similar to: Partitioning matrix

Hi, I have following codes : library(zoo); library(ggplot2); library(plyr) dat <- rnorm(306); vv <- letters[1:6]; dat1 <- data.frame(dat, vv) dat2 = zooreg(rnorm(51), as.yearmon(as.Date("2000-01-01")), frequency=12) ggplot(dat1) + geom_line(aes(y=dat, x=index(dat2), colour=vv), group=vv, size = 1.3) However I got error while plotting them :

Dear all, I have following two list object, both are basically collection of matrices : dat1 <- matrix(rnorm(25*6), ncol=6) dat1 <- split(dat1, seq(5,25,by=5)) dat1 <- lapply(dat1, matrix, ncol=6) dat2 <- matrix(rnorm(25*4), ncol=4) dat2 <- split(dat2, seq(5,25,by=5)) dat2 <- lapply(dat2, matrix, ncol=4) Now I want to replace last 4 columns of each matrix at "dat1"

Hi , I am trying to get some estimator based on lognormal distribution when we have left,interval, and right censored data. Since, there is now avalible pakage in R can help me in this, I had to write my own code using Newton Raphson method which requires first and second derivative of log likelihood but my problem after runing the code is the estimators were too high. with this email ,I provide

HI R help, I was trying to get identical data frame from a list using two methods. #Suppose my list is: listdat1<-list(rnorm(10,20),rep(LETTERS[1:2],5),rep(1:5,2)) #Creating dataframe using cbind dat1<-data.frame(do.call("cbind",listdat1)) colnames(dat1)<-c("Var1","Var2","Var3") #Second dataframe conversion

I need to split a given matrix in a sequential order. Let my matrix is : > dat <- cbind(sample(c(100,200), 10, T), sample(c(50,100, 150, 180), 10, > T), sample(seq(20, 200, by=20), 10, T)); dat [,1] [,2] [,3] [1,] 200 100 80 [2,] 100 180 80 [3,] 200 150 180 [4,] 200 50 140 [5,] 100 150 60 [6,] 100 50 60 [7,] 100 100 100 [8,] 200 150 100 [9,]

I would like to compare two data sets saved as text files (example below) to determine if both sets are identical(or if dat2 is missing information that is included in dat1) and if they are not identical list what information is different between the two sets(ie output "a1", "a3" as the differing information). The overall purpose would be to remove "a1" and

Hi R users I am trying to find unmatched data from two dataframes. I would like to find unmatched data based on several factors. For the following data: dat1 <- data.frame(x = paste("A", 1:6, sep=""), y = c("andy","bob","ciaran","dan", "eion", "fred")) dat1 dat2

For those who suggest other ways to do this, I ALREADY HAVE ANOTHER DESIGN SOLUTION, DESCRIBED AT THE END. That being said, I want to know if it's possible to reference a slot in an S4 class from another slot, i.e. I'd like to have the "self.*" semantics of Python so that I can reuse a slot. That is, for various reasons it would be nice to be able to do something like:

Hello and thanks for helping. #some data L3 <- LETTERS[1:3] dat1 <- data.frame(cbind(x=1, y=rep(1:3,2), fac=sample(L3, 6, replace=TRUE))) #When x==1 and y==1 I want to replace the 1 values with NA #I can select the rows I want: dat2<-subset(dat1,x==1 & y==1) #replace the 1 with NA dat2$x<-rep(NA,nrow(dat2) dat2$y<-rep(NA,nrow(dat2) #select the other rows and rbind

Hello, Apologies for a similar earlier post. I didn't include enough details in that one. I am having a little trouble subsetting some data based on a grouping variable. I am using an instrument that does depth profiles of a water column. The instrument records on the way down as well as the way up. So thanks to an off-list reply I can subset the data so that all data collected at the

Dear list, I have a function whose first argument is '...'. Each element of '...' is a data frame, and there will be at least 2 data frames in '...'. The function processes each of the data frames in '...' and returns a list, whose components are the processed data frames. I would like to name the components of this returned list with the names of the original data

Hi, useR- In RMySQL, how do I overwrite records? (equivalent to "replace" query). For example, suppose that dat2 is a newer data.frame than dat1. con <- dbConnect(MySQL()) res <- dbWriteTable(con, "DBname", dat1, row.names=F, append=T, replace=T) res <- dbWriteTable(con, "DBname", dat2, row.names=F, append=T, replace=T) This would not update/replace the

Hi, dat1<- read.table(text=" id??????????????? t???????????????????? scores 2???????????????? 0??????????????????????? 1.2 2???????????????? 2???????????????????????? 2.3 2???????????????? 3??????????????????????? 3.6 2???????????????? 4??????????????????????? 5.6 2???????????????? 6??????????????????????? 7.8 3???????????????? 0??????????????????????? 1.6 3????????????????

Dear List, I have some problem with my simulation code. Here is output from R: > sim.sp <- function(data,CM,n,N) + { + C <- matrix(rep(NA,N),ncol=1) + for(i in 1:N) + { + j <- n + xx <- which(colSums(CM[j,])==1) + V <- names(xx) + V <- paste(V, collapse="+") + V <- paste("SBA~", V) + rd <- round(nrow(data)*(2/3)) + d <-

I would be incredibly grateful to anyone who'll help me translate some SAS code into R code. Say for example that I have a dataset named "dat1" that includes five variables: wshed, site, species, bda, and sla. I can calculate with the following SAS code the mean, CV, se, and number of observations of "bda" and "sla" for each combination of

HI Fares, You could try this: dat1<- read.table(text=" date????? donation 3jan2003?? 20235 4jan2003?? 25655 5jan2003?? 225860 6jan2003?? 289658 7jan2003?? 243889 8jan2003?? 244338 9jan2003?? 243889 ",sep="",header=TRUE,stringsAsFactors=FALSE) The post is not very specific as to what you need.? I hope this works for you. library(xts)

Hi R Users, I do have very big data sets and wanted to run some of the analyses many times with randomization (1000 times). I have done the analysis using an example data but it need to be done with randomized data (1000 times). I am doing manually for 10000 times but taking so much time, I wonder whether it is possible to perform the analysis with creating a loop for many replicated datasets?

About time series graphs, I need help to move on: A time series of data directly from a data logger comes in the dat format created below: year<-c(rep(2005,10)) doy<-c(rep(173,5),rep(174,5)) time<-c(15,30,45,100,115,15,30,45,100,115) dat1<-c(0.022128,0.035036,0.051632,0.071916,0.081136,0.07837,0.083902,0. 126314,0.080214,0.117094) dat2<-

Folks, The projection pursuit regression function in the base R seems to crash when the optimization level is set to zero, i.e. the initial ridge terms are accepted without refitting. I encountered this problem in an out-of-sample prediction exercise using predict. But further investigation suggests the issue is with the ppr fit and predict just sppeds up the crash. The other optlevels seem to be

Hi Elisa, I hope this is what you wanted. dat1<-read.csv("peaks.csv",sep=",") #Subset dat2<-dat1[1:5,] res1<-do.call(cbind,lapply(seq_len(nrow(dat2)),function(i) do.call(rbind,lapply(split(rbind(dat2[i,],dat2[-i,]),1:nrow(rbind(dat2[i,],dat2[-i,]))), function(x) {x1<-rbind(dat2[i,],x);