I am not sure what this means, and Ideally just need to fix this problem. Obviously, I have validated my model e.t.c, and my supervisor has suggested that the problem is due to over specification of my model.
Predictions are formed as an extra process after the final iteration and they are primarily used for generating tables of adjusted means for all levels of a given model factor. Pred1 <- predict(sch1, classify = "Malaria.RDT:Vil:Age:wealth:water.score:Canoe:Bed.or.matress:what.is.bilharzia:what.is.malaria:d", The predict.asreml () command in ASReml-R forms a linear function of the vector of fixed and random effects to obtain a predicted value for a factor of interest. during prediction as well as the influence of aliased parameters on prediction. What.is.bilharzia+what.is.malaria+d,įamily = asreml.gaussian(link = "identity"),įrom this model, I have tried to predict Malaria, however, when I use the predict function ASReml produces NA values. the package ASReml and is also used by GENSTAT, and is available in. Butler, D 2009, asreml: asreml() fits the linear mixed model, R package version. Sch1 <- asreml(fixed = sqrt(Schisto.elisa) ~ Malaria.RDT+Vil+Age+ library(asreml) dat Mu <-1 Create pseudo intercept to obtain estimate for Mu Genotype as random effect: g.ran <-asreml(fixed yield -1 + Mu + rep, random gen + rep: block, data dat, ran.order ' user ') Force 'gen' as first random effect in asreml object - makes BLUP extraction easier Genotype as fixed effect: g.fix <-asreml. Genotypes were ranked according to predicted breeding values (PBVs). My question is: What is the relationship between malaria and schistosomiasis?