similar to: LM with summation function

I would like to code the following in R: a1(b1+b2+b3) + a2(b1+b3+b4) + a3(b1+b2+b4) + a4(b1+b2+b3) or in summation notation: sum_{i=1, j\neq i}^{4} a_i * b_i I realise this is the same as: sum_{i=1, j=1}^{4} a_i * b_i - sum_{i=j} a_i * b_i would appreciate some help. Thank you. -- View this message in context: http://r.789695.n4.nabble.com/summation-coding-tp4646678.html Sent from the R

Dear R users: I have some difficulties analizing data with mixed effects NLME and the last version of R. More concretely, I have a repeated measures design with a single group and 2 experimental factors (say A and B) and my interest is to compare additive and nonadditive models. suj rv A B 1 s1 4 a1 b1 2 s1 5 a1 b2 3 s1 7 a1 b3 4 s1 1 a2

Hi. I tried the following: F<-function(x,y,b1,b2,b3) b1/(1+b2*(x+b3*y)); t<-data.frame(ExM=c(T$ExM,F(T$S1,T$S2,0.2,0.2,51.28205)),S1=rep(T$S1,2),S2=rep(T$S2,2),gr=c(rep(1,length(T$ExM)),rep(2,length(T$ExM)))); library(lattice); wireframe(ExM ~ S1 * S2, data = t, groups=gr); where T is da dataframe like T<-data.frame(S1=...,S2=...,ExM=...) But the resulting response

Hello, I am trying to learn how to perform Multiple Regression Analysis in R. I decided to take a simple example given in this PDF: http://www.utdallas.edu/~herve/abdi-prc-pretty.pdf I created a small CSV called, students.csv that contains the following data: s1 14 4 1 s2 23 4 2 s3 30 7 2 s4 50 7 4 s5 39 10 3 s6 67 10 6 Col headers: Student id, Memory span(Y), age(X1), speech rate(X2) Now

Hi I have a dataframe, RQ, like this: A B1 B2 B3 1 NA 112 12 2 NA 123 123 3 NA 324 13 4 3 21 535 5 4 12 33 6 7 1 335 7 4 NA 3535 8 4 NA NA 9 NA NA NA 10 5 NA NA 12 4 NA NA 15 2 NA NA 17 3 NA 1 63 1 NA 1 75 NA NA NA 100 NA NA NA 123 NA NA

Hello R users, I've used the following help two compare two regression line slopes. Wanted to test if they differ significantly: Hi, I've made a research about how to compare two regression line slopes (of y versus x for 2 groups, "group" being a factor ) using R. I knew the method based on the following statement : t = (b1 - b2) / sb1,b2 where b1 and b2 are the two slope

Hello R guru's I am a newbie to R, In my research work I usually generate a lot of ELISA data in form of absorbance values. I ususally use Excel to calculate the concentrations of unknown, but it is too tedious and manual especially when I have 100's of files to process. I would appreciate some help in creating a R script to do this with minimal manual input. s A1-G1 and A2-G2 are

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

#I have a data set that looks like this. A bit more complicated actually with # three factor levels but these calculations need to be done on one factor at a #I then have a set of different rates that are applied #to it. #dataset cata <- c( 1,1,6,1,1,2) catb <- c( 1,2,3,4,5,6) doga <- c(3,5,3,6,4, 0) data1 <- data.frame(cata, catb, doga) rm(cata,catb,doga) data1 # start rates #

(sorry if this is a duplicate-problems with posting at my end) ---- Hello all, I need some help with a nice R-idiomatic and efficient solution to a small problem. Essentially, I am trying to eliminate randomly half of the entries in a vector that contains index values into some other vectors. More details: I am working with two strings/vectors of 0s and 1s. These will contain about 200

If I have matrics as follows: > a <- c(1,1,0,0) > b <- c(4,4,0,0) > c <- c(3,5,5,6) How can I use R code to solve the equation ax^2+bx+c=0. thanks! yuying shi [[alternative HTML version deleted]]

Dear R experts I need to write a function that incorporates double summation, the problem being that the upper limit of the second summation is the index of the first summation, i.e: sum_{j=0}^{x} sum_{i=0}^{j} choose(i+j, i) where x variable or constant, doesn't matter. The following code obviously doesn't work: f=function(x) {j=0:x; i=0:j; sum( choose(i+j,i) ) } Can you help? Thanks

I am attempting to optimize a regression model's parameters to meet a specific target for the sum of positive errors over sum of the dependent variable (minErr below). I see two courses of action , 1) estimate a linear model then iteratively reduce the regressors to achieve the desired positive error threshold (naturally the regressors and predicted values are biased - but this is

Suppose I have x variables x1, x2, x3 (however in general I don't know how many x variables there are). I can do X<-cbind(x1,x2,x3) lm(y ~ X) This fits the no-interaction model with b0, b1, b2, b3. How can I get lm() to fit the model that includes interactions when I pass X to lm()? For my example, lm(y~x1*x2*x3) I am looking for something along the lines of lm(y~X ...) where ... is some

Hi I searched in the list and only found questions without answers e.g. http://finzi.psych.upenn.edu/R/Rhelp02a/archive/19955.html : Is there a way to get the same results with lme as with aov with Error()? Can anybody reproduce the following results with lme: id<-c(1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5)

Hello, I am new to R and I am trying to estimate a discrete model with three choices. I am stuck at a point and cannot find a solution. I have probability functions for occurrence of these choices, and then I build the likelihood functions associated to these choices and finally I build the general log-likelihood function. There are four parameters in the model, three of them are associated to

Hello, I have a vector {a, b1, b2, b3, b4}. How can I calculate the following matrix: var(a) cov(a, b1) cov(a, b2) cov(a, b3) cov(a, b4) cov(a, b1) var(b1) cov(a, b2) cov(a, b3) cov(a, b4) ... ... cov(a, b1) cov(a, b2) cov(a, b3) cov(a, b4) var(b4) I would very appreciate your inputs. Thank you very much. Sincerely, Jialin Huang [[alternative HTML version deleted]]

Hi, I have a three dimensional array, e.g., my.array = array(0, dim=c(2,3,4), dimnames=list( d1=c("A1","A2"), d2=c("B1","B2","B3"), d3=c("C1","C2","C3","C4")) ) what I would like to get is then a dataframe: d1 d2 d3 value A1 B1 C1 0 A2 B1 C1 0 . . . A2 B3 C4 0 I'm sure there is one function to do

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

Dear R users... I made the R-code for this double summation computation http://www.nabble.com/file/p19213599/doublesum.jpg ------------------------------------------------- Here is my code.. sum(sapply(1:m, function(k){sum(sapply(1:m, function(j){x[k]*x[j]*dnorm((mu[j]+mu[k])/sqrt(sig[k]+sig[j]))/sqrt(sig[k]+sig[j])}))})) ------------------------------------------------- In fact, this is