similar to: Package impute exist in quite different version on CRAN and BioC

I am wanting to plot a 95% confidence band using segplot, yet I am wanting to have groups. For example if I have males and females, and then I have them in different races, I want the racial groups in different panels. I have this minor code, completely made up but gets at what I am wanting, 4 random samples and 4 samples of confidence, I know how to get A & B into one panel and C&D in to

Dear Dr.Goslee and anyone may intrested in matrix manipulate, I am using your ecodist to do mantel and partial mantel test, I have locality data and shape variation data, and the two distance matrixs are given as belowings. When I run the analysis, it is always report that the matrix is not square, but I didn't know what's wrong with my data. Would you please help me on this. I am quite

Hi all, As a molecular biologist by training, I'm fairly new to R (and statistics!), and was hoping for some advice. First of all, I'd like to apologise if my question is more methodological rather than relating to a specific R function. I've done my best to search both in the forum and elsewhere but can't seem to find an answer which works in practice. I am carrying out

Dear group, I have a matrix like the following: Name Sample1 sample2 sample3 sample4 ..... sample(n) nm1 10.5 13.5 30 31 nm2 8 11 34 29 nm3 9 10.3 27.8 35 nm(j) I want to be able to calculate correlation between all pairs of names. For example (nm1,nm2),

Hello All, After a few months of hiatus, we have resumed working on mips port of Xen. We are at the point of booting dom0 userland (init process and its children). The init process gets spawned and in turn forks off processes to run commands in /etc/rc.d/init.d/rcS. However, the getty process does not seem to run subsequently and hence the login prompt is not displayed. Dom 0 does not use the

Starting with data from a microarray experiment and I would like to analyse the influence of two factors (age, treatment) on gene expression. Looking through the r-help archives and the web I tried the following: I put my data in a dataframe similar to this one: > example.df <- as.data.frame(matrix(data=runif(32,100,1000), nrow=4, ncol=4)) > example.df <-

Hi. I need a very short piece of help regarding colouring segments plotted on a graph. When I am plotting segments for the graph, I am using "red" and "darkgreen for the values "1" and "2" respectively. Heres the relevant line of code in R: + col = c("red", "darkgreen")[line.colour.value]) I just need to extend this to refer to a larger

Hi there ! I've got 7 Samples that may contain 29 differents kinds of material.... every line below corresponds to a differente kind of material, example: Sample 1, 2,3,5 and 6 has material 1 .... So I want to know how can I do a Venn Diagram with the data below .... ps ( it was generated by a csv, where every line is sep with (";")) Can someone helps me ? thanks csv file :

Hello all, I posted a question to this list last week and received no response. I am unsure if this means no-one knows the answer or if I posed the question badly. I'm going to assume I posed the question badly and try again. I am new to R so it is quite likely it's a very naive question, however if there is something blindingly obvious that I am missing or if there is another resource I

Hallo! <?xml:namespace prefix = o ns = "urn:schemas-microsoft-com:office:office" /><o:p></o:p> <o:p> </o:p> I want to compare different samples with the formula … <o:p></o:p> <o:p> </o:p> Here some example data: <o:p></o:p> <o:p> </o:p> <o:p> </o:p> sample1<o:p></o:p>

Dear friends, In R, the help of "bic.glm" tells the difference between postmean(the posterior mean of each coefficient from model averaging) and condpostmean(the posterior mean of each coefficient conditional on the variable being included in the model), But it's still unclear about the results explanations, and the artile of Rnews in 2005 on BMA still don't give more detail on

Hello, I would like to fit a linear regression and when I use summary(), I got the following result: Call: lm(formula = weight ~ group - 1) Residuals: Min 1Q Median 3Q Max -1.0710 -0.4938 0.0685 0.2462 1.3690 Coefficients: Estimate Std. Error t value Pr(>|t|) groupCtl 5.0320 0.2202 22.85 9.55e-15 *** groupTrt 4.6610 0.2202 21.16 3.62e-14

Hi all, I have a question about linear model with interaction: I created a data frame df like this: >df V1 V2 V3 V4 V5 1 6.414094 c t a g 2 6.117286 t a g t 3 5.756922 a g t g 4 6.090402 g t g t ... which holds the response in the first column and letters (a,c,g,t) in the other columns. I am interested to see if there are interactions between the neigbouring letters

Hi all, I have a question about linear model with interaction: I created a data frame df like this: >df V1 V2 V3 V4 V5 1 6.414094 c t a g 2 6.117286 t a g t 3 5.756922 a g t g 4 6.090402 g t g t ... which holds the response in the first column and letters (a,c,g,t) in the other columns. I am interested to see if there are interactions between the neigbouring letters

Hi, I want to fit multiple cox models using the coxph() function. To do this, I use a for-loop and save the relevant results in a separate matrix. In the example below, only two models are fitted (my actual matrix has many more columns), one gives a warning message, while the other does not. Right now, I see all the warning message(s) after the for-loop is completed but have no idea which model

I have to perform ANOVA's on many different data organized in a dataframe. I can run an ANOVA for each sample, but I've got hundreds of data and I would like to avoid manually carrying out each test. in addition, I would like to have the results organized in a simple way, for example in a table, wich could be easy to export. thank you for assistance simone

I wonder if the 'sep' argument in reshape() is being ignored unintentionally: ## From example(reshape) df <- data.frame(id=rep(1:4,rep(2,4)), visit=I(rep(c("Before","After"),4)), x=rnorm(4), y=runif(4)) reshape(df, timevar="visit", idvar="id", direction="wide", sep = "_") id x.Before

hi, i am getting an error when calling the impute.knn function (see the screenshot below). what is the problem here and how can it be solved? screenshot: ################## *** caught segfault *** address 0x513c7b84, cause 'memory not mapped' Traceback: 1: .Fortran("knnimp", x, ximp = x, p, n, imiss = imiss, irmiss, as.integer(k), double(p), double(n), integer(p),

Hello R experts I am using Hmisc aregImpute and Impute (following example on page 105 of The Hmisc and Design Libraries). *My end goal is to have NAs physically replaced in my dataframe. I have read the help pages and example in above sited pdf file, but to no avail. Here is example of what I did. Ph, my data frame, is attached. > xt <- aregImpute (~ q5 + q22rev02 + q28a, n.impute=10,

Hi I have a dataset from biological data with forty samples whichh relate to four different treatments. Each sample has thousands of values but as usuual contains missing values I want to use knn to imput these missing values. I am doing tthis using knn.impute. Do I need to specify the various groups or can I just use the knn.impute command on the whole dataset together. Also I am setting