ovaskain
commented
2 years ago Hi,
It is quite common to have the full posterior concentrated to alpha=0 (note that half of the default prior mass is set to alpha=0). So the first guess is that the residual does not show spatial signal, i.e. “hab” explains all of that. You may try a “space only model” with XFormula = ~1 to see if the model picks up a spatial signal from the raw data (rather than from the residual).
O2
From: abigailcohen @.> Sent: tiistai 19. heinäkuuta 2022 20:14 To: hmsc-r/HMSC @.> Cc: Subscribed @.***> Subject: [hmsc-r/HMSC] All 0's in Alpha posterior? (Issue #146)
Hello,
I am attempting to predict distribution of a single species over a grid using the real-life case study in Ch.5 of the JDSM textbook as a template. This is currently more of a proof-of-concept, so I use one species at a time and use a linear model where habitat is the only predictive variable. However, when I use sampleMCMC to draw samples from the distribution, every single sample of the Alpha parameter is 0. I have tried the same model setup with a very common beetle and a less common bee species, so I don't think it's that the data itself has no spatial effect- and getting all 0's seems odd. I have tried centring the UTM coordinates (subtracted mean and divided by 1000) to reduce the integer size, and using more chains, but neither had any effect.
Here is the head of the bee data to give a sense of what it looks like. site X Y X.c Y.c hab B_nevadensis 10092 -1229321 1337016 -13.509709 -2.408190 Developed 1 10212 -1238714 1348803 -22.902527 9.379033 Developed 3 10348 -1258299 1350211 -42.487295 10.786970 Developed 0 10495 -1198551 1326071 17.260420 -13.352796 Developed 5 10558 -1219215 1330417 -3.403721 -9.007228 Crop 9 10781 -1203414 1319531 12.396961 -19.892887 Crop 9
The model is constructed like so: XData = data.frame(neva.example$hab) #predictor variable Y = as.matrix(neva.c$B_nevadensis) #response variable colnames(Y) = "B_nevadensis" xy.c = as.matrix(cbind(neva.c$X.c, neva.c$Y.c)) #coordinates site = as.factor(neva.c$BLID.x) studyDesign = data.frame(site = site) rownames(xy.c) = (studyDesign[,1]) #assign each site the corresponding coordinates rL.c = HmscRandomLevel(sData = xy.c) # create random spatial effect XFormula = ~ hab # linear regression formula for fixed effects
mFULL = Hmsc(Y=Y, XData = XData, XFormula = ~ hab, distr = "lognormal poisson", studyDesign = studyDesign, ranLevels = list("site"=rL.c)) #full model with fixed and random effects
Hmsc object with 156 sampling units, 1 species, 8 covariates, 1 traits and 1 random levels Posterior MCMC sampling with 4 chains each with 250 samples, thin 100 and transient 12500
I have also attached the summary plot of Alpha. [image]https://user-images.githubusercontent.com/109101526/179803381-11e09557-6acf-476d-b3a4-a03a10d68a4e.png
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abigailcohen
Hello,
I am attempting to predict distribution of a single species over a grid using the real-life case study in Ch.5 of the JDSM textbook as a template. This is currently more of a proof-of-concept, so I use one species at a time and use a linear model where habitat is the only predictive variable. However, when I use sampleMCMC to draw samples from the distribution, every single sample of the Alpha parameter is 0. I have tried the same model setup with a very common beetle and a less common bee species, so I don't think it's that the data itself has no spatial effect- and getting all 0's seems odd. I have tried centring the UTM coordinates (subtracted mean and divided by 1000) to reduce the integer size, and using more chains, but neither had any effect.
Here is the head of the bee data to give a sense of what it looks like.
The model is constructed like so: XData = data.frame(neva.example$hab) #predictor variable Y = as.matrix(neva.c$B_nevadensis) #response variable colnames(Y) = "B_nevadensis" xy.c = as.matrix(cbind(neva.c$X.c, neva.c$Y.c)) #coordinates site = as.factor(neva.c$BLID.x) studyDesign = data.frame(site = site) rownames(xy.c) = (studyDesign[,1]) #assign each site the corresponding coordinates rL.c = HmscRandomLevel(sData = xy.c) # create random spatial effect XFormula = ~ hab # linear regression formula for fixed effects
mFULL = Hmsc(Y=Y, XData = XData, XFormula = ~ hab, distr = "lognormal poisson", studyDesign = studyDesign, ranLevels = list("site"=rL.c)) #full model with fixed and random effects
Hmsc object with 156 sampling units, 1 species, 8 covariates, 1 traits and 1 random levels Posterior MCMC sampling with 4 chains each with 250 samples, thin 100 and transient 12500
I have also attached the summary plot of Alpha.