similar to: composition of a matrix by matches between two data frames

Problem: I have a Set of samples each with a list of observed species (presence only). Data is stored in a excel spreadsheet and the columns (spl) have different numbers of observations (spcs). Now I want to organize the data in a species by sample matrix with presence/absence style in R. data style (in excel): spl_A spl_B spl_C spcs1 spcs1 spcs2 spcs2 spcs3 spcs3 spcs4 spcs5 spcs5 desired

Dear R users, I face to a nested pattern and despite the numerous examples in the help I am still confused. I sampled bugs in different habitats within sites which were within rivers themselves within different regions. The habitat correspond to different substrata (not systematically present in all sites). For rivers and sites, I have environemental variables (e.g. altitude and slope of

R-users, Background: I took roadside samples of avian species richness and I would like to model the relationship between species richness and habitat around my 500 + sample locations (in a file called ROADSIDE). However, one criticism is that roadside habitats do not represent habitats throughout the study area. I tend to disagree because I detect birds away from the roads and the pixel size

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Hi, I'm working on the codes below however every time I run them when they get to OpenBUGS I keep getting the error message: array index is greater than array upper bound for hab. Any help would be greatly appreciated, Suzie Codes: ungulate <- read.csv(file.choose ()) #ungulate ungulate <- as.matrix(ungulate);colnames(ungulate)<-NULL;rownames(ungulate)<-NULL

Hi, I'm currently working on the below codes however whenever I run it in openbugs it gives an error message saying: unknown type of logical function error pos 76. Any help would be appreciated. ## bugs code library(R2OpenBUGS) sink("C:/Users/CCF/Documents/Suzie Work/PTY Project/Waterhole Correction/ungulate.txt") cat(" model{ # hyperparameters # habitat effects for each

I am a beginner with R and have managed to use vegan to run a CCA on my data. I am evaluating the habitat relationships of select species in riparian areas around creeks before and after logging across three sampling intervals (pre-logging, two-years post-logging,. and 10 years post-logging). I sampled from 18 sites of 3 treatments (n = 6 sites for each treatment). When I plot my

Hi! Thanks for providing great help in R-related statistics. Now, however I'm stuck. I'm not a statistics person but I was recommended to use R to perform a nmds plot and PerMANOVA of my dataset. Sample(treatment) in the columns and species (OTU) in the rows. I have 4 treatments (Ambient Temperature, Ambient temperature+Low pH, High temperature, High temperature+low pH), and I have 16

Hi, I have three years of species presence/absence data for a set of ~100 ponds, with a list of associated habitat characteristics (park, vegetation, hydroperiod, etc.). The datasets differ slightly by year because some ponds were dry in some years and not in others. My goal is to look at habitat associations for a couple focal species. For each year, I have a data table that looks like

Hello, I have some conflicting output from an aov summary and tukey contrasts with a mixed effects model I was hoping someone could clarify. I am comparing the abundance of a species across three willow stand types. Since I have 2 or 3 sites within a habitat I have included site as a random effect in the lme model. My confusion is that the F test given by aov(model) indicates there is no

Dear listers, I am trying to assess variance components for a nested, mixed-effects model. I think I got an answer that make sense from R, but I have a warning message and I wanted to check that what I am looking at is actually what I need: my data are organized as transects within stations, stations within habitats, habitats within lagoons. lagoons: random, habitats: fixed the question is:

Hi, I am new in statistics and i manage to make the linear model analysis but i have some difficulties in explaining the results. Can someone help me explalinig the output of my linear model analysis ? My data are with 2 variables habitat (e,s) and treatment (a,c,p) with multiple trials within. Thank you in advance Call: lm(formula = a$wild ~ a$habitat/a$treatment/a$trial) Residuals: Min

I have two data sets with locations, X, Y of houses (df.house) and habitats(df.habitat), respectively. In each dataset, there are 3 replicates (Repeat). Because each replicate has different locations of houses and habitats, I would like to plot them in panels. I wrote something like this: mypanel<-function(x,y,subscripts,...){ panel.xyplot(x,y,pch=20) panel.xyplot(

I have been working on the following code but keep getting an err message. My current thinking is that the problem is on the indexing but do not know how to fix it. Any help please? ungulate <- read.csv("Ungulate.csv",row.names=1) ungulate <- as.matrix(ungulate);colnames(ungulate)<-NULL;rownames(ungulate)<-NULL habitat <- read.csv("Ungulate_vegetation.csv")

The package I am writing is for the Center for Tropical Forest Science, CTFS. This "center" is a collaboration of 15+ institutions world wide that are investigating properties of tropical forest dynamics, species diversity, species distributions. The investigation is composed of the same sampling design of the forest: a large 50 hectare plot (usually) in which every tree >= 10 mm in

Dear All, I am trying to model the habitat mapping of 4 sharks species and my data are zero inflated and positively skewed. The zero percentages in my data vary between 56-77%. I did some tests fitting GAMs with negative binomial family (with theta between 1-10). Is this the best way to go? Some people have used VGAM (with zero inflated negative binomial) but my concern is that using the VGAM

Thanks for your answers Stephen and Ben, I hope I am posting on the correct list now. I managed so far to run the multinomial model with random effect with the following command: MCMCglmm(fixed=cbind(Apsy,Mygl,Crle,Crru,Miag,empty) ~ habitat:trait,random=~idh(trait):mesh,family="multinomial12", data=dataA,rcov=~trait:units) (where multiple responses are different species, Habitat

Dear all, I make habitat suitability models for animal species. The purpose of my research is to investigate the accuracy of different models. I clearly have a nested design: - accuracy_measure -> response variable - 2 model types (model_type) -> fixed effect - 230 species (species) -> random effect - 10 replicates/species (replicate) -> random effect - 10 subreplicate/replicate

Hi All, I'd like to be able to specify the ordered composition of several functions. So I defined compose: compose <- function(f,g){ function(x){f(g(x))} } and some simple test functions: plus2 <- function(x){x+2} plus3 <- function(x){x+3} plus4 <- function(x){x+4} > (compose(plus2,compose(plus3,plus4)))(3) [1] 12 Works fine. But trying to reduce a list of functions by

Dear all, I would like to add mixed effects in a multinomial model and I am trying to use MCMCglmm for that. The main problem I face: my data set consits of a trapping data set, where the observation at eah trap (1 or 0 for each species) have been aggregated per traplines. Therefore we have a proportion of presence/absence for each species per trapline. ex: ID_line mesh habitat Apsy Mygl