search for: vegetational

Hello, I am trying to create multiple matrices (to run a PVA) but can't import all of them from a .csv without the numbers treated as labels and not factors. I can enter the matrix slowly: Site05_96 <- matrix(c(0.07,0,0.03,0.00,NA,0.00, 0.09,0.166666667,0.31,0.42,NA,0.00, 0.00,0,0.00,0.00,NA,0.00, 0.00,0,0.00,0.00,NA,0.00,

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

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

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

Hello All, I have a data frame (dput information below) with food item weight for fish species. I need to calculate the Mean proportion by weight of each food item for each specie, as show in solution data frame (dput information below). I use the ddply function (plyr package) in two steps. First calculate the proportion of weight for each individual: step1 = ddply (example, .(ID), transform,

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. >

Hello all, I'm new in R, and I have a data-frame like this (dput information below): Specie Fooditem Occurrence Volume 1 Schizodon vegetal 1 0.05 2 Schizodon sediment 1 0.60 3 Schizodon vegetal 1 0.15 4 Schizodon alga 1 0.05 5 Schizodon sediment 1 0.90 6 Schizodon

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

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

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.

I was wondering if anyone could help me with this, simple problem. I am essentially following the example on Hadley's webpage (http://had.co.nz/ggplot2/geom_errorbar.html), but it still doesn't make any sense to me. df <- data.frame(trt = factor(c("intact", "intact", "removed", "removed")), coon = c(0.093, 0.06, 0.057, 0.09), group =

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

I am trying to run an ANCOVA with defined error terms. Thus I have to use AOV and not lm. my response variable is proportion of mice paw prints on track plates. These plates were placed on plots that had vegetation and fruit manipulated to two levels each (present or absent), and were sampled monthly for 14 months (repeated measures). The fully crossed factor design was also blocked. My sample

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

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.

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

*Dear **Madam**/**Sir,* *What would happen if we irrigate the degraded land using the desalinated saline water in Minqin Oasis, Gansu Province, China? Can we prevent the disappearing of Minqin Qasis?* * * *Background:* *1. **The Minqin Oasis is surrounded by two deserts, Badain Jaran Desert and Tengger Desert. * *2. **Water is the root of ecological

...unction. As for standard RDA's I have different plots (lines, the dependent matrix) and for each plot a value for diversity, soil and vegetation (in columns, the environmental matrix). I additionally have different species for each plot (separated in columns). In standard RDA's for vegetational data you put your dependent variable (e.g. number of a specific species in the specific plot) below your species columns. Problem: I do not have vegetational data with values 0, 1,2, 3 for each plot and species. I have 6 species in total, but the plots have a predetermined diversity level (e....

Hi everybody, I know this might be very off topic and it took me quite a while to up my courage to post this?. But I remember a thread some time ago about how we can find the packages we need to do specific tasks in R if we don?t know before hand which ones actually do it. Now all the packages are listed alphabetically on the web site. Since I am not very advanced in writing my own functions I