similar to: Mardia's multivariate normality test

Dear all, I have two vectors and connect the points by line segments: x <- c(1990,1994,1995,1997) y <- c(30,40,80,20) plot(x,y,type="l") I want to get the values of y's on these lines at missing x's, i.e., at 1991,1992,1993,and 1996. Is there a simple way to do this? Best regards, Remigijus mailto:remigijus.lapinskas at maf.vu.lt

Hello R-help, My vector aa has some NA's: > aa_c(NA,1) > aa=="NA" [1] NA FALSE > aa==NA [1] NA NA How can I detect NA's? In other words, I would like my answer to be TRUE FALSE. Best regards, Remigijus mailto:remigijus.lapinskas at maf.vu.lt -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help

Dear All, There is an example for the priceIts function (the its package) which does not work for me as expected. > ?priceIts > x1 <- priceIts(instrument = c("^ftse"), start = "1998-01-01", + quote = "Close") Error in validObject(.Object) : invalid class "its" object: Missing values in dates > x2 <-

Dear R users, suppose I have a matrix of observations for which I calculate all pair-wise correlations: m=matrix(sample(1:100,replace=T),10,10) w=cor(m,use="pairwise.complete.obs") How do I extract only those correlations that are >0.6? w[w>0.6] #obviously doesn?t work, and I can?t find a way around it. I would very much appreciate any help! Best wishes Christoph (using R

Dear all, I have a function MYFUN which depends on 3 positive parameters TETA[1], TETA[2], and TETA[3]; x belongs to [0,1]. I integrate the function over [0,0.1], [0.1,0.2] and [0.2,0.3] and want to choose the three parameters so that these three integrals are as close to, resp., 2300, 4600 and 5800 as possible. As I have three equations with three unknowns, I expect the exact fit, i.e., the SS

Dear all, How can I replace text in objects that are of class "formula"? y="a * x + b" class(y)="formula" grep("x",y) y[1] Suppose I would like to replace the "x" by "w" in the formula object "y". How can this be done? Somehow, the methods that can be used in character objects do not work 1:1 in formula objects... Many

Dear R users, Let?s say I have 10 models, each named m1,m2,m3..., and I would like to summarize them automatically and simultaneously - e.g., to extract parameter estimates later on from all models; how can I do that? I have tried: x=1:10 #this creates some example data y=rnorm(10) m1=lm(x~y) m2=lm(x~1) sum.lms=function(x)summary(paste("m",x,sep="")) sum.lms(1:2) but

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

The last two times I have originated message threads on R or Bioconductor I have received the message included below from someone named Patrick Connolly. Both times I was the originator of the message thread and used what I thought was a unique subject line that explained as best I could what my question was. Patrick seems to be implying that I am abusing the R and BioC help newsgroups in this

Dear List, There has just been this question (as well as the answer to it) in the S list: > I would like to put some text in plots... but in french. How can I > write accented letters? > I suppose that there is a possibility similar as the one which > works in LaTeX, isn't it? What about R (for Windows and in East European languages)? Specifically, how can I give a title like

Is it possible to fit a lagged regression, "y[t]=b0+b1*x[t-1]+e", using the function "lag"? If so, how? If not, of what use is the function "lag"? I get the same answer from y~x as y~lag(x), whether using lm or arima. I found it using y~c(NA, x[-length(x)])). Consider the following: > set.seed(1) > x <- rep(c(rep(0, 4), 9), len=9) > y <-

Dear all, Suppose I have a nested, repeated measure lme model. Which of the following formulae is correct? (assuming data are sampled from several plots in an agricultural experiment) (1) y~explanatory.variables,random=~time|block/plot/subplot/individual (2) y~explanatory.variables,random=~time|unique.ID.of.every.individual I have read that (2) is the only approach that works. But how could I

Dear All, R 1.9.1, Windows When copying and pasting a few lines from the 'predict.Arima' help, I get an error message: > data(lh) > predict(arima(lh, order = c(3,0,0)), n.ahead = 12) Error in eval(expr, envir, enclos) : Object "xreg" not found On the other hand, the following is OK: > data(lh) > predict(arima0(lh, order = c(3,0,0)), n.ahead = 12) $pred Time

Dear R users, I am analyzing several response variables (all scaled to [0;1]) using a multivariate linear model. After fitting the model, I set up a hypothesis matrix to test specific contrasts for these response variables; for example: "a always increases significantly more than b when regressed against x". What I am stuck with now is how to calculate the correct F-values (and

Hello, We are attempting to use nlme to fit a linear mixed model to explain bird abundance as a function of habitat: lme(abundance~habitat-1,data=data,method="ML",random=~1|sampleunit) The data consist of repeated counts of birds in sample units across multiple years, and we have two questions: 1) Is it necessary (and, if so, how) to specify the repeated measure (years)? As written,

I have data that looks like YC Age Num 82 11 2 83 10 0 84 9 8 85 8 21 86 7 49 87 6 18 88 5 79 89 4 28 90 3 273 91 2 175 with a program mod1=lm(log(Num+1)~YC, data=box44) plot(log(Num+1)~YC, data=box44, pch=19, xlab="Year Class", ylab="Loge Number at age", ylim=c(0,6), xlim=c(91,82)) abline(lm(log(Num+1)~YC), col="blue", lwd=2) summary(mod1) I need to

In the R code of Mardia's test, what does the line " x1 = x[x[, p] == i, -p]" mean? Thanks a lot! function (x) { p = dim(x)[2] f = p - 1 clases = length(table(x[, p])) for (i in 1:clases) { x1 = x[x[, p] == i, -p] ndat = dim(x1)[1] mo3 = mo3(x1) mard1 = ndat * mo3/6 cat("Mardia's test for class", i,

Hi: I'm clustering a microarray dataset with a large # of samples. I would like your opinion on the best way to automatically determine the optimal # of clusters. Currently I am using the "cluster" package, clustering with "clara", examining the average silhouette width at various numbers of clusters. I'd like opinions on whether any newer packages offer

Hi: I'm clustering a microarray dataset with a large # of samples. I would like your opinion on the best way to automatically determine the optimal # of clusters. Currently I am using the "cluster" package, clustering with "clara", examining the average silhouette width at various numbers of clusters. I'd like opinions on whether any newer packages offer

Dear all, I have a data frame that looks like this: c1 c2 c3 A B C B C A A A B and so on; I?d like to produce one single vector consisting of the columns c1,c2, c3, such that vector=("A","B","A","B","C","A","C","A","B") I guess it?s easy to do but I don?t know how...Can anyone