similar to: creating names for regressios using the assign()

Hi there, > Value=c(709,679,699,657,594,677,592,538,476,508,505,539) > Lard=rep(c("Fresh","Rancid"),each=6) > Gender=rep(c("Male","Male","Male","Female","Female","Female"),2) > Food=data.frame(Value,Lard,Gender) > Food Value Lard Gender 1 709 Fresh Male 2 679 Fresh Male 3 699 Fresh

Dear all, I have the following problem. Given an expression object 'expr' containing a certain set of symbols (say 'a', 'b', 'c'), I would like to translate the expression object in an R function of, say, 'a', programmatically. Here an example of what I mean. Given: > expr <- expression(a+b+c) a call like: > asFunctionOf(expr, 'a',

Hi I have (8 measures * 96 groups) = 768 datasets for which I did linear regressions using lm(). Now I want to compare the slopes for each of the 8 measures in each of the 96 groups. As I understand , I can not use > anova(lm1, ..., lm8) as the lm1 ... lm8 are based on different datasets. I also read in previous discussions in this list, that I can see if the slope +- stddev(slope)

Hi there, From the vector X of integers, X = c(11999, 122000, 81997) I would like to make these two vectors: Z= c(1999, 2000, 1997) Y =c(1 , 12 , 8) That is, each entry of vector Z receives the four last digits of each entry of X, and Y receives "the rest". Any suggestions? Thanks in advance, Dimitri [[alternative HTML version deleted]]

Hello, and sorry for asking a question without the data - hope it can still be answered: I've run two things on the same data: # Using lme: mix.lme <- lme(DV ~a+b+c+d+e+f+h+i, random = random = ~ e+f+h+i| group, data = mydata) # Using lmer mix.lmer <- lmer(DV ~a+b+c+d+(1|group)+(e|group)+(f|group)+(h|group)+(i|group), data = mydata) lme provided an output (fixed effects and random

Dear all, I have a list with three (named) numeric vectors: > lst = list(a=c(A=1,B=8) , b=c(A=2,B=3,C=0), c=c(B=2,D=0) ) > lst $a A B 1 8 $b A B C 2 3 0 $c B D 2 0 Now, I'd love to use this list to create the following data frame: > dtf = data.frame(a=c(A=1,B=8,C=NA,D=NA), + b=c(A=2,B=3,C=0,D=NA), + c=c(A=NA,B=2,C=NA,D=0) ) > dtf a b

Dear All, I have written the following programs to find a non-singular (10*10) covariance matrix. Here is the program: nitems <- 10 x <- array(rnorm(5*nitems,3,3), c(5,nitems)) sigma <- t(x)%*%x inverse <- try(solve(sigma), TRUE) while(inherits(inverse, "try-error")) { x <- array(rnorm(5*nitems,3,3), c(5,nitems)) sigma <- t(x)%*%x inverse <-

Hi all, I'm fitting a linear model (using lm) to some 2500 data points. The model consists of 4 single terms and two combined terms. I get the following warning message: "Extra arguments projections are just disregarded. in: lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) " Can anybody clarify this ? I don't seem to find any pointer to what this might

Hi all, I was using the Newton-Raphson method to estimate paremeters in the model developed by my supervisor. However, when I interatively computed theta(t+1)=theta(t) - solve(H)*s (where the Hessian matrix and score vector were explicitely derived), I got the error message: Error in solve.default(H) : system is computationally singular: reciprocal condition number = 1.70568e-032. Assume my score

Dear all, A few months ago, I asked for your help on the following problem: I have a list with three (named) numeric vectors: > lst = list(a=c(A=1,B=8) , b=c(A=2,B=3,C=0), c=c(B=2,D=0) ) > lst $a A B 1 8 $b A B C 2 3 0 $c B D 2 0 Now, I'd love to use this list to create the following data frame: > dtf = data.frame(a=c(A=1,B=8,C=NA,D=NA), +

Hi all. A statistical question. I have to estimate the variance of the sum: X(1) + X(2) + ...+ X(n) from an observed sample, where X(i) are *correlated* and not necessarly identically distributed. Someone can suggest a simple strategy (I hope by exploiting some already present R package) for obtaining such estimate from an observed vector X[1:n]? Tnx all, Antonio, Fabio Di Narzo.

Dear all, This is a pure statistical question, not necessarly related to R. I could not find it in literature. Suppose I'm intersted in a parameter rho, say, equal to: r=beta1/beta2, where beta1 and beta2 come from a linear model y=beta0+beta1X1+beta2X2+.... Fitting the model I can get the (biased) estimate of r=b1/b2, where b1 and b2 are the estimates in the regression model; I can get the

Attached is a patch to fix a GLIBCXX_DEBUG error in ScheduleDAGRRList. The problem is that calls to CapturePred may reprioritize elements in the priority queue, violating streak weak ordering requirements. To fix this, I introduced a reference wrapper for containers to obtain access to the SUnitVec used by std::priority_queue. When CapturePred runs, it calls updateNode which does a

Hi, Suppose I I wish to run lm( y ~ x + z + log(w) ) where w assumes non-negative values. A problem arises when w=0, as log(0) = -Inf, and R doesn't accept that (as it "accepts" NA). Is there a way to tell R to do with -Inf the same it does with NA, i.e, to ignore it? ( Otherwise I have to do something like w[w==0] <- NA which doesn't hurt, but might be a bit

Hi everyone I am using box.tidwell to transform the explanatory variables. However, it appears problematic. The warning I received as follows. box.tidwell(Newresponse ~ FAC2_1 + FAC4_1 + FAC5_1 + FAC6_1 + FAC7_1 + KXI + RECODINC) Warning in log(x) : NaNs produced Warning in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : extra arguments na.rm are just disregarded.

Quantreggers: I'm trying to run rq() on a dataset I posted at: https://docs.google.com/file/d/0B8Kij67bij_ASUpfcmJ4LTFEUUk/edit?usp=sharing (it's a 1500kb csv file named "singular.csv") and am getting the following error: mydata <- read.csv("singular.csv") fit_spl <- rq(raw_data[,1] ~ bs(raw_data[,i],df=15),tau=1) > Error in rq.fit.br(x, y, tau = tau, ...) :

Hello, my data is sorted by start.ens (see below). And now I would like to extract all rows (so called* defined row*s) with type==Expression - subset (df, type==Expression) - and the aforegoing type==DNase HS (which is not necessarly row n-1 - assumung that the defined row is n). I dont know how to add this to my subset command. Is that possible? Thanks Hermann > df start.ens fc.trans

I want to do a lm regression, some of the variables are going to be affected with log, I would like not no take into account the values which imply doing log(0) for just one variable I have done the following but it doesn't work: lmod1.lm <- lm(log(dat$inaltu)~log(dat$indiam),subset=(!(dat$indiam %in% c(0,1))) and obtain: Error en lm.fit(x, y, offset = offset, singular.ok =

Hello, Currently, determinant(A) calculates the determinant of 'A' by factorizing A=LU and computing prod(diag(U)) [or the logarithm of the absolute value]. The factorization is done by LAPACK routine DGETRF, which gives a status code INFO, documented [1] as follows: *> INFO is INTEGER *> = 0: successful exit *> < 0: if INFO = -i, the i-th

hey guys I have a panel data set that i want to perform some regressions on. I am using the /plm/ package. I defined a model in the following way: PWBw.pool <- plm(*PWB* ~ log(*I_EQON*) + log(*RD*) + ... + *PAGRI*, data = pfem, na.action=na.exclude, model="pooling") When i run this it gives the following error (the error remains when i use other model = "" specifications