similar to: formula error inside function

Can anyone help me resolve this? A part of the R function looks like this: print(is.loaded('merge_xtabs_patterns_file')) print(is.loaded('merge_xtabs_patterns_file_')) .Fortran('merge_xtabs_patterns_file_',ydim[1],ydim[2],x=as.integer(as.matrix(y)),na=as.integer(c), maxD=as.integer(maxD),lrowmem=length(rowmem),rowmem=as.integer(rowmem),

K-means recluster data with given cluster centers Dear R user, I have several large data sets. Over time additional new data sets will be created. I want to cluster all the data in a similar/ identical way with the k-means algorithm. With the first data set I will find my cluster centers and save the cluster centers to a file [1]. This first data set is huge, it is guarantied that cluster

The solve function in r-patched has been changed so that it applies a tolerance when using Lapack routines to calculate the inverse of a matrix or to solve a system of linear equations. A tolerance has always been used with the Linpack routines but not with the Lapack routines in versions 1.7.x and 1.8.0. (You can use the optional argument tol = 0 to override this check for computational

Dear list, given this formula: > fmla <- formula(y1 ~ spp1 + spp2 + spp3 + spp5) > fmla[[3]] spp1 + spp2 + spp3 + spp5 is this the intended behaviour of as.character: > as.character(fmla[[3]]) [1] "+" "spp1 + spp2 + spp3" "spp5" ? Where does the extra "+" come from? > as.character(fmla) [1] "~"

Thanks a lot for help in advance. I am switching from matlab to R and I guess I need some time to get rolling. I was wondering why this code : > fit.0 <- lm( Response ~ 1, data = ds3) > step(fit.0,scope=list(upper=~.,lower=~1),data=ds3) Start: AIC= -32.66 Response ~ 1 Call: lm(formula = Response ~ 1, data = ds3) Coefficients: (Intercept) 1.301 is not working

Hello, I am trying to "paste" together a formula to use in the mob function of party. This means the formula will be of the form y ~ x1+ ...+xM | z1+..zN. I am doing some preliminary fits of y ~ x1+ ...+xM, then want to add the conditional part of the equation using update(). Here's the test code: var1 <- 1:78 x1 <- paste("x", var1, sep="") f1 <-

This is the general outline of my code:: main(argc,argv){ ... Rf_initEmbeddedR(argc,argv); ... Test_tryEval("source(test.r)"); ... } ############# # test.r ############# ... dyn.load("toload.so") tmp <-matrix(data=1,nrow=narray*2,ncol=nclust) .Call("Init",tmp,...) while(...) { criteria <-feval(tmp) if (criteria < criteria.min)

Hi! I just started to use R recently, and would like to ask a help about automating the job. I need to use "kmeans" function with my own 300 data files, and wonder if it's possible to do it automatically. For example, > library (mva) > mydata <- read.table ("data1") > cl <- kmeans(mydata, 5, 20) and I just need to save "cl" info (i.e. the center

Hi, I'm trying to compile half precision program for ARM, while it seems LLVM fails to automatically generate fused-multiply-add instructions for c += a * b. I'm wondering whether I did something wrong, if not, is it a missing feature that will be supported later? (I know there're fp16 FMLA intrinsics though) Test programs and outputs, $ clang -O3 -march=armv8.2-a+fp16fml

Hi, I am trying to build a series of rlm models. I have my data frame and the models will be built using various coulmns of the data frame. Thus a series of models would be m1 <- rlm(V1 ~ V2 + V3 + V4, data) m2 <- rlm(V1 ~ V2 + V5 + V7, data) m3 <- rlm(V1 ~ V2 + V8 + V9, data) I would like to automate this. Is it possible to use a string in place of the formula? I tried doing: fmla

Hola, buenas tardes estoy intentando hacer un bootstrap de un modelo, pero me da el siguiente error: "Error in FUN(newX[, i], ...) : unused argument(s) (list(age = c(33, 47, 49, 56, 60, 64, 64, 66, 68, 69, 71, 71, 72, 73, 74, 75, 75, 76, 78, 81, 83, 83, 36, 43, 46, 47, 49, 49, 51, 51, 52, 52, 53, 54, 54, 54, 55, 56, 56, 57, 57, 58, 58, 58, 58, 59, 59, 60, 61, 62, 63, 64, 65, 65, 66, 66,

Greetings. I've written a convenience function for multicollinearity diagnosis. I'd like to report to the user the formula that is used in a regression. I get output like this: > mcDiagnose(m1) [1] "The following auxiliary models are being estimated and returned in a list:" [1] "`x1` ~ ." formula(fmla)() [1] "`x2` ~ ." I'd like to fill in the period

Thanks for reply. I was using LLVM 8.0. Let me try trunk and will let you know if it works. On Wed, Sep 4, 2019 at 11:19 PM Sjoerd Meijer <Sjoerd.Meijer at arm.com> wrote: > > Hi, > Which version of Clang are you using? I do get a "vfma.f16" with a recent trunk build. I haven't looked at older versions and when this landed, but we had an effort to plug the remaining

Hi, I'm trying to evaluate a Monte Carlo p-value (using truncgof package) on a left truncated sample. >From an empirical sample I've estimated a generalized pareto distribution parameters (xi, beta, threshold) (I've used fExtremes pkg). I'm in doubt on what of the following command is the most appropriate: Let: x<-sample t<-threshold xt<-x[x>t] xihat<-gpdFit(x,

I have a very general question about what the centering option in basehaz does to factors. (basehaz computes the baseline cumulative hazard for a coxph object using the Breslow estimator). Lets say I'm interested in a survival model with two (dichotomous) factors and a continuous covariate. Variable Possible Values Factor1 0 or 1 Factor2 0 or 1

Hello everybody, I'm reading the .td files defining the Cortex-A57 processor, which is a subtarget of AArch64 target, and there is something confusing me in the `AArch64SchedA57.td` file. In the top of `AArch64SchedA57.td`, various processor resource are defined, as follows ``` def A57UnitB : ProcResource<1>; // Type B micro-ops def A57UnitI : ProcResource<2>; // Type

Dear R-help, Here's a simple example of nonlinear curve fitting where nls seems to get the answer wrong on a very simple exponential fit (my R version 2.7.2). Look at this code below for a very basic curve fit using nls to fit to (a) a logarithmic and (b) an exponential curve. I did the fits using self-start functions and I compared the results with a more simple fit using a straight lm()

Hi, I have a dataframe ds1.2 - 503 categorial variables and 1 continuous response variables. I ran aregImpute to deal with NA's and got the followig error: > fmla = terms( Response ~ . ,data=ds1.2) > ds.i = aregImpute(fmla,data=ds1.2) Error in matrix(as.double(1), nrow = n, ncol = p, dimnames = list(rnam, : length of dimnames [2] not equal to array extent Could you explain

Could someone point me in the right direction for the following issue: A function is defined as follows: tfun <- function(dat) { fmla <- as.formula("y~x+z") dat2 <- dat mdl <- lm(fmla,dat2) mdl <- step(mdl) } Then the following code dat <- data.frame(x=1:10,z=1:10,y=(1:10)^2+10*(1:10)) tfun(dat) generates the output Start: AIC= 43.67

Hi, If I create a formula with say 100 terms and then paste it: xnam <- paste("x", 1:100, sep="") fmla <- as.formula(paste("y ~ ", paste(xnam, collapse= "+"))) paste(fmla) The result seems to cut off everything after the first 500 characters and gives no warning message. I have the most recent version of R from the R website and the problem occurs