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Here is an approach for comparing two methods of adjusting branch lengths on trees: bladj in the program Phylocom and a fxn written by Gene Hunt at the Smithsonian. Get the code and example files (tree and node ages) at https://gist.github.com/938313 Get phylocom at http://www.phylodiversity.net/phylocom/ Gene Hunt’s method has many options you can mess with, including setting tip ages (not available in bladj), setting node ages, and minimum branch length imposed. You will notice that Gene’s method may be not the appropriate if you only have extant taxa. ...

A while back I posted some messy code to run Phylometa from R, especially useful for processing the output data from Phylometa which is not easily done. The code is still quite messy, but it should work now. I have run the code with tens of different data sets and phylogenies so it should work. I fixed errors when parentheses came up against numbers in the output, and other things. You can use the code for up to 4 levels of your grouping variable. In addition, there are some lines of code to plot the effect sizes with confidence intervals, comparing random and fixed effects models and phylogenetic and traditional models. ...

Bio-ORACLE A new dataset available of geophysical, biotic and climate data. Should be fun to play with in R.

I posted last week a simple function to plot networks using ggplot2 package. Here is version 2. I still need to work on figuring out efficient vertex placement. Changes in version 2: You have one of three options: use an igraph object, a matrix, or a dataframe (matrices will be converted to data frames within the function) If you have data on food webs similar to that provided in the Takapoto dataset provided in the NetIndices package, you can set trophic = “TRUE”, and gggraph will use the function TrophInd to assign trophic levels (the y axis value) to each vertex/node. You have to provide additional information along with this option such as what the imports and exports are, see NetIndices documentation. I added some simple error checking. if using method=“df” and trophic=“FALSE”, x axis placement of vertices is now done using the function degreex (see inside the fxn), which sorts vertices according to their degree (so the least connected species are on the left of the graph; note that species with the same degree are not stacked on the y-axis because e.g., two vertices of degree=5 would get x=3 then x=4). # ggraph Version 2 require(bipartite) require(igraph) require(ggplot2) # gggraph, version 3 g = an igraph graph object, a matrix, or data frame # vplace = type of vertex placement assignment, one of rnorm, runif, etc. # method = one of 'df' for data frame, 'mat' for matrix or 'igraph' for an # igraph graph object trophic = TRUE or FALSE for using Netindices # function TrophInd to determine trophic level (y value in graph) # trophinames = columns in matrix or dataframe to use for calculating # trophic level import = named or refereced by col# columns of matrix or # dataframe to use for import argument of TrophInd export = named or # refereced by col# columns of matrix or dataframe to use for export # argument of TrophInd dead = named or refereced by col# columns of matrix # or dataframe to use for dead argument of TrophInd gggraph <- function(g, vplace = rnorm, method, trophic = "FALSE", trophinames, import, export) { degreex <- function(x) { degreecol <- apply(x, 2, function(y) length(y[y > 0])) degreerow <- apply(x, 1, function(y) length(y[y > 0])) degrees <- sort(c(degreecol, degreerow)) df <- data.frame(degrees, x = seq(1, length(degrees), 1)) df$value <- rownames(df) df } # require igraph if (!require(igraph)) stop("must first install 'igraph' package.") # require ggplot2 if (!require(ggplot2)) stop("must first install 'ggplot2' package.") if (method == "df") { if (class(g) == "matrix") { g <- as.data.frame(g) } if (class(g) != "data.frame") stop("object must be of class 'data.frame.'") if (trophic == "FALSE") { # data preparation from adjacency matrix temp <- data.frame(expand.grid(dimnames(g))[1:2], as.vector(as.matrix(g))) temp <- temp[(temp[, 3] > 0) & !is.na(temp[, 3]), ] temp <- temp[sort.list(temp[, 1]), ] g_df <- data.frame(rows = temp[, 1], cols = temp[, 2], freqint = temp[, 3]) g_df$id <- 1:length(g_df[, 1]) g_df <- data.frame(id = g_df[, 4], rows = g_df[, 1], cols = g_df[, 2], freqint = g_df[, 3]) g_df_ <- melt(g_df, id = c(1, 4)) xy_s <- data.frame(degreex(g), y = rnorm(length(unique(g_df_$value)))) g_df_2 <- merge(g_df_, xy_s, by = "value") } else if (trophic == "TRUE") { # require NetIndices if (!require(NetIndices)) stop("must first install 'NetIndices' package.") # data preparation from adjacency matrix temp <- data.frame(expand.grid(dimnames(g[-trophinames, -trophinames]))[1:2], as.vector(as.matrix(g[-trophinames, -trophinames]))) temp <- temp[(temp[, 3] > 0) & !is.na(temp[, 3]), ] temp <- temp[sort.list(temp[, 1]), ] g_df <- data.frame(rows = temp[, 1], cols = temp[, 2], freqint = temp[, 3]) g_df$id <- 1:length(g_df[, 1]) g_df <- data.frame(id = g_df[, 4], rows = g_df[, 1], cols = g_df[, 2], freqint = g_df[, 3]) g_df_ <- melt(g_df, id = c(1, 4)) xy_s <- data.frame(value = unique(g_df_$value), x = rnorm(length(unique(g_df_$value))), y = TrophInd(g, Import = import, Export = export)[, 1]) g_df_2 <- merge(g_df_, xy_s, by = "value") } # plotting p <- ggplot(g_df_2, aes(x, y)) + geom_point(size = 5) + geom_line(aes(size = freqint, group = id)) + geom_text(size = 3, hjust = 1.5, aes(label = value)) + theme_bw() + opts(panel.grid.major = theme_blank(), panel.grid.minor = theme_blank(), axis.text.x = theme_blank(), axis.text.y = theme_blank(), axis.title.x = theme_blank(), axis.title.y = theme_blank(), axis.ticks = theme_blank(), panel.border = theme_blank(), legend.position = "none") p # return graph } else if (method == "igraph") { if (class(g) != "igraph") stop("object must be of class 'igraph.'") # data preparation from igraph object g_ <- get.edgelist(g) g_df <- as.data.frame(g_) g_df$id <- 1:length(g_df[, 1]) g_df <- melt(g_df, id = 3) xy_s <- data.frame(value = unique(g_df$value), x = vplace(length(unique(g_df$value))), y = vplace(length(unique(g_df$value)))) g_df2 <- merge(g_df, xy_s, by = "value") # plotting p <- ggplot(g_df2, aes(x, y)) + geom_point(size = 2) + geom_line(size = 0.3, aes(group = id, linetype = id)) + geom_text(size = 3, hjust = 1.5, aes(label = value)) + theme_bw() + opts(panel.grid.major = theme_blank(), panel.grid.minor = theme_blank(), axis.text.x = theme_blank(), axis.text.y = theme_blank(), axis.title.x = theme_blank(), axis.title.y = theme_blank(), axis.ticks = theme_blank(), panel.border = theme_blank(), legend.position = "none") p # return graph } else stop(paste("do not recognize method = \"", method, "\";\nmethods are \"df\" and \"igraph\"", sep = "")) } # Eg library(NetIndices) data(Takapoto) gggraph(Takapoto, vplace = rnorm, method = "df", trophic = "TRUE", trophinames = c(8:10), import = "CO2", export = c("CO2", "Sedimentation", "Grazing")) ...

I have been looking around on the web and have not found anything yet related to using ggplot2 for making graphs/networks. I put together a few functions to make very simple graphs. The bipartite function especially is not ideal, as of course we only want to allow connections between unlike nodes, not all nodes. These functions do not, obviously, take full advantage of the power of ggplot2, but it’s a start. ...

A post over at the Phased blog (http://www.nasw.org/users/mslong/) highlights a recent paper in PLoS One by Robert Warren et al. Similar results were obtained in a 2007 Ecology Letters paper by Nekola and Brown, who showed that abundance distributions found in ecology are similar to those found for scientific citations, Eastern North American precipitation, among other things. A similar argument was made by Nee et al. in 1991 (in the journal PRSL-B). The author of the blog appears to agree with the outcome of the Warren et al. study. ...

UPDATE: I guess it still is not actually available. Bummer… Has anyone used cloudnumbers.com? http://www.cloudnumbers.com/ They provide cloud computing, and have built in applications, including R. How well does it work? Does it increase processing speed? I guess it may at the least free up RAM and processor space on your own machine.

UPDATE: At the bottom are two additional methods, and some additions (underlined) are added to the original 5 methods. Thanks for all the feedback… -Also, another post here about ordered-categorical data -Also #2, a method combining splom and hexbin packages here, for larger datasets In data analysis it is often nice to look at all pairwise combinations of continuous variables in scatterplots. Up until recently, I have used the function splom in the package lattice, but ggplot2 has superior aesthetics, I think anyway. ...

They have online seminars that you can join in on live, and watch later as recorded videos. Check it out at phyloseminar.org