similar to: Growth calculation

Hi, I have a data frame with one column and several rows of the form. "Peak Usage : init:2359296, used:15859328, committed:15892480, max:50331648Current Usage : init:2359296, used:15857920, committed:15892480, max:50331648|-------------------|" I tested the regex Current.*?[\|] in an online tester which greedily matches upto the first 'pipe' character Current

Hi, Kbytes RSS rNo 2689632 1450876 1433788 2689632 1450876 1433788 2689632 1450876 1433788 2689632 1450876 1433788 2689632 1450876 1433788 2689632 1450876 1433788 2689632 1450876 1433788 2689632 1460168 1443084 plot(data$rNo,data$RSS,pch=0,type="b",col=36,axes=FALSE, ylab="", xlab="",las=2,lwd=2.5) title(" PID -

Hi, I would like to understand how to draw a graph to find out the average memory used by a single httpd process given these details collected over a period of several days. I code R but this is about a method to find an average memory utilization. I believe I have enough data. How should I statistically calculate this ? The figures inside braces are the total httpd processes at

Hi, ' Not specific to 'R'. I search for patterns and found http://patternsinfp.wordpress.com/ which is too heavy for me. There is a 'Pragmatic Programmer' book on such patterns for Scala and Clojure. Is there anything for R ? I wanted to code this. Is there a functional pattern in R for multiple 'if' loops like this ? if(

Hi, I have been using R for a few months and I have this working code. Don't seen any problem but this takes a long time. So if I have about 30000 rows it takes a few minutes. If I have 100000 it does not seem to complete. Original Data: Proto Recv-Q Send-Q Local-Address Foreign-Address State tcp 0 0 172.20.100.2:60255

Hi, I haven't tried the code yet. Is there a way to parse this data using R and create bar plots so that each program's 'RAM used' figures are grouped together. So 'uuidd' bars will be together. The data will have about 50 sets. So if there are 100 processes each will have about 50 bars. What is the recommended way to graph these big barplots ? I am looking

Dear all, I'm trying to remove some text after the period (a decimal point) in the data frame 'hi', below. This is one step in formatting a table. So I would like e.g. "2.0" to become "2" and "5.3" to be "5.3", where the variable digordered contains the number of digits after the decimal that I would like to display, in the same order in which

Hi, Can R read in .xyz files? If so, what is the package, thanks -- Shane [[alternative HTML version deleted]]

I'm a programmer in a biology lab who is starting to use R to automate some of our statistical analysis of growth rate determination. But I'm running into some problems as I re-code. 1) Hypotheses concerning Slope similarity/difference: I'm using R's anova(lm()) methods to analyse a model which looks like this: growth.metric ~ time * test.tube I understand that

I would like to install the package "growth" as it contains the function "corgram" and some other presumably useful stuff for time series analysis. I can see it is in R standard library list: http://hosho.ees.hokudai.ac.jp/~kubo/Rdoc/doc/html/packages.html<http://hosho.ees.hokudai.ac.jp/%7Ekubo/Rdoc/doc/html/packages.html>

Dear all, I am running matrix population models using package "popbio". In a deterministic model {i.e., transition matrix is defined as A <- matrix(c(0.70, 0.70,0.35,0.50), nrow=2,byrow=TRUE}, population growth rate can be estimated from the dominant eigenvalue {command "eigen.analysis"}. However, I cannot figure out the way to compute the asymptotic stochastic population

Here is my problem, 100 decision trees were built(similar to random forest) and I want to replace some of them by new trees. How can I define a tree array including 100 trees, i.e. t[100], and every t[n] is an "C5.0" object, such that when a new tree comes, i can do n<-10 t[n]<-C5.0(...) -- View this message in context:

What is the R way of computing cumulative growth rates given a series of discrete values indexed . For instance, given a matrix of 20 observations for each of 5 series (zz), what is the most straight forward technique in R for computing cumulative growth (zzcum) ? It seems for the solution I'm after might be imbedding the following cum growth rate calc as a function into a function call

I am having trouble using Jim Lindsey's libraries because of unresolved symbols. I am currently using R 0.65.0, but had this problem in earlier releases as well. I have a RedHat 6.0 Linux on i386 and use egcs (upgraded to that distributed with rawhide, after first failures with the libraries): egcs-g77-1.1.2-19 egcs-1.1.2-19 Installation of the libraries works but on loading >

Dear all, I have some growth curve data from an experiment that I try to fit using lm and lmer. The curves describe the growth of classification accuracy with the amount of training data t, so basically y ~ 0 + t (there is no intercept because y=0 at t0) Since the growth is somewhat nonlinear *and* in order to estimate the treatment effect on the growth curve, the final model is y ~ 0 + t +

We are constructing growth charts (age/weight and age/length) for children with diagnosis that impacts weight/length. But we we don't know how to use R for producing growth charts. We are collection data of Age, Weight and Length. The data are used to produce diagnosis-specific Growth charts (like the CDC Growth Charts:

Hi, I would like to access a local mysql database. In python using the sqlite3 library it is quite straightforward. I just have to pass the path to the sqlite-file; sqlite3.connect('.../xy.sqlite') Is there something similar in R (or specifically in the RMySQL package)? It seems like I have to use the MySQL function in the RMySQL package in order to ... "initializes a MySQL

I'm trying to understand how to plot individual growth curve trajectories, with the overall mean trajectory superimposed (preferably in a slightly thicker line, maybe in black) over the individual trajectories. Using the sleepstudy data in lme4, here is the code I have so far: library(lme4) library(lattice) xyplot(Reaction ~ Days, data = sleepstudy, group = Subject, type = 'l')

I need the output to have groups and the probability any given record in that group then has of being in the response class. Just like my email in the beginning i need the output that looks like if A and if B and if C then %77 it will be D. The examples you provided are just simply not similar. They are different and would take interpretation to get what i need. On Apr 14, 2016 1:26 AM,

Hi everyone, I have a quick and probably easy question about lme for this list. Say, for instance you want to model growth in pituitary distance as a function of age in the Orthodont dataset. fm1 = lme(distance ~ I(age-8), random = ~ 1 + I(age-8) | Subject, data = Orthodont) You notice that there is substantial variability in the intercepts (initial distance) for people at 8 years, and that