similar to: How to add a function to another written code?

I am using the code below to calculate the correlation map between two datasets. This code worked fine. dir1 <- list.files("D:thly", "*.bin", full.names = TRUE) dir2 <- list.files("D:002", "*.envi", full.names = TRUE) file_tot <- array(dim = c(1440, 720, 11, 2)) for(i in 1:length(dir1)) { file_tot[, , i, 1] <-

x <- c( "a1", "a10", "a2" ) y <- c( "b10", "b2", "a12", "ca1" ) DF <- expand.grid( x = x, y = y ) # randomize set.seed( 42 ) DF <- DF[ sample( nrow( DF ) ), ] # missing from gtools mixedrank <- function( x ) { seq.int( length( x ) )[ gtools::mixedorder(x) ] } o <- do.call( order, lapply( DF, mixedrank ) )

??? > y <- sort( c("a1","a2","a10","a12","a100")) > y [1] "a1" "a10" "a100" "a12" "a2" > mixedsort(y) [1] "a1" "a2" "a10" "a12" "a100" **Please read the docs!** They say that mixedsort() and mixedorder() both take a **single

Thanks for that answer. I was not aware of gtools::mixedsort <https://www.rdocumentation.org/packages/gtools/versions/3.5.0/topics/mixedsort> function. Jean On Thu, Jun 29, 2017 at 2:47 PM, Henrik Bengtsson < henrik.bengtsson at gmail.com> wrote: > You can use: > > > files <- list.files(path = "folder01") > > files <- gtools::mixedsort(files) >

split any mixed columns into letter and number columns and then order can be used on that: DF <- data.frame(x = c("a10", "a2", "a1")) o <- do.call("order", transform(DF, let = gsub("\\d", "", x), no = as.numeric(gsub("\\D", "", x)),

1- mixedorder does not work in a "do.call(mixedorder, mydataframe)" call like the order function does This is tangential, but do.call(order, mydataframe) is not safe to use in a general purpose function either - you need to remove the names from the second argument: > d <- data.frame(method=c("New","New","Old","Old","Old"),

You can use: > files <- list.files(path = "folder01") > files <- gtools::mixedsort(files) to order the files in a "human-friendly" order rather than lexicographic order (which sort() provides). FYI 1; it's preferred to use file.path("folder01", list[i]) rather than paste('folder01',lists[i],sep='/'). FYI 2; if you use

Hi, I am trying to create 3 mds plots: one with axis 1 vs axis 2, one with axis 2 vs axis 3, and one with axis 1 vs axis 3. When inputting my code, I only end up with one diagram and when inputting mdsg.mds$dims, the program returns 2 for 2 dimensions. How can I create the other two plots? Any help would be greatly appreciated, Calla Carbone The table I use is number of each taxa on each

Hi, Searching for functions that would order strings that mix characters and numbers in a "natural" way (ie, "a1 a2 a10" instead of "a1 a10 a2"), I found the mixedsort and mixedorder from the gtools package. Problems: 1- mixedorder does not work in a "do.call(mixedorder, mydataframe)" call like the order function does 2- gtools has not been updated in 2.5

Error: could not find function "hier.part" things I have tried: 1. reinstall R (lastest version, on windows XP) 2. install package "gtools" 3. include: library(gtools) require(gtools) 4. how I call this function: hier.part(c$Y, xdata, fam = "gaussian", gof = "Rsqu") 5.when I try to check what's in the package "gtools", I get (hier.part is

I have a revised version of the gregmisc package, which I've converted into a package bundle each of which has a namespace: gplots, gmodels, gdata, gtoools. Of course, there are interdependencies among these namespaces: gsun374: /tmp [11]> cd gregmisc/ gsun374: gregmisc [12]> grep import */NAMESPACE gdata/NAMESPACE:importFrom(gtools, odd, invalid, mixedsort)

Dear Researches, I am using RF (in regression way) for analize several metrics extract from image. I am tuning RF setting a loop using different range of mtry, tree and nodesize using the lower value of MSE-OOB mtry from 1 to 5 nodesize from1 to 10 tree from 1 to 500 using this paper as refery Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007). Random Forest Models

Hello R-gurus, I'm trying to adjust different growth curves to a rather extensive dataset. I wrote up a function to go through all of them, but am encountering a problem : among the more than 1000 curves I have, obviously for some of them I encounter conversion problems. I'd like for my function to keep going to the next curve and store the fact that for curve number X I had a convergence

Dear community, I'm performing svm-regression with svm at library e1071. As I wrote in another post: "svm e1071 call - different results", I get different results if I use the svm.default rather than the svm.formula, being better the ones at svm.formula I've debugged both options. While debugging the svm.formula, I've seen that when I reach the call: ret <-

Full_Name: Noel O'Boyle Version: 2.1.0 OS: Debian GNU/Linux Sarge Submission from: (NULL) (131.111.8.96) (1) Description of error The 10-fold CV option for the svm function in e1071 appears to give incorrect results for the rmse. The example code in (3) uses the example regression data in the svm documentation. The rmse for internal prediction is 0.24. It is expected the 10-fold CV rmse

I'm trying to use a self-written function with the summaryBy function (doBy package). I have lots of data from Monte Carlo experiments comparing different estimators across different (combinations of) parameter values, similar to the following form: colnames(mydata) <- c("X", "b0", "b1", # parameter combination, corresponding (true) parameter values

Dear Researches, I am using RF (in regression way) for analize several metrics extract from image. I am tuning RF setting a loop using different range of mtry, tree and nodesize using the lower value of MSE-OOB mtry from 1 to 5 nodesize from1 to 10 tree from 1 to 500 using this paper as refery Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007). Random Forest Models

1. This is _not_ a bug in R itself. Please don't use R's bug reporting system for contributed packages. 2. This is _not_ a bug in svm() in `e1071'. I believe you forgot to take sqrt. 3. You really should use the `tot.MSE' component rather than the mean of the `MSE' component, but this is only a very small difference. So, instead of spread[i] <- mean(mysvm$MSE), you

Need Help in Debugging below script:-------------------------------- dat <- get_majorlandmarks(dat,Dmin,Per) fit_xts <- xts(dat$fit,order.by = dat$Date,frequency = 365) close_xts <- xts(dat$Close, order.by = dat$Date, frequency = 365 ) majorlandmarks_xts <-xts(dat$Close[dat$majorlandmarks==TRUE], order.by = dat$Date[dat$majorlandmarks==TRUE], frequency = 365 ) minorlandmarks_xts

Hi, I am doing predictive modelling of Multivariate Time series Data of a Motor in R using various models such as Arima, H2O.Randomforest, glmnet, lm and few other models. I created a function to select a model of our choice and do prediction. Model1 <- function(){ .. return() } Model2 <- function(){ ... return() } Model3 <- function(){ ... return() } main <-