similar to: Correlation Matrix in R

Hello, I'm trying to position a plot title "1 a)" in the top left corner of a graph; i've set outer=TRUE for it to be in the outer margin unfortunately this is cut off. Is there a way either to make it so that it is not cut off or increase the number of margins and then place it in margin below the outer? Heres what I have so far windows(width=7,height=7)

Hi all, I have count data (number of flowering individuals plus total number of individuals) across 24 sites and 3 treatments (time since last burn). Following recommendations in the R Book, I used a glm with the model y~ burn, with y being two columns (flowering, not flowering) and burn the time (category) since burn. However, the residual deviance is roughly 10 times the number of degrees of

Hello, I am trying to increase the thickness of the axis in plot() without reverting to the use of paint programs i see posts on that topic for the xyplot function but want to see if i can do it with plot() because i've already setup my graph script using that i thought i could use axis() function and specify lwd="thickness" or lwd.axis= but that does not work like it does for

Given four known and fixed vectors, x1,x2,x3,x4, I am trying to generate a fifth vector,z, with specified known and fixed partial correlations. How can I do this? In the past I have used the following (thanks to Greg Snow) to generate a fifth vector based on zero order correlations---however I'd like to modify it so that it can generate a fifth vector with specific partial

Dear All, I have a file (a matrix) on which I have to compute the correlations. The function "cov" compute the correlations between the columns of a matrix, but I want to compute the correlations between the raws. Can somebody say to me how I can carry out that? Thanks Laurence -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list --

I'm looking to create a correlation matrix, but I have already obtained the correlations, which are stored in a vector. (Basically, I'm running a simulation which requires a correlation matrix, but I am simulating the various correlations.) My aim is to create a function that can take the vector, and fit the values into their respective locations in a correlation matrix. (The correlations

I'm trying to obtain within-group correlations on a subset of variables. I first selected my variables using the following command: mydata$x<-mydata[c("iq","education","achievement")] I'd like to look at correlations among those variables separately for men and women. My gender variable in mydata is coded 1 (women) and 0 (men). I have successfully used

Hello, I am new to R and have a problem I have had some trouble with. Basically I have a list of some 80000 genes or so with data points for expression levels at various time points/conditions. I also have subsets of these, usually only a few hundred genes in size, known to be associated with some biological process. What I want to do is correlate the entire list with my subset and then

I use R 2.15.2 on MacosX 10.8.2 I have a problem checking for a package that I want to submit in CRAN. When I check like that, no problem or warning are detected. system("R CMD check '[path]/phenology'") I create the .tar.gz package with that. The file phenology_3.43.tar.gz is generated. I can install the package and it works. system("R CMD build

Hi, I'm dealing with a datamining analysis: I've a lot of categories of product sold per week (n. week =26, n. categories about 50. my dataframe is like this: Settimana ALIMENTI..ALTRI. ALIMENTI.APROTEICI 1 1 3 19 2 2 2 0 3 3 1 22 4 4 2

Hello everyone My name is Vangelis and I want to ask a question about partial correlation. I have used the command "pcor.shrink" to evaluate the partial correlations of a data.frame but the problem is that in the output results I cannot see whether these correlations are significant or not. Is there any command which can show me if these correlations are significant at 95% level or

Hi, can you help? I am really struggling to find the answer to the following question. I have conducted a standard multiple regression using SPSS. The dependent variable is CE, and the independent variables are Ibelieve and Tbelieve. Because the independent variables are highly correlated I have examined the squared semi-partial correlations. I understand that the Ibelieve squared semi partial

Hello all. I have 14 variables, named D2_1, D2_2,...,D2_14 (inherited from a data set). I would like to create a matrix of correlations, although I would be content in just learning how to create a proper do loop. I tried something like this: (for i in 1:14){cor(D2_[i],D2_[i], use = "complete.obs")} This is wrong, of course, but I don't know how to "tell" R to run

Thanks in advance. I want to derive correlations of variables in a dataset Specifically library(Ecdat) data(Housing) attach(Housing) cor(lotsize, bathrooms) this code results only the correlationship between two variables. But I want to examine all the combinations of variables in this dataset. And I will finally make a table in Latex. How can I test correlations for all combinations of

[Apologies in advance if this is too "statistics" and not enough "R".] I've got an experiment with two sets of treatments. Each subject either received all treatments from set A or all treatments from set B. I can compute the N pairwise correlations for all treatments in either set using cor(). If I take the mean of these N pairwise correlations, I see that the effects

Hello, I calculate the correlation between two matrices cor(x,y, method="spearman") and I am wondering if it possible to see only the significant correlations. I can do that for single OTUs with cor.test() command but I would like to have an output for whole matrix. Besides,I would like to plot the relative abundance of each OTU, against the number of significant pair-wise correlations

Hi there, Am I correct to believe that tetrachoric correlation is a special case of polychoric correlation when there are only two levels to the ordered factor? Thus it should be okay to use hetcor from the polycor package to build a matrix of correlations for binary variables? If this is true, how can one estimate 95% confidence intervals for the correlations? My guess would be mat =

Dear All, I have two matrices A (40 x 732) and B (40 x 1230) and would like to calculate correlation between them.  I can use: cor(A,B, method="pearson") to calculate correlation between all possible pairs. But the issue is that there is one-many specific mappings between A and B and I just need to calculate correlations for those pairs (not all). Some variables in A (proteins, say p1)

Dear R-users Could anyone tell me which library contains a function to compute polycoric correlations? I wonder the same question was asked a while ago, but I could not locate the mail in the R-help archives. Sorry for bothering you. Sincerely ------------------------ Hiroto Miyoshi ???? h_m_ at po.harenet.ne.jp

Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g., a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a