search for: ifault

...rng <- x[n] - x[1L] if (rng == 0) stop("all 'x' values are identical") if (rng < 1e-10) x <- x/rng n2 <- n%/%2L sw <- .C(R_swilk, init = FALSE, as.single(x), n, n1 = n, n2, a = single(n2), w = double(1), pw = double(1), ifault = integer(1L)) if (sw$ifault && sw$ifault != 7) stop(gettextf("ifault=%d. This should not happen", sw$ifault), domain = NA) RVAL <- list(statistic = c(W = sw$w), p.value = sw$pw, method = "Shapiro-Wilk normality test", data.name...

Dear McLeod, I am a PhD student working with the Kendall package in R. I used this package to make the so-called TauKr matrix correlation analysis (Hemelrijk, 1990). However, lately I've been getting this warning: Error exit, tauk2. IFAULT = 12. From the forums I've been looking into, I noticed that when this warning pops up, it means that the p-value of the correlation may be wrong or not so accurate, is this correct? If this right, I would have nothing to worry about since the TauKr matrix correlation analysis uses the permutat...

...ng == 0) stop("all `x[]' are identical") if (rng < 1e-10) x <- x/rng n2 <- n%/%2 sw <- .C("swilk", init = FALSE, as.single(x), n, n1 = as.integer(n), as.integer(n2), a = single(n2), w = double(1), pw = double(1), ifault = integer(1), PACKAGE = "ctest") if (sw$ifault && sw$ifault != 7) stop(paste("ifault=", sw$ifault, ". This should not happen")) RVAL <- list(statistic = c(W = sw$w), p.value = sw$pw, method = "Shapiro-Wilk normality test",...

> library(Kendall) > Kendall(1:3,1:3) WARNING: Error exit, tauk2. IFAULT = 12 <<<<<< tau = 1, 2-sided pvalue =1 I believe Kendall tau is well-defined for this case and the reported value is correct; isn't it a bug to give a warning? (And if, e.g., the pvalue is not well-defined in this case, wouldn't it be better to return NA or NaN...

...55,0.7,0.33,0.48,0.6,0.8)) > W<-array(rep(1.0,length(X))) > X<-matrix(X,nrow=4,ncol=4) > X<-t(X) > W<-matrix(W,nrow=4,ncol=4) > A<-array(0,dim=c(NROW,NCOL,4)) > B<-array(0,dim=c(NDIM,5)) > NCYCLE<-5 > ICYCLE<-5 > G<-(X*0)+0.0 > EPS=0.001 > IFAULT<-5 # Here is how the subroutine says to input the parameters; #SUBROUTINE SMOOTH(NROW, NCOL, NDIM, X, W, A, B, NCYCLE, ICYCLE, G, EPS, IFAULT) # Definitions of each of these input parameters are below; #REAL X(NROW,NCOL), W(NROW,NCOL), A(NROW,NCOL,4), B(NDIM,5), G(NROW,NCOL), EPS, ZERO, DELTA...

...ECISION". Compiling went o.k.: > g77 -fpic -O2 -shared -o rtest.o fisher.f ("g77 --version" gives 2.7.2.1) Here are the first lines of the code from AS 304, "FISHER" being the routine to be used from within R: SUBROUTINE FISHER (X, M, Y, N, TOTAL, POSSIB, P, IFAULT) C C ALGORITHM AS 304.1 APPL.STATIST. (1996), VOL.45, NO.3 C C Fisher's non-parametric randomization test for two small C independent random samples C INTEGER M, N, TOTAL, POSSIB, IFAULT DOUBLE PRECISION X(*), Y(*), P C ... etc., lots of code ... Here is th...

...data was probably causing the problem so I applied the following transformation: powerTransform(1e7*(data - 0.99*(min(data)))) [1] And it worked! However, now when I try to check the normality of my data, shapiro.test(data) I get a new warning message: Error in shapiro.test(data) : ifault=6. This should not happen I think this may be solved by removing the transformation I applied previously in [1], but I’m not sure how to do it properly. Could anyone give me some advice? MY DATA: c(0.000103113, 0.000102948, 0.000104001, 0.000103794, 0.000104628, 9.2765e-05, 9.4296e-05, 9.5025e-...

...data was probably causing the problem so I applied the following transformation: powerTransform(1e7*(data - 0.99*(min(data)))) [1] And it worked! However, now when I try to check the normality of my data, shapiro.test(data) I get a new warning message: Error in shapiro.test(data) : ifault=6. This should not happen I think this may be solved by removing the transformation I applied previously in [1], but I’m not sure how to do it properly. Could anyone give me some advice? MY DATA: c(0.000103113, 0.000102948, 0.000104001, 0.000103794, 0.000104628, 9.2765e-05, 9.4296e-05, 9.5025e-...

...as.integer(ncol(x)), centers = as.double(centers), + as.integer(k), c1 = integer(m), integer(m), nc = integer(k), + double(k), double(k), integer(k), double(m), + integer(k), integer(k), as.integer(iter.max), + wss = double(k), ifault = 0L) + switch(Z$ifault, stop("empty cluster: try a better set of initial centers", + call. = FALSE), warning(gettextf("did not converge in %d iterations", + iter.max), call. = FALSE, domain = NA), stop("number of cluster centres...

...SION". Compiling went o.k. ... > g77 -fpic -O2 -shared -o rtest.o fisher.f ("g77 --version" gives 2.7.2.1) Here are the first lines of the code from AS 304, "FISHER" being the routine to be used from within R: SUBROUTINE FISHER (X, M, Y, N, TOTAL, POSSIB, P, IFAULT) C C ALGORITHM AS 304.1 APPL.STATIST. (1996), VOL.45, NO.3 C C Fisher's non-parametric randomization test for two small C independent random samples C INTEGER M, N, TOTAL, POSSIB, IFAULT DOUBLE PRECISION X(*), Y(*), P C ... etc., lots of code ... Here is th...

Sir, Would like to know what sort of input matrix are taken by the kmeans function defined in mva library of R application. As per the documentation for the Kmeans it takes the following 2 data sets: 1) data 2) centers The commands to be executed in R are as follows: library(mva) data <- read.table('file1',header=TRUE,sep="\t") centers <-

...lin( tab, list(1,2,3,4,5) )[[1]] # AOK 2 iterations: deviation 5.456968e-12 [1] 10909.89 > loglin( tab, list(1,2,3,4,5), c(1,2,3) )[[1]] # OUCH! Process R bus error at Wed Jul 21 17:03:55 2004 this is inconsistent - sometimes issuing this msg several times before barfing: Error in switch(z$ifault, stop("This should not happen"), stop("This should not happen"), : Incorrect specification of 'table' or 'start' appl/loglin.c assumes that start (aka 'fit') has enough memory allocated. Possible fix in stats/R/loglin.R : ntab <- length(ta...

Hello everyone, I'm trying to perform some trend analysis using the package "Kendall" and for some observations I get the following error "WARNING Error exit, tauk2, IFAULT = 12" Could anyone tell me how to create a logical vector of observations having its element equal to TRUE if this error happened and FALSE if not? I'm thinking about something similar as "inherits" implemented in the "nls" function which determines the occurrence of th...

I am using R 1.3.0 on Windows 2000. For an experiment, I am wanting to find the most diverse 400 items to study in a possible 3200 items. Diversity here is based on a few hundred attributes. For this, I would like to do a clustering analysis and find 400 clusters (i.e. different from each other in some way hopefully). From each of these 400 clusters, I will pick a representative. I expect