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First I would really like to thank the mailing list for help I got in the past, as a new to R I am really needing some support on hoe to code the following problem. I am trying to sort some data I have in a big file. The file has 4 columns and 19000 rows. An example of it looks like this:- G 0.892 A 0.108 G 0.883 T 0.117 T 0.5 C

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Full_Name: Adrian Custer Version: 0.90.0 OS: Linux on Thinkpad (pentium) and desktop (K6) Submission from: (NULL) (128.32.251.234) When I create a boxplot, and then try to overlay a lowess fit or just the points, the points do not appear in the highest level and the lowess curve does not reach the highest level. However, if I add one to each of the models, the problem is solved. I tried this

Hi, I'm estimating a loglogistic aft (accelerated failure time) model, just a simple plain vanilla one (without time dependent covariates), I'm comparing the results that I obtain between aftreg (eha package) and survreg(surv package). If I don't use any covariate the results are identical , if I add covariates all the coefficients are the same until a precision of 10^4 or 10^-5 except

I made an ANOVA with repeated mesures (aov(Mesure~Distance*Genre*Correct+Error(Sujet/(Distance*Genre*Correct)), data)) and I would like to extract the pvalues. The output is: ----------------------------------------------------------- Error: Sujet Df Sum Sq Mean Sq F value Pr(>F) Residuals 21 97.082 4.623 Error: Sujet:Distance Df Sum Sq Mean Sq F value Pr(>F) Distance

Dear R Users I would like to use optim function to optimize a function. I read help but I couldn't find what I need: is it possible to get information after each iteration, for example as there is in MATLAB: Gradient's Iteration Func-count f(x) Step-size infinity-norm 0 24 388.976

  Dear R helpers   Suppose I have two sets of ranges (interest rates) as   Range 1 : (7 – 7.50, 7.50 – 8.50, 8.50 – 10.00) with respective probabilities 0.42, 0.22 and 0.36.     Range II : (11-12, 12-14, 14-21) with respective probabilities 0.14, 0.56 and 0.30 respectively.     My problem is to form the combinations of these ranges in a decreasing order of joint probabilities. It is assumed that

Hi all, I am trying to implement a Monte-Carlo simulation for each cell in a spatial matrix (using mcd2 package) . I have figured out how to conduct the simulation using data from a single location (where I manually input distribution parameters into the R code), but am having trouble (a) adjusting the code to pull input variables from my various data sets and then (b) applying the entire

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I have the following data: > y [1] 0.000 0.004 0.008 0.016 0.024 0.032 0.044 0.064 0.072 0.088 0.108 0.140 [13] 0.156 0.180 0.208 0.236 0.264 0.296 0.320 0.360 0.408 0.444 0.472 0.524 [25] 0.576 > x [1] 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 [16] 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 I'd

Estimados Quisiera tener la sintaxis generar una serie de números entre 0 y 1 con distribución gamma de 25 puntos y que la suma de todos los puntos debe dar 1. Por ejemplo: c (0.000, 0.001, 0.012, 0.043, 0.078, 0.104, 0.117, 0.116, 0.108, 0.094, 0.078, 0.063, 0.049, 0.038, 0.028, 0.021, 0.015, 0.011, 0.008, 0.005, 0.004, 0.003, 0.002, 0.001, 0.001) No hemos podido

HI, Dear R community, I am using the following codes to plot, however, the lines code works. But the line was not drawn on the previous plot and did not shown up. How comes? # specify the data for missense simulation x <- seq(0,10, by=1) y <- c(0.952, 0.947, 0.943, 0.941, 0.933, 0.932, 0.939, 0.932, 0.924, 0.918, 0.920) # missense z <- c(0.068, 0.082, 0.080, 0.099, 0.108, 0.107,

Hello, R-i-zens. I'm working on an data set with a factorial ANOVA that has a significant interaction. I'm interested in seeing whether the simple effects are different from 0, and I'm pondering how to do this. So, I have my.anova<-lm(response ~ trtA*trtB) The output for which gives me a number of coefficients and whether they are different from 0. However, I want the

I am trying to calculate monthly means by year of phosphates and nitrates from a multi year data set. Can anybody help me out with the most effective way to do this? My data looks like this: Collection_Date                   Test.Name              Value 2000-01-24 17:00:00            Phosphate               0.108 2000-01-24 17:00:00            Nitrate                     0.037 2001-11-12

I have a table like the following. I want to fit Cm to Vm like this: Cm ~ Cl+Q1*b1*38.67*exp(-b1*(Vm-Vp1)*0.03867)/(1+exp(-b1*(Vm-Vp1)*0.03867))^2+Q2*b2*38.67*exp(-b2*(Vm-Vp2)*0.03867)/(1+exp(-b2*(Vm-Vp2)*0.03867))^2 I use nls, with start=list(Q1=2e-3, b1=1, Vp1=-25, Q2=3e-3, b2=1, Vp2=200). But I always get 'singlular gradient' error like this. But in SigmaPlot I can get the result. How

Hi: I need help with geom_text(). I would like to count the number of Locations and put the sum of it right above each bar. x <- "Location Lake_dens Fish Pred Lake1 1.132 1 0.115 Lake1 0.627 1 0.148 Lake1 1.324 1 0.104 Lake1 1.265 1 0.107 Lake2 1.074 0 0.096 Lake2 0.851 0 0.108 Lake2 1.098 0 0.095 Lake2 0.418 0 0.135 Lake2 1.256 1 0.088 Lake2 0.554 1 0.126 Lake2 1.247 1 0.088

Estimado Jorge Perdóneme que lo moleste de nuevo, hay otra condición además de que sum(y)=1 y es que y[1] tiene que dar 0 en el ejemplo y<- c (0.0000000000, 0.6321985783, 0.2325728597, 0.0855587737, 0.0314753138, 0.0115791209, 0.0042597205, 0.0015670636, 0.0005764905, 0.0002120790) y[1]=0 sum(y)=1 esto se utiliza en el paquete EpiEstim donde se

Hi there, I am doing multiple comparisons for data that is not normally distributed. For this purpose I tried both functions kruskal{agricolae} and kruskalmc{pgirmess}. It confuses me that these functions do not yield the same results although they are doing the same thing, don't they? Can anyone tell my why this happens and which function I can trust? kruskalmc() tells me that there are no

Dear r-help-list: If I use the rpart method like cfit<-rpart(y~.,data=data,...), what kind of tree is stored in cfit? Is it right that this tree is not pruned at all, that it is the full tree? If so, it's up to me to choose a subtree by using the printcp method. In the technical report from Atkinson and Therneau "An Introduction to recursive partitioning using the rpart

I am trying to use rbind to have the two data on the top of each other but I am getting an extra X on the column header and the rows are numberd , How to get rid of this problem? I appreciate your help x1<- read.table(file="data1.txt", header=T, sep="\t") x2<-read.table(file="data2.txt", header=T, sep="\t") y<-rbind(x1,x2) > y X0