search for: abiot

I'm trying to run an SEM, but I keep getting the following error message. In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. I have 4 latent variables (plant, AMF, abiotic, and soilAgg) with 2 or 4 indicator variables for each latent variable.My model is specified as: > mod.AL.5 <- specify.model() 1: plant -> tnCSG, plantCSG, .6 2: plant -> tnDIV, plantDIV, NA 3: plant -> tnFRL, plantFRL, NA 4: plant -> tnCRL, plantCRL, NA 5: AMF -> tnHL, AMFH...

...;All_animals.txt", sep=""),header=T) collect.results <- function(x) { #resets vectors which will be filled i <- 0 AICA <- NA; #put models names hierarchically in vector modelnames <- c("1=global", "2=biotic1", "3=biotic2", "4=abiotic") #keep track of changes in model names and number for (i in 1:length(modelnames)) #model structure of the four models given for all models to run #global modelstructure <- c( "ZlogHRS ~ ZRi+ZE+ZPROX_MN+ZED+ZAlwd+ZT2+ZW+ZN+Sex+y", #biotic1 "ZlogHRS ~ ZRi", #bi...

...data matrix orientation (i.e. looking at differences among samples (columns) based on variables (rows) vs. differences among variables (columns) based on samples(rows))? Thank you, Colin Wahl Graduate student, Western Washington University code & background: I am looking at the ordination of abiotic stream variables between different sampling locations. abiot.pca=prcomp(all24[, c(10, 13:18)], retx=TRUE, center=TRUE, scale= TRUE) summary(abiot.pca) Importance of components: PC1 PC2 PC3 PC4 PC5 PC6 PC7 Standard deviation 1.5925 1.0...

hello dear list! as I am currently helping someone with their statistical analysis of a count survey, I stumbled upon a very basic question upon model optimization: when fitting a model like: model<-lmer(abundance~(x+y+z)^3,family=poisson,...) in which x,y,z are continuous abiotic parameters such as po4 concentration, no2-concentration, which terms / interaction terms would you recommend removing FIRST? the ones of lowest significance (i.e. the ones with highest p-value) OR the ones with the most complex interaction structure (even though p-values may be low-ish)? an...

Dear all, I'm trying to fit a model on ecological data in which I have measured a few biotic and abiotic factors over the course of a few days in several individuals. Specifically, I'm interested in modelling y ~ x1, with x2, x3, and 'factor' as independent variables. Because data suggests both slope and intercept (for y ~x1) might differ between individuals, I'd want to compare mode...

I've got a small problem. I have some observational data (environmental samples: abiotic explanatory variable and biological response) to which I've fitted both a multiple linear regression model and also a gam (mgcv) using smooths for each term. The gam clearly fits far better than the lm model based on AIC (difference in AIC ~ 8), in addition the adjusted R squared for the gam...

Dear list, I am relatively new to ordinal models and have been working through the example given by Frank Harrell in the predict.lrm {Design} help All of this makes sense to me, except for the responses, i,e how do i interpret them? i would be extremely grateful if someone could explain the results? First i establish the date and model - > y <- factor(sample(1:3, 400, TRUE), 1:3,