similar to: vegan -varpart is bigger than 100% in total?

I have two data matrices that I want to make the correlation between each column from data1 and each column from data 2 and also calculate the p-value Matrices dont have the same size and I tried such a script. > bg <- read.table (file.choose(), header=T, row.names) > bg > Otu00022 Otu00029 Otu00039 Otu00042 Otu00101 Otu00105 Otu00125 Otu00131 Otu00137 Otu00155 Otu00158 Otu00172

Hello, I have a very data matrix and I have a file which has the names that I need to subset. However I cannot manage to subset the main file. ANy idea? bg <- read.table (file.choose(), header=T, row.names) bg Otu00022 Otu00029 Otu00039 Otu00042 Otu00101 Otu00105 Otu00125 Otu00131 Otu00137 Otu00155 Otu00158 Otu00172 Otu00181 Otu00185 Otu00190 Otu00209 Otu00218 Gi20Jun11 0.001217

Dear All, I am plotting a graph in ggplot, I would like to magnify the values between 0-1 without losing data in the higher range. How can I do that? neither scale_y_continous nor coord_cartesian works. Thank you ?zg?l Universit? Libre de Bruxelles

Dear All, When I plot the values and linear regression line for one data set, it is fine. But for another one I see zigzags, when I plot the confidence interval >cd Depth CHAOsep12RNA 9,94 804 25,06 1476,833333 40,04 1540,561404 50,11 1575,166667 52,46 349,222222 54,92 1941,5 57,29 1053,507042 60,11 1535,1 70,04 2244,963303 79,97 1954,507042 100,31 2679,140625 >

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

Dear All, I am trying to perform MANOVA. I have table with 504 columns(species) and 36 rows) with two grouping (season and location) Zx <- Z[c(4:504)] Zxm <- as.matrix(Z) m<- manova(Zxm~Season*location, data=Z) when I do summary.aov, I get respond for each species but summary.manova summary.manova(m) :" residuals have rank" 24<501. What can it be the reason for this error

Dear all, I am perplexed when trying to get the same results using pairwise.t.test and t.test. I'm using examples in the ISwR library, >attach(red.cell.folate) I can get the same result for pairwise.t.test and t.test when I set the variances to be non-equal, but not when they are assumed to be equal. Can anyone explain the differences, or what I'm doing wrong? Here's an example

Hi, I did a linear regression with 5 explanatory variables. Now, to see the contribution of each variable, I use varpart from vegan library. But varpart don?t accepts my 5 explanatory variables, it accept only 4. 1- How must I do to use my 5 explanatory variables? 2- Is it the sum of variance fraction of each variable must be equal to 1. Thanks for your help. Komine -- View this

I am not familiar with the vegan package, so I am just making a guess here. If 'na.action=na.omit' is part of the call to varpart, try removing it from the function call and moving it above as follows: options(na.action="na.omit") RDA_Ger <- varpart(comm, x1, x2, x3, transfo="hellinger", scale = FALSE) Maybe that will help. Regards, Eric On Sat, Sep 16, 2017 at

The varpart function does not have na.action argument, and all undefined arguments are regarded as explanatory data sets X (they come after ?). Best wishes, Jari Oksnaen

Dear R-users I applied vegan's varpart function to partition the effects of explanatory matrices. Adj. R square for the unique fraction [a] is 0.25. Does anyone know why the decomposition by hand using rda gives me a different result for [a] (constrained proportion is 0.32)? I used cbind() for the conditional fractions, but it should be similar to condition()? Thanks very much

I'm trying to perform a RDA analysis in the vegan package using the "varpart" function. I have a matrix of community structure data in different sites (rows), which I want to explain using 3 matrices of environmental data. The 3 matrices are: water quality parameters; local land use variables; total land use variables. In each matrix, the number of the rows is the same (the

Dear R help list members, I am experiencing difficulty in trying to generate a subscript '2' in an axis label. Although I can get the '2' into a subscript using expression(), R then forces me to leave at least one space between the '2' and the following character. My label is supposed to read 'N2O concentration (ppm)', and the space between the '2' and

hello, how can I do to drop C from this character "C325" ? _____________________________________________________________________________ [[alternative HTML version deleted]]

SUMMARY: Specific problem: I'm regridding biomass-burning emissions from a global/unprojected inventory to a regional projection (LCC over North America). I need to have boundaries for Canada, Mexico, and US (including US states), but also Caribbean and Atlantic nations (notably the Bahamas). I would also like to add Canadian provinces and Mexican states. How to put these together? General

Hello I am using Jari Oksanen's CCA routine from the Vegan package on some estuary data, following a technique applied in (Anderson, M.J. & Gribble, N.A., 1998, Partitioning the variation among spatial, temporal and environmental components in a multivariate data set, Australian Journal of Ecology 23, 158-167). Some steps in the process require that the dependent matrix be constrained by

Hi, I am trying to run the command "forward.sel.par," however I receive the error message: "Error: could not find function 'simpleRDA2'." I have the vegan library loaded. The documentation on "varpart" has not helped me to understand why I cannot call this function. Maybe I am missing something obvious because I am still an 'R' novice. Below is a

Hello, I am trying to estimate 4 parameters of a non-linear model using nls. My model function is a Fourier integral and is very expensive to calculate. I get the following error: > theta0 <- c(0.045, 1.02*10^(-4), 0.00169, 5.67*10^(-4)) > res <- nls(log(y) ~ log(model(theta,r,t)), data=dataModel, + start=list(theta=theta0), trace=TRUE, + control=nls.control(tol=1e-2))

Dear R-Users My problem is quite simple: I need to use a fitted model to predict the next point (that is, just one single point in a curve). The data was divided in two parts: identification (x and y - class matrix) and validation (xt and yt - class matrix). I don't use all values in x and y but only the 10 nearest points (x[b,] and y[b,]) for each regression (b is a vector with the

I am using the R statistical package in a pretty straightforward manner, just interested in getting summary statistics and reliable estimates of means, variances, confidence intervals, p-values using standard tests, etc. I've been using the generalized estimating equation package because I often deal with repeatedly sampled pairs of data from the same source, and my reading (and my