similar to: Filling NA with cumprod?

Full_Name: J. Sisk Version: 1.6.1 OS: Linux (RedHat 8) Submission from: (NULL) (67.119.41.66) Suppose you make a data-frame like so: xxx <- data.frame(a=10,b=20,c=30,d=40) Then cumprod(xxx[1,]) returns > cumprod(xxx[1,]) a b c d 1 10 20 30 40 The documentation for cumprod says that it should work on "numerical objects", and this is a data-frame, but it

I spent a lot of time searching and came up empty handed on the following query. Is there an equivalent to rowSums that does product or cumulative product and avoids use of apply or looping? I found a rowProd in a package but it was a convenience function for apply. As part of a likelihood calculation called from optim, I?m computing products and cumulative products of rows of matrices with

Dear R-help, I have been trying really hard to generate the following vector given the data (x) and parameter (alpha) efficiently. Let y be the output list, the aim is to produce the the following vector(y) with at least half the time used by the loop example below. y[1] = alpha * x[1] y[2] = alpha^2 * x[1] + alpha * x[2] y[3] = alpha^3 * x[1] + alpha^2 * x[2] + alpha * x[3] ..... below are

Hi everybody, I have a little problem about filling some gaps of NAs in my data. These gaps are between nearly constant data (temperature under snow). Here's a fake example to illustrate how it looks like approximately: DF <- data.frame(data=c(-0.51,-0.51,-0.48,-0.6,-0.54,-0.38,-0.6,-0.42,NA,NA,NA,NA,NA,NA,NA, -0.25,-0.41,-0.5,-0.5,-0.35,-0.7,-1,-0.87)) I would like to replace my NAs

Is there anything like cumsum and cumprod but which allows you to apply an arbitrary function instead of sum and product? In other words, I want a function cumfunc(x, f) that returns a vector, so that for all n up to the length of x cumapply(x,f)[n] = f(x[1:n]) This would give cumsum and cumprod as special cases when f=sum or f=prod. I could write such a function, but I can't see

Just when I think I'm starting to learn .... Statement z1 works, statement z doesn't. Why doesn't z work and what do I do to fix it ? Clearly the problem is with the first NA, but I would think it's handled through the loop vectorization. y1 <- rnorm(20, 0, .013) y1 [1] -0.0068630836 -0.0101106230 -0.0169663344 -0.0066314769 0.0075063818 [6] -0.0033548024 0.0015647863

This is my code i don't understand the error message: library(rgenoud) rm(list=ls()) set.seed(666) ######################################################### # As a first step, it is assumed that all input parameters are independent of ageing : ######################################################### InputDim <-20 # Max number of ageings in the inputs CPIRate <- rep(0.02 , InputDim ) #

I thought the "apply" functions are faster than for loops, but my most recent test shows that apply actually takes a significantly longer than a for loop. Am I missing something? It doesn't matter much if I do column wise calculations rather than row wise ## Example of how apply is SLOWER than for loop: #rm(list=ls()) ## DEFINE VARIABLES mu=0.05 ; sigma=0.20 ; dt=.25 ; T=50 ;

Hi! I noticed that cumsum behaves different than the other cumulative functions wrt. NaN values: > values <- c(1,2,NaN,1) > for ( f in c(cumsum, cumprod, cummin, cummax)) print(f(values)) [1] 1 3 NA NA [1] 1 2 NaN NaN [1] 1 1 NaN NaN [1] 1 2 NaN NaN The reason is that cumsum (in cum.c:33) contains an explicit check for ISNAN. Is that intentional? IMHO, ISNA would be better

Thanks to Fernando Tusell and especially to Brian Ripley for their work on 'mix', leading to an apparently good package mow available on CRAN. Going through the R code for the function prelim.mix, I am wondering why the following method of calculation is used at one point: umd <- as.integer(round(exp(cumsum(log(d))))) (d is a vector containing, in effect, the numbers of levels of

Hi all, I am looking for a function like cumprod() that works for matrix multiplication. In other words, I have matrices [M1, M2, ..., Mn], and I want to calculate [M1, M1%*%M2, M1%*%M2%*%M3, ..., M1%*%...%*%Mn] as quickly as possible. Right now I'm using a for() loop but it seems like there should be a faster way. Any help is appreciated! Thanks, Todd Schneider todd.w.schneider@gmail.com

Hi, is there a function that replaces the following code? n=200 boot.x[1]=odhad+boot.res[1] #(boot.x[0]=1) for (j in 1:(n-1)) { boot.x[j+1]=odhad*boot.x[j]+boot.res[j+1] } This is nested in two other loops, and I am looking for some way to improve code performance I tried sapply and cumprod but no success. Thanks Jan -- View this message in context:

Hi all, I am trying to display a matrix of plots(images), for example a 3*3 matrix of 9 image plots, such that when a user clicks on a image i can show the enlarged plot. I tried the multiple graphic device(using mfcol=c(3,3) and mfg), but it creates multiple plots in a single image file. So, i won't be able to highlight a particular plot when the user clicks on it. To be more clear, can i

Hi all, I am trying to display a matrix of plots(images), for example a 3*3 matrix of 9 image plots, such that when a user clicks on a image i can show the enlarged plot. I tried the multiple graphic device(using mfcol=c(3,3) and mfg), but it creates multiple plots in a single image file. So, i won't be able to highlight a particular plot when the user clicks on it. To be more clear, can i

Dear r users, I have 4 variables x1,x2,x3,x4 and each one has two levels, for example Y and N. For x1: prob(Y)=0.6, prob(N)=0.4; For x2: prob(Y)=0.5, prob(N)=0.5; For x3: prob(Y)=0.8, prob(N)=0.2; For x4: prob(Y)=0.9, prob(N)=0.1; Therefore, the sample space for (x1, x2, x3, x4)={YYYY, YYYN, YYNY,......} (16 possible combination) and the corresponding probabilities are {(0.6)(0.5)(0.8)(0.9),

Hello, everyone I wonder if there is in R somewhere a function similar to cumsum(). The function calculates a statistic (say mean or standard deviation) buy adding consequtively one more data point. So, say I have a timeseries of 100 observations. I start by calculating mean of first 30 observations Then I add one observation and calculate mean of 31 observations Then I add one more observation

Hi there, I have this code: Prepared_Data <- na.omit(read.csv("Prepared_Data.csv", header=TRUE)) pd <- Prepared_Data[,-3] ## data minus response variable lev <- sapply(pd,function(x) length(unique(x))) ## total parameters for n variables par(las=1,bty="l") plot(cumprod(lev),log="y") library(Matrix) m <- sparse.model.matrix(~.^2,data=pd) ncol(m)

Hi, I'm currently in the process of writing an R-installation SOP for my company. As part of that process I'm using the recommendations from the 'R Installation and Administration' document, section 3.2, "Testing an installation". This is done on an XP machine, using the latest binary of 2.11.0. The binary is downloaded and then installed from the installer. I then

hi I'm trying to use the function odiag(x) for matrix calculations. I would like to assign new values to an off-diagonal in a matrix. When I use the diag (x) function I could write something like p<-matrix(seq(1:12),ncol=4) p.new<-matrix(rep(0,12),ncol=4) diag(p.new)<-diag(p) p.new But this won't work with odiag. How can I turn odiag (x) into something like diag (x) in order

I wonder if someone could explain why, when I perform confirmatory factor-analysis model using polychoric correlations why I do not get an estimated confidence interval for the RMSEA. My experience with these type models is that I would obtain a confidence interval estimate. I did not get any warning messages with the output. RESULTS: Model Chisquare = 1374 Df = 185 Pr(>Chisq) = 0