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Dear R-help listers, Is anyone aware of a function that outputs dendrogram ultrametrics? Cheers, Lisa. PS please reply to me personally as well as to the list because the website wasn't letting me subscribe for some reason. thanks... Lisa Holman Research Officer, Vegetation Dynamics Policy & Science Division NSW Department of Environment & Conservation PO Box 1967, Hurstville 2220.

I'm not sure if this is a bug, or if I'm doing something wrong. =20 =46rom the worms dataframe, which is at in a file called worms.txt at =20 http://www.imperial.ac.uk/bio/research/crawley/therbook <http://www.imperial.ac.uk/bio/research/mjcraw/therbook/index.htm>=20 =20 the idea is to extract a subset of the rows, sorted in declining order of worm density, with only the maximum

First I would like to say thank you for taking the time to read it.Here is my problem. I am running a lmer analysis for binary longitudinal (repeated measures) data. Basically, I manipulated fruits and vegetation to two levels each(present and absent) and I am trying to access how these factors affect mice foraging behavior. The design consist of 12 plots, divided in 3 blocks. So each block

Would anybody be able to advise on which package would offer the best approach for producing a model able to predict the probability of species occupation based upon a range of variables, some of them catagorical (eg. ten soil types where the numbers assigned are not related to any qualitative/quantitative continuum or vegetation type) and others continuous such as field size or vegetation height.

Dear r-helpers, I estimated a generalized additive model (GAM) using Hastie's package GAM. Example: gam1 <- gam(vegetation ~ s(slope), family = binomial, data=aufnahmen_0708, trace=TRUE) pred <- predict(gam1, type = "response") vegetation is a categorial, slope a numerical variable. Now I want to assess the accurancy of the model using k-fold cross validation. I found the

Hello all, I am going slightly mad trying to create a table for running co-correspondence analysis. What I have is seed bank and vegetation data, and my aim is to see if the vegetation found in a site (containing several seed bank samples) can predict the composition of a seed bank sample within that site. So for this I need two tables with matching rows. I have created an empty matrix,

Hi, This is a question regarding technique rather than an R specific issue. I have been asked to evaluate a 30+ year long term continuous survey of bird presence/absence data that has an associated ocular estimate of the vegetation community percent coverage. The data are organized by subpopulations (5), and by year ( 1991 - present). We are interested in gaining understanding on whether bird

I am trying to fit a formula to my data, but I just can't find the right way to do it. My experiment consists of manipulating FRUITS and VEGETATION to two levels each(intact or removed) on 12 experimental plots. This leaves me with 4 treatment combinations Fruit intact Vegetation removed Fruit int. Veget int. Fruit rem. Veget rem. Fruit rem. Veget. intac those treatements are distributed

Dear R users, I know, the topic is more related to the r-sig-ecology. I decided to post it to the r-help as some specific topics of my question deals with NA-values and RDA (R vegan) and an adaptated RDA code due to a specific study design (including a second matrix). I am calculating a RDA for a dependent matrix (different variables for tree performance) and different explanatory

Hi everyone. I am trying to add a formula to my data using the groupedData function. My experiment consists of randomized block design using fruits, vegetation and time as factors. The idea is to see if fruits, vegetation and time explain the abundance of mice. I am using tree density as a covariate. So I tried to fit the following structure to my data. >

Hi R listers, I am trying to group distances of nests to the vegetation into classes that are define by (0-5m, 6-10m, 11-15m, 16-20m, 21-25m, 26-30m, 31-35m, 36-40m, 41-45m, 46-50m, 51-55m, 56-60m). Each row is a nest and all the distances to the vegetation is in a column. In plyr, I have tried - below script but I think I am going about this the wrong way and am not successful. #Veg index

Hi, everyone, I haven't found anything similar in the forum, so here's my problem (I'm no expert in R nor statistics): I have a data set of 59.000 cases with 9 variables each (fractional coverage of 9 different plant types, such as deciduous broad-leaved temperate trees or evergreen tropical trees etc.), which was generated by a vegetation model. In order to evaluate the quality of

A colleague has just passed me this interesting address: http://cc.oulu.fi/~jarioksa/softhelp/vegan.html "Vegan: R functions for vegetation ecologists" "Vegan package is intended to help vegetation ecologists and other community ecologists to use R. It is not a completely self-contained package, but it complements other R functions. .../... At this first stage, vegan implements

Hello all, I am currently working with rpart to classify vegetation types by spectral characteristics, and am comming up with poor classifications based on the fact that I have some vegetation types that have only 15 observations, while others have over 100. I have attempted to supply prior weights to the dataset, though this does not improve the classification greatly. Could anyone supply some

Hi, I hope someone knows the answer to this or has a real good reference about it (I am using Legendre & Legendre, Numerical Ecology, 1998).... My data is a data.frame with locations as rows and vegetation assemblages / species as columns. I've done a PCA, a correspondance analysis (CA) using ca in ca package and a detrended correspondance analysis (DCA) using decorana from vegan package.

Dear R users I am currently investigating time series analysis using an irregular time series. Our study is looking at vegetation change in areas of alien vegetation growth after clearing events. The irregular time series is sourced from Landsat ETM+ data, over a six year period I have 38 scenes. For certain periods I have monthly data while for others, images are up to three months apart. So far

Hi, For my master thesis I have 24 micro-plots on which I did measurements during 3 months. The measurements were: - Rainfall and runoff events throughout 3monts (runoff being dependant on the rainfall, a coefficient (%) has been made per rainfall event and per 3 months) - Soil texture (3 different textures were differentiated) - Slope (3 classes of slopes) - Stoniness (one time measurement)

Hi, Thanks for reading this mesage! I have created a GLM (using the quasipoisson family) and am now trying to simplify it. One of my explanatory variables is categorical (vegetation type, with 6 different levels). In the model, 5 of the 6 levels are significant and one is not. How should I simplify my model? Do I need to take out the whole category (i.e. all of vegetation type), or just the

Dear r-helpers! I want to classify my vegetation data with hierachical cluster analysis. My Dataset consist of Abundance-Values (Braun-Blanquet ordinal scale; ranked) for each plant species and relev?. I found a lot of r-packages dealing with cluster analysis, but none of them is able to calculate a distance measure for ranked data. Podani recommends the use of Goodman and Kruskals' Gamma for

Dear R-helpers, Apologies in advance for this (probably) simple question. I've searched the R Archive and can't seem to find a solution to my problem. I have a data frame of vegetation quadrat data with the following format: Q S C 1 A 5 1 B 10 1 C 50 1 D 10 2 A 20 2 E 10 2 C 40 3 D 5 3 F 1 3 G 5 3 B 75 Where Q is the sample (vegetation quadrats), S is the species and C is the