hi all, I am struggling to work out how to use the rownames from an edited dataframe rather than the row names from the original dataframe. In my data set i'm trying to extract several rows of data on particular individuals, i don't doubt i'm using the long way round but what i have in the way of a script is this: ##selecting the IDs from the dataframe individually

Hi All, I have data on the sequence of births for families with completed fertility cycle (in a data frame); the relevant variables are called b1, b2, b3, b4, b5, b6 and record the birth of the first, second, ..., sixth child. So, b1=1 if the first birth is male, b1=2 if the first birth is female, and b1=NA if the family did not record any first birth. Similarly for b2, b3, b4, b5 and b6. I

Estimados usuarios de R: Tengo dos data frames, A y B, donde hay algunas variables comunes y otras distintas. En el A tengo las variables, a1, a2, a3, a4, a5. En el B tengo las variables a1, a2, a3, b1, b2, b3,b4, b5, b6, b7. Los valores llave son a1 y a2, al data frame B quiero pegarle las variables a4 y a5 y en el caso no se tenga ningun valor en la variable a3, ponerle el valor que aparece

Hi, I'm trying to reshape my data set from wide to tall format for multilevel modeling. Unfortunately, the function I typically use (make.univ from the multilevel package) does not appear to work with unbalanced data frames, which is what I'm dealing with. Below is an example of the columns of a data frame similar to what I'm working with: ID a1 a2 a4 b2 b3 b4 b5 b6 Below

Hello all, I'm trying to replicate a confirmatory factor analysis done in Amos. The idea is to compare a one-factor and a two-factor model. I get the following warning message when I run either model: "Could not compute QR decomposition of Hessian. Optimization probably did not converge." I have no idea what to do here. I believe posters reported the same problem. It seems

Hello all and thanks in advance for any advice. I am very new to R and have searched my question but have not come up with anything quite like what I would like to do. My problem is: I have a data set for individuals (rows) and values for behaviours (columns). I would like to know the proportion of shared behaviours for all possible pairs of individuals. The sum of shared behaviours divided by

Greetings - On a RHEL6 system, Samba 4.2.1 compiles without any apparent problems but 'make quicktest' reports an error: FAILED (1 failures, 0 errors and 0 unexpected successes in 1 testsuites) A summary with detailed information can be found in: ./st/summary test: running (/usr/bin/perl /var/tmp/samba-4.2.1/selftest/selftest.pl --target=samba --prefix=./st

Hi, I have a column (V4) in a midi event list which includes tone pitch names, i.e. "A4, E4, C#4, A3...": > compo[1:10,] V1 V2 V3 V4 V5 V6 V7 1 1 1 0 A4 96 2 0 2 1 1 0 E4 96 2 0 3 1 1 0 C#4 96 2 0 4 1 1 0 A3 96 2 0 5 1 3 0 B4 96 1 0 6 1 3 0 E4 96 1 0 7 1 3 0 B3 96 1 0 8 1 3 0 G#3 96 1 0 9 1 4 0 C#5 96 1 0 10

Hi, Using S=1000 and simdata <- replicate(S, generate(3000)) #If you want both "m1" and "m0" #here the missing values are 0 res1<-sapply(seq_len(ncol(simdata.psm1)),function(i) {x1<-merge(simdata.psm0[,i],simdata.psm1[,i],all=TRUE); x1[is.na(x1)]<-0; x1}) res1[,997:1000] #????? [,1]???????? [,2]???????? [,3]???????? [,4]??????? #x1??? Numeric,3000 Numeric,3000