search for: 0.2000000

Hello, I have a problem with the hist() function and showing densities. The densities sum to 50 and not to 1! I use R version 2.9.1 (2009-06-26) and I load the seqinR library. My data is the following vector: [1] 0.1400000 0.2000000 0.2200000 0.2828283 0.1600000 0.1600000 0.3600000 [8] 0.1600000 0.2200000 0.2600000 0.2000000 0.3000000 0.2200000 0.2342342 [15] 0.1800000 0.2200000 0.1600000

Hello R-Users, its again me with a question. I´m using R 2.5.0 on Mac Power Book running Mac OS X 10.4.10 I try to calculate distances betweeen two data tables looking like this V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 1 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0

Hello, I am trying to get a linear model of y ~ log(x). *> lm (y~log(x))* However, I always get an error report: /Error in model.frame.default(formula = y ~ log(x), drop.unused.levels = TRUE) : variable lengths differ (found for 'log(x)')/ *Here was my y:* > y [1] 0.4500000 0.0500000 0.5000000 0.4000000 0.0000000 0.5000000 0.4000000 [8] 0.0500000

Hello, I have this data: Time AMP 0 0.2000000 10 0.1958350 20 0.2914560 40 0.6763628 60 0.8494534 90 0.9874526 120 1.0477692 where AMP is the concentration of this metabolite with time. If you plot the data, you can see that it could be fitted using a logistic regression. For this purpose, I used this code: AMP.nls <- nls(AMP~SSlogis(Time,Asym, xmid, scal), data

See ?rpart.control. I get: > golf.rp = rpart(Outlook ~ ., golf, control=rpart.control(minsplit=1)) > golf.rp n= 14 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 14 9 rain (0.2857143 0.3571429 0.3571429) 2) Temperature< 71.5 6 2 rain (0.1666667 0.6666667 0.1666667) 4) Temperature< 64.5 1 0 overcast (1.0000000 0.0000000 0.0000000) * 5)

Hello again, Let say I have a matrix: Mat <- matrix(1:12, 4, 3) And a vector: Vec <- 5:8 Now I want to do following: Each element of row-i in 'Mat' will be divided by i-th element of Vec Is there any direct way to doing that? Thanks for your help

Dear all, I have a quite basic questions about anova analysis in R, sorry for this, but I have no clue how to explain this result. I have two datasets which are named: nmda123, nmda456. Each dataset has three samples which were measured three times. And I would like to compare means of them with Posthoc test using R, following please see the output: (CREB, mCREB and No virus are the name of

Dear R-list! My question is related to an Anova including within and between subject factors and unequal group sizes. Here is a minimal example of what I did: library(car) within1 <- c(1,2,3,4,5,6,4,5,3,2); within2 <- c(3,4,3,4,3,4,3,4,5,4) values <- data.frame(w1 = within1, w2 = within2) values <- as.matrix(values) between <- factor(c(rep(1,4), rep(2,6))) betweenanova <-

Hi, I sometimes play around with extreme parameters for distributions and found that qbeta is not always monotone as the following example shows. I don't know whether this is serious enough to submit a bug report (as this example is near to the limitations of floating point arithmetic). Josef > x <- qbeta((0:100)/100,0.01,5) > x [1] 0.000000e+00 1.253990e-201 1.589622e-171

I'm sory for my weak english. I need to analyze this subject : x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 y 0 0 1 0 0 1 0 0 1 0 czarne 1 1 0 0 0 0 1 0 0 0 rude 0 0 1 0 0 1 1 0 0 0 braz 0 0 1 0 1 0 1 0 0 0 blond 1 0 0 0 0 1 0 0 0 1 rude 1 1 0 0 0 0 0 0 0 1 blond 0 0 1 1 0 0 0 0 1 0 czarne 1 0 0 1 0 0 1 0 0 0 blond 0 0 1 0 0 1 1 0 0 0 blond 1 0 0 0 0 1 1 0 0 0 czarne 0 0 1 0 0 1 0 0 0 1 czarne 1 0 1 0 0 0

Dear Mailing-List, I think this is a newbie question. However, i would like to integrate a loop in the function below. So that the script calculates for each variable within the dataframe df1 the connecting data in df2. Actually it takes only the first row. I hope that's clear. My goal is to apply the function for each data in df1. Many thanks in advance. An example is as follows: df1

Hi, I am using package deSolve to run some ordinary differential equations (ODE) as part of a mathematical modeling project. I have solved for the following equilibrium states: Seq1<-a*(1-Neq1)/(f*Veq1+m+d) Ceq1<-(f*Seq1*Veq1+g*Ieq1+r*(1-Neq1)-b1*Veq1*Ieq1)/(b2+m+d+g) Ieq1<-(-b2*Ceq1)-r*(1-Neq1)/(b1*Veq1-g-u) Veq1<-o*(Ceq1+Ieq1)/e I want to take the differential of both sides of

Dear list, I have very little experience in dealing with proportions, i am sure this is a very simple question but i could find no suitable answer beyond doing a chi-sq test and then using the Marascuilo procedure as a post-hoc analysis. I am simply wanting to know if the proportions ( i.e the number of Yes / No) significantly differ between the cases and if so which cases are significantly

Dear list, Im trying to do the following operation but im not able to do it This is my table: 1 2 3 1 0 7 4 2 0 2 0 3 0 1 3 4 0 3 4 what i would like to do is divide each row values with the corresponding column' sum,namely: 1 2 3 1 0 0.54 0.36 2 0 0.15 0 3 0 0.08

Hi, Assuming that you provided the sample data from the file. temp <- readLines(textConnection("#Hogd/met, Temp, 005[M], Value #Hogd/met, Difftemp, 051[M], Value BA0+ 1 MTEMP005 1 [deg.C] 2 MDTMP051 1 [deg.C] EOH 891231, 2400, -1.5, -0.21, 900101, 0100, -1.4, -0.25, 900101, 0200, -1.6, -0.28, 900101, 0300, -1.7, -0.25, 900101, 0400, -2.1, -0.0999999, 900101, 0500, -2.3, -0.0899999,

The "genetics" package for handling single-locus genetic data is now available on CRAN in both source and Windows binary formats. The purpose of this package is to make it easy to create and manipulate genetic information, and to facility use of this information in statistical models. The library includes classes and methods for creating, representing, and manipulating genotypes

The "genetics" package for handling single-locus genetic data is now available on CRAN in both source and Windows binary formats. The purpose of this package is to make it easy to create and manipulate genetic information, and to facility use of this information in statistical models. The library includes classes and methods for creating, representing, and manipulating genotypes

Hi all, I frequently encounter datasets that require me to repeat the same calculation across many variables. For example, given a dataset with total employment variables and manufacturing employment variables for the years 1990-2010, I might have to calculate manufacturing's share of total employment in each year. I find it cumbersome to have to manually define a share for each year and

Hello, I am currently doing my project and I need some help. I am trying to schedule tutors for a study room where students come in and out for helps. My goal is to schedule the tutors to maximally accomodate to the flow of students that need help with math so that each tutor is given an appropriate(also consecutive) hours of schedule. I am using monte carlo simulation for this, however I have

Hi, #working directory data1 #changed name data to data1.? Added some files in each of sub directories a1, a2, etc. ?indx1<- indx[indx!=""] lapply(indx1,function(x) list.files(x)) #[[1]] #[1] "a1.txt"??????? "mmmmm11kk.txt" #[[2]] #[1] "a2.txt"??????? "mmmmm11kk.txt" #[[3]] #[1] "a3.txt"??????? "mmmmm11kk.txt" #[[4]] #[1]