search for: grassland

...#39;t obtain any improvement. Please let me know, how I can correct this. Best regards Joerg > worms<-read.table("C:/Programme/R/R-2.5.0/Data/Worms.txt", header = T) > worms Field.Name Area Slope Vegetation Soil.pH Damp Worm.density 1 Oak.Mead 3.1 2 Grassland 3.9 F 2 2 Church.Field 3.5 3 Grassland 4.2 F 3 3 Ashurst 2.1 0 Arable 4.8 F 4 4 Field.Name Area Slope Vegetation Soil.pH Damp Worm.density 5 Nash's.Field 3.6 11 Grassland 4.1 F...

...atly should that be easier to work with.) > length(foo) [1] 314482 > foo[1:10] [1] Montane Chaparral Barren Red Fir Red Fir [5] Red Fir Red Fir Red Fir Red Fir [9] Red Fir Red Fir 39 Levels: Alpine-Dwarf Shrub Annual Grassland Aspen Barren ... White Fir > summary(foo) Alpine-Dwarf Shrub Annual Grassland Aspen 7402 0 582 Barren Bitterbrush Blue Oak-Foothill Pine 69111...

hello all R experts, how do I calculate the reliability between the two groups using the ICCs? I'll appreciate your reply, Thanks Sincerely, Supreet kaur, Biomedical research engineer, Nationwide Childrens Hospital, Columbus, OH (614)355-3509 [[alternative HTML version deleted]]

...rub =20 worms[rev(order(Worm.density)),] [!duplicated(Vegetation),] Field.Name Area Slope Vegetation Soil.pH Damp Worm.density 9 The.Orchard 1.9 0 Orchard 5.7 FALSE 9 16 Water.Meadow 3.9 0 Meadow 4.9 TRUE 8 10 Rookery.Slope 1.5 4 Grassland 5.0 TRUE 7 2 Silwood.Bottom 5.1 2 Arable 5.2 FALSE 7 4 Rush.Meadow 2.4 5 Meadow 4.9 TRUE 5 and here is the correct set of rows, but in the wrong order, using unique =20 worms[rev(order(Worm.density)),] [unique(Vegetation),]...

dear R-friends, i`ve got a large dataset of vegetation-samples with about 500 variables(=species) in the following format: 1 spec1 1 spec23 1 spec54 1 spec63 2 spec1 2 spec2 2 spec253 2 spec300 2 spec423 3 spec20 3 spec88 3 spec121 3 spec200 3 spec450 . . this means: sample 1 (grassland) with the species (=spec) 1, 23, 54, 63 is it possible to get a following data-structure for further analysis? 1 2 3 ...... spec1 1 1 0 spec2 0 1 0 spec3 ... spec253 0 1 0 ... spec450 0 0 1 with thanks from the snowy tirol helli

...e error-term right in aov(), in lme() it appears impossible to get as expected. I would be greatful for any help. My experiment aimed to identify whether two fixed factors (habitat type and soil type) affect the development of plants. I took soil from six random sites each of two types (arable and grassland) and transplanted them back into the sites of origin in such way that in each of the sites there were six pots containing arable soil and six pots of grassland soil, each containing a seedling. With aov(), I got the analysis as I expected, with habitat type tested against destination site, and soi...

...as field size or vegetation height. I have tried using the TREE package but the models produced seem too simplistic and discard most variables with the result that there is no predictive power in the result. I would expect that there will be interactions between variables eg. if the vegetation is grassland then the vegetation height variable will mediate the interaction, if the vegetation is arable then crop type will be more significant. Would it be possible to use GLM or GAM models for this type of predictive modelling? Any assistance would be greatly appreciated - it's several years since I...

...s which are classfied into ~2000 AREA's and ~750 STATE'S So my question is do AREA'S differ in the amount of grass they contain i.e does AREA A contain significantly more grass than AREA B? I have modelled this by area_grass <- gls(grass~AREA, correlation=corExp(form=~x+y), data = grassland I have set the contrasts to options(contrasts = c("contr.treatment", "contr.poly")) as there are no control groups. What i will get ( it is taking ages!) is AREA A: -0.12.... ** AREA B: 0.17....* AREA C.. So can i then say AREA A has significantly less grass than...

...ctor(habitat) > destination=factor(destination) > origin=factor(origin) > summary(aov(response~soiltype*habitat+Error(destination+origin))) > anova(lme(response~soiltype*habitat,random=~1|destination/origin)) > # > #"habitat" type is either 'arable' or 'grassland' > #"destination" indicates what site the soil was transplanted into, and is considered a random factor within habitat type > #"soiltype" is either 'arable' or 'grassland' > #"origin" indicates what site the soil was taken from, and is c...

...espace (and not attached): [1] gdata_2.4.2 grid_2.9.2 gtools_2.6.1 MASS_7.2-48 svMisc_0.9-5 Many thanks for any help or advice that anyone can provide :) Cheers, Zaneta ____________________________ Zaneta Park Biometrician T +64 6 351 8008 E zaneta.park-ng@agresearch.co.nz AgResearch Limited Grasslands Research Centre Tennent Drive, Private Bag 11008, Palmerston North, New Zealand T +64 6 356 8019 F +64 6 351 8032 www.agresearch.co.nz Farming Food and Health. First Te Ahuwhenua Te Kai me te Whai Ora. Tuatahi ======================================================================= Attention...

...this. I have been trying to use Adonis to find out if there are significant difference between groups on data that I have analyses with NMDS, and have been struggling with getting this to work and understanding what is going on. I am looking at diversity in different soils with either woodland or grassland habitats. I have run the scripts library(vegan) library(ecodist) library(MASS) mydata <- read.table("ash_data.csv", header=TRUE, sep=",", row.names="Site") envdata_fit <- read.table("ash_env.csv", header=TRUE, sep=",", row.names="Site&...

I'm working with large data frames and running out of memory. I hope some of you may be able to suggest a more efficient approach. I have grid/lattice data representing a time series of 1 m2 quadrats in a grassland: Each 1 cm2 cell or pixel contains one ecological state (ie grass or bare ground). The goal is to calculate, for each cell, the transition probabilities to all available states (given that a cell is occupied by grass, what are the probabilities it will change to grass or bare ground in the nex...

...eg*year, data=a, random = list(Site=pdDiag(~Veg), Plot=pdDiag(~Veg)) Dataset: a-data frame of daily measurements of soil temperature (Soil.temp) over six years Site (6 sites), Plot(2 plots per site), Veg(2 vegetation types: 2 sites as grassland, 4 sites as forest) M-month (categorical predictor) year (continues) Thanks, Julia P.S. I a sorry if this message showed up a few times, since I had trouble posting it. [[alternative HTML version deleted]]

TechTV News advises consumers of Open Vorbis being better then mp3's in both quality and at a reduced file size. (Tue Jul 23 00:09:18 EDT 2002) -- Roger ----- Verify my pgp/gnupg signature on my HomePage: http://www.alltel.net/~rogerx/about/index.html http://www.alltel.net/~rogerx/about/index.html <p>--- >8 ---- List archives: http://www.xiph.org/archives/ Ogg project homepage:

Hello! I'm a college undergrad desperately trying to finish up my thesis. I have a dataset on the distribution of a grassland bird from the Breeding Bird Survey. I have a very straightforward and simple version of the logistic growth model to describe changes in this bird's abundance over time. The variables are the natural birth rate, mortality, and carrying capacity. The equation itself looks like this: birds <-...

wow! i must say vorbis deprecates mp3! lol. although, i had to use the "OSS/3d for XMMS" plugin on my sblive value w/ 4 point sound to get a proper base. On my other box with a diff card, bass is just fine. however, are there any h/w players for vorbis files? the faq only states one manufacturer called iObjects but i see no portable players! Basically, i want to player vorbis files

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