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Dear all, I've used the 'prcomp' command to calculate the eigenvalues and eigenvectors of a matrix(gg). Using the command 'principal' from the 'psych' package  I've performed the same exercise. I got the same eigenvalues but different eigenvectors. Is there any reason for that difference? Below are the steps I've followed: 1. PRCOMP #defining the matrix

Dear R-helpers, First I apologize if my question is quite simple I have a large datasets which more 100 variables. For a research I need to change all name of variables with add one or more letters on each variables. For example, > data(Pima.tr) > Pima.tr[1:5,] npreg glu bp skin bmi ped age type 1 5 86 68 28 30.2 0.364 24 No 2 7 195 70 33 25.1 0.163 55 Yes 3 5

Hola! I will want to identify pixels in an image with the mouse, for so getting the image data from the matrix(es), for use in subsequent discriminant analysis. But the following bombs R: (windows XP, rw1071) > str(baboon) list() - attr(*, "size")= int [1:2] 512 512 - attr(*, "cellres")= num [1:2] 1 1 - attr(*, "bbox")= num [1:4] 0 0 512 512 - attr(*,

I currently work on a draft of an aquatic bioassessment. The conditions tested are the following: ER river water T dechlorinated water control 0.5 + 0.5mg / L of malate T + 1 dechlorinated water control + 1g / L of malate T ED dechlorinated water control SED + ER + river water sediment SED ED + sediment + water dechlorinated. It is the result of AChE in muscle (fillet of fish). The production of

Hi All, I have conducted the following survival analysis which appears to be OK (thanks BRipley for solving my earlier problem). > surv.mod1 <- survreg( Surv(timep1, relall6)~randgrpc, data=Dataset, dist="weibull", scale = 1) > summary(surv.mod1) Call: survreg(formula = Surv(timep1, relall6) ~ randgrpc, data = Dataset, dist = "weibull", scale = 1)

Hello dear all, I am unable to understand why when I run the following three lines: set.seed(4254) > a <- data.frame(y = rnorm(40), x=ordered(sample(1:5, 40, T))) > summary(lm(y ~ x, a)) The output I get includes factor levels which are not relevant to what I am actually using: Call: > lm(formula = y ~ x, data = a) > Residuals: > Min 1Q Median 3Q Max >

Dear Colleague I am trying to figure out how to use R to do OLS restricted VECM regression. However, there are some notation I cannot understand. Please tell me what is 'ect', 'sd' and 'LRM.dl1 in the following practice: #OLS retricted VECM regression data(denmark) sjd <- denmark[, c("LRM", "LRY", "IBO", "IDE")] sjd.vecm<-

I am teaching a DTrace class and a student noticed that 2 iosnoop scripts run in two different windows were producing different results. I was not able to answer why this is. Can anyone explain this. Here are the reults from the two windows: # io.d ... sched 0 <none> 1024 dad1 W 0.156 bash 1998

Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

> -----Original Message----- > From: William Dunlap > Sent: Tuesday, November 25, 2008 9:16 AM > To: 'johannes_graumann at web.de' > Subject: Re: [R] Efficient passing through big data.frame and > modifying select fields > > > Johannes Graumann johannes_graumann at web.de > > Tue Nov 25 15:16:01 CET 2008 > > > > Hi all, > > > >

Dear R gurus I am running a GLMM that looks at whether chimpanzees spend time in shade more than sun (response variable 'y': used cbind() on counts in the sun and shade) based on the time of day (Time) and the availability of shade (Tertile). I've included some random factors too which are the chimpanzee in question (Individual) and where they are in a given area (Zone). There are

Hello All, I have a data file in a text format and there are two data sets. The data set are continuous. For each data set there is a header which has the number of data rows and the name of data series. For example first data set has "6240 Terry Cove-Model". Then the data for that series follows upto 6240 rows. Then another data would start and it will have the header such as

Dear R-listers, I have a very strange problem. I made a package (under Windows and Linux). The package passed the R CMD Check without problem. Then, I installed the package and executed a function which calls to a 'dll' mod<-frailtyPenal(Surv(time,status)~sex+age+cluster(id), + n.knots=8,kappa1=10000,data=kidney) mod Call: frailtyPenal(formula = Surv(time, status)

Hello, could anyone explain what am I doing wrong. When I use colldiag function from package perturb I get different Variance Decomposition Proportions matrix in R than in SAS, although the eigenvalues and indexes are the same. Thanks for your attention. Results: in R: eigen(cor(indep2)) $values [1] 4.197131e+00 6.674837e-01 9.462858e-02 4.070314e-02 5.323022e-05 colldiag(indep2,c=T)

Full_Name: Roger Bivand Version: 1.7.1 OS: i686-pc-linux-gnu Submission from: (NULL) (129.177.30.18) identify() seg.faults when x is an empty list, when the n argument is given a positive value, avoiding the check for non-positive n (n <- length(x) when x is the non-existent x component of the empty list, and when neither x nor y are components of the list. suggested resolution: add test in

Hi I have a question (which may be an obvious one). It is about an idiom which I have seen quite often: o <- order(x); <- x[o] vs. the alternative x <- sort(x) I am just wondering as to the rationale behind the order/reindex idiom vs sorting. Especially as there seems to be a marked performance difference (especially for integer vectors): > x <- trunc(runif(1E6, 1, 100)) >

I am trying to get a random matrix based on an original matrix called disperser.mx, with dimensions 30x73 When I write the following code: >scramble = sample (disperser.mx) >newmat = matrix(scramble, nrow=30) I get the following warning message and a very weird matrix with 30 rows but only 3 columns shown below: Warning message: data length [73] is not a sub-multiple or multiple of the

Further pursuing my curiosity to measure the efficiency of R/C++ interface, I conducted a simple matrix-vector multiplication test using .C and .Call functions in R. In each case, I measured the execution time in R, as well as inside the C++ function. Subtracting the two, I came up with a measure of overhead associated with each call. I assume that this overhead would be non-existent of the entire

Hi there, I am doing multiple comparisons for data that is not normally distributed. For this purpose I tried both functions kruskal{agricolae} and kruskalmc{pgirmess}. It confuses me that these functions do not yield the same results although they are doing the same thing, don't they? Can anyone tell my why this happens and which function I can trust? kruskalmc() tells me that there are no

Dear all, I am attempting to explain patterns of arthropod family richness (count data) using a regression model. It seems to be able to do a pretty good job as an explanatory model (i.e. demonstrating relationships between dependent and independent variables), but it has systematic problems as a predictive model: It is biased high at low observed values of family richness and biased low at