similar to: inconsistent lm results with fixed response variable

Dear List, I have a richness data distributing across 20 N to 20 S latitude. (120 E-140 E longitude). I would like to draw the richness in the north hemisphere and a regression line in the plot (x-axis: latitude, y-axis: richness in the north hemisphere). The above demand is done using plot. Then, south hemisphere richness and regression are required to be generated using the same y-axis above

Hello, I have been trying to get an ANOVA table for a linear model containing a single nested factor, two fixed factors and a covariate: carbonmean<-lm(C.Mean~ Mean.richness + Diversity + Zoop + Diversity/Phyto + Zoop*Diversity/Phyto) where, *Mean.richness* is a covariate*, Zoop* is a categorical variable (the species), *Diversity* is a categorical variable (Low or High), and

Sorry about re-posting this, it never went out to the mailing list when I posted this to r-help forum on Nabble and was pending for a few days, now that I am subscribe to the mailing list I hope that this goes out: I've been a viewer of this forum for a while and it has helped out a lot, but this is my first time posting something. I am running glm models for richness and abundances. For

Dear all, I?m analyzing this dataset containing biodiversity indices, measured over time (Week), and at various contaminant concentrations (Treatment). We have two replicates (Replicate) per treatment. I?m looking for the effects of time (Week) and contaminant concentration (Treatment) on diversity indices (e.g. richness). Initial analysis with GAM models showed temporal autocorrelation of

Hi everyone I am new to the list and read all the instructions, hope i get it right. I have the following linear model: model_sqrt<-lm(sqrt(mortality)~richness +Acer_davidii+Ailanthus_altissima+Alniphyllum_fortunei +Betula_luminifera+Castanea_henryi+Castanopsis_carlesii +Castanopsis_eyrei+Castanopsis_fargesii+Castanopsis_sclerophylla

Hi, I'm analyzing a large number of large simulation datasets, and I've isolated one of the bottlenecks. Any help in speeding it up would be appreciated. `dat` is a dataframe of samples from a regular grid. The first two columns are the spatial coordinates of the samples, the remaining 20 columns are the abundances of species in each cell. I need to calculate the species richness in

Dear List, I am using the command plot to present the relationship between bird richness (Y axis) and elevation (X axis). However, I would like to observe the distributions of bird richness in different administrative areas (A, B, C, …., G) in this plot. For example, the dots in area A might fall in the upper right part of the plot, while those in area B might appear in the middle of

Dear List, I've around 1000 *.txt files, I've generate with other software. I've now done the following code (below). My question is how can I automate this (with do.call () ?), so it could be done for all the *.txt files. Thanks in advance, Rog??rio names<- list.files() file <- "BLU_Var_%04d.txt" for(i in 1:1000){

I am trying to suppress the tick labels on the x-axis of the following: barchart(richness[Wood=="V"]~Sample[Wood=="V"]) I have tried col.axis="white" I have tried removing the axis all together with axes=FALSE I have tried xaxt="n" I have also tried labels=c"(label1", "label2") to replace the default labels, based on reading that the

Dear Listers, I have a regression problem (x->y) with biological data, where x influences y in two ways, (1) y increases with x and (2) the variation around the mean (residuals) decreases with increasing x, i.e. y becomes more 'predictable' as x increases. The relationship is saturating, y~a + bx + cx^2, gives a very good fit. I know basically how to test for heteroscedasticity. My

Hi, I am new to this list. I have a question regarding including both spatial and temporal random factors in lmer. These two are not nested, and an example of model I try to fit is model1<-lmer(Richness~Y+Canopy+Veg_cm+Treatment+(1|Site/Block/Plot)+(1|Year), family=poisson, REML=FALSE), where richness = integer Y & Treatment = factor Canopy & Veg_cm = numerical, continous

Hi, I have made a plot with panels (attached) using R code (below) and I'd like to increase the size of each panel and decrease the white space, especially the white space between: 1. rows of panels 2. the top panel and its title (which contains info on r2 and N) 3. each panel and its x label. I've dug around in the plot help files but can't seem to find how to do this. Any help

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

Hi! I’m running glmer (lme4) models with biodiversity data and I’m having trouble with understanding/finding information on how the offset() option is implemented. Explicitly, I’m wondering if the offset is only implemented on the dependent variable (as I think it is), or does it also affect independent variables in the model (was told this by a stat guy at our department)? My data is

hello, i found it most convenient to use package contrast for planned comparisons on mixed models. for instance i have a model with 2 fixed factors, one with 4 levels (stage) and one with 2 levels (gap) and a nested random factor (site) and i tested gap within level A of factor stage: library(contrast) library(nlme) m1<-lme(rich ~ stage*gap, random=~1|site,data=richness) contrast(m1, a =

R-users, Attached is the file (SR_use2.txt) I'd like to include and includes column headers. nat_est is the response variable and is the number of species at a particular point. The other variables are the explanatory vars (vark, var2, var1, UK, U2, U1, GK, G2, G1, PK, P2, P1). Here is Walsh's sample code for hier.part: data(urbanwq) env <- urbanwq[,2,8] hier.part(urbanwq$lec,

I'm trying to make a glmm to identify the relationship between insect species richness with fragment size, isolation and time (different years). I already tried to analyse it using poisson distribution error, but I always face with the following warning: *glm.fit: fitted probabilities numerically 0 or 1 occurred * This is probably hapenning because my dataset has a lot of zeros. So, what

Hi everyone! I have a simple model that i would like to plot with 95% CIs. It is like follows: m1<-lmer(Richness~Grazing+I(Grazing^2)+(1|Plot),family=poisson) By using the effects package I get two plots, one for the linear term and one for the squared term. Q1: Can I get all in one? I.e. with one line for the whole model? Q2: Can I also visualize the random effects? I would be very happy for

Dear everyone, I am doing some work about species richness estimation. Nonparametric estimation (such as Chao1, Jacknife1) can be done just using function "specpool()" and "estimateR()" in package "vegan". The problem is that I can not found any functions for parametric estimation (such as MMMeans, MMruns, Michaelis-Menten). Do you know any function for doing this?

Hi all, I suppose this is a very simple question, but as I've lost already a bit of time with it, without being able to get what I wanted, I'm addressing the question to the group in the hope someone can help me. I pretend to plot the richness of herbaceous species (RichHN) as a function of time since remnant isolation (Isol) conditioned to the area of the remnant (fArea - this is a