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Dear list, I have a dataset containing values obtained from two different instruments (x and y). I want to generate 5 samples from normal distribution for each instrument based on their means and standard deviations. The problem is values from both instruments are non-negative, so if using rnorm I would get some negative values. Is there any options to determine the lower bound of normal

Tom Cohen <tom.cohen78@yahoo.se> skrev: Thanks Prof Brian for your suggestion. I should know that for right-skewed data, one should generate the samples from a lognormal. My problem is that x and y are two instruments that were thought to be measured the same thing but somehow show a wide confidence interval of the difference between the two intruments.This may be true that these

I am installing R 2.15.1 onto RHEL 6, using gcc 4.7.0 with Intel MKL 10.3.7 and the following environment: export BLAS_LIBS="-Wl,--start-group /usr/caen/intel-12.1/mkl/lib/intel64/libmkl_gf_lp64.a /usr/caen/intel-12.1/mkl/lib/intel64/libmkl_sequential.a /usr/caen/intel-12.1/mkl/lib/intel64/libmkl_core.a -Wl,--end-group -lpthread" export LAPACK_LIBS="-Wl,--start-group

I currently work on a draft of an aquatic bioassessment. The conditions tested are the following: ER river water T dechlorinated water control 0.5 + 0.5mg / L of malate T + 1 dechlorinated water control + 1g / L of malate T ED dechlorinated water control SED + ER + river water sediment SED ED + sediment + water dechlorinated. It is the result of AChE in muscle (fillet of fish). The production of

I had found the author's (Wolfgang Viechtbauer) earlier meta-analytic code in R, MiMa, useful. so I have been exploring metafor using an example dataset from MiMa. metafor provides a lot more. However, MiMa provided parameter estimates, standard errors, z values, etc. for individual moderators in the meta-analysis, but I don't see how to obtain these from metafor. Have you any help

Hi Team, I downloaded the R 3.0.2 and, built and installed it using Intel compilers (icc and ifort) from Intel parallel studio 2013, sp1. After performing make tried to check it using 'make check'. Most of the tests passed successfully but while checking a 'stats' package I saw many lines were printed which did not match the desired output. e.g. There

Hello everyone, I have a set of data in the following form, which are stored in an Excel file: nick john peter lesson1 0.465 0.498 0.473 lesson2 0.422 0.44 0.134 lesson3 0.45 0.35 0.543 lesson4 0.590 0.64 0.11 lesson5 0.543

Hello, I am frastruated with this graph, just cannot get what I need. Thank you for any suggestions or help. I really appreciate it. I wrote the following code, but there are 3 problems 1, the red line is added on the graph but without any marker on the y-axis. I want to display the number '.1361' on the y-axis. So people can easily tell 'method 2' gets a constant estimate, which

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 R users, I need some help in the followings: I'm doing factor analysis and I need to extract the loading values and the Proportion Var and Cumulative Var values one by one. Here is what I am doing: > fact <- factanal(na.omit(gnome_freq_r2),factors=5); > fact$loadings Loadings: Factor1 Factor2 Factor3 Factor4 Factor5 b1freqr2 0.246 0.486 0.145

Dear all, I have an annual time-series of population numbers and I would like to estimate the auto-correlation. Can I use acf() function and judge whether auto-correlation is significant by the plots? The acf array, eg: Autocorrelations of series 'x$log.s.r', by lag 0 1 2 3 4 5 6 7 8 9 10 11 12 1.000 0.031 -0.171

Hi. I have a CentOS 4.2 server running. I've just experience that it stops responding (http, ssh) when doing yum update pam xorg-x11 gnupg This has happened two times in a row, so I'm curious if anyone has experienced anything like this? Could it be yum or should I look for the explanation elsewhere? [root at machine ~]# yum update pam xorg-x11 gnupg Setting up Update Process Setting

Dear list, I am facing an unusual situation where I need to create two sets of legends based on the color mapping. Can't get exactly what I want and really appreciate any advice from ggplot experts. Let's say I have the first dataset "df1" that draws some points and based on which a "loess" line with confidence interval is added. Then the second dataset

Folks, I have a small dataset of counts of recoveries on defaulted loans: recoveries<-structure(c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 0, 0, 0, 0, 0, 4, 0, 1, 2, 2, 12), .Dim = c(11L, 2L), .Dimnames = list( NULL, c("pcts", "counts"))) Here is the data in columnar form: pcts counts [1,] 0.0 0 [2,] 0.1 0 [3,] 0.2 0 [4,] 0.3

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

I am comparing the Splus and R fits of a simple glm. In the following, foo is generated from rbinom with size = 20 p = 0.5. The coefficients (and SE's0 of the fitted models are the same, but the estimated deviances are quite different. Could someone please tell me why they are so different? I am using R version 1.3.1 and Splus 2000 release 3 on windows 2000. ++++++++++++++++++++++ foo

Dear all, The 'make check' step fails for the pacakge mva on IBM AIX. The tail of the Rout log file looks like: > for(factors in 2:4) print(update(Harman23.FA, factors = factors)) Call: factanal(factors = factors, covmat = Harman23.cor) Uniquenesses: height arm.span forearm lower.leg weight 0.170 0.107 0.166

Hi, I wonder if anyone can answer the following or point me in the direction of how to obtain answers to the questions. Below is Output from R and further down are the questions raised and explanation of the study. Output from R: glm(formula = CB95TO00 ~ URB + INC, family = poisson) Deviance Residuals: Min 1Q Median 3Q Max -1.2272 -1.1290 0.2709 0.4272 2.1376

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

I am doing multiple imputation with Hmisc, and can't figure out how to replace the NA values with the imputed values. Here's a general ourline of the process: > set.seed(23) > library("mice") > library("Hmisc") > library("Design") > d <- read.table("DailyDataRaw_01.txt",header=T) > length(d);length(d[,1]) [1] 43 [1] 2666