R: multicollinearity issues using glib(), Bayesian Model Averaging (BMA-package) -

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i experiencing difficulties estimating bma-model via glib(), due multicollinearity issues, though have specified columns use. please find details below.

the data i'll using estimation via bayesian model averaging:

cij <- c(357848,766940,610542,482940,527326,574398,146342,139950,227229,67948,  352118,884021,933894,1183289,445745,320996,527804,266172,425046,  290507,1001799,926219,1016654,750816,146923,495992,280405,  310608,1108250,776189,1562400,272482,352053,206286,  443160,693190,991983,769488,504851,470639,  396132,937085,847498,805037,705960,  440832,847631,1131398,1063269,  359480,1061648,1443370,  376686,986608,  344014) n <- length(cij); tt <- trunc(sqrt(2*n)) <- rep(1:tt,tt:1); #row numbers: year of origin j <- sequence(tt:1) #col numbers: year of development k <- i+j-1 #diagonal numbers: year of payment #since k=i+j-1, have  leave out dummy in order avoid  multicollinearity k <- ifelse(k == 2, 1, k)

i want evaluate effect of i , j both via levels , factors, of course not in same model. since can decide include i , j factors, levels, or not include them @ , k either include level, or exclude, there total of 18 (3x3x2) models. brings following data frame:

x <- data.frame(cij,i.factor=as.factor(i),j.factor=as.factor(j),k,i,j) x <- model.matrix(cij ~ -1 + i.factor + j.factor + k + + j,x)  x <- as.data.frame(x[,-1])

next, via following declaration specify variables consider in each of 18 models. according me, no linear dependence exists in these specifications.

model.set <- rbind(   c(rep(0,9),rep(0,9),0,0,0),   c(rep(0,9),rep(0,9),0,1,0),   c(rep(0,9),rep(0,9),0,0,1),   c(rep(0,9),rep(0,9),1,0,0),   c(rep(1,9),rep(0,9),0,0,0),   c(rep(0,9),rep(1,9),0,0,0),   c(rep(0,9),rep(0,9),0,1,1),   c(rep(0,9),rep(0,9),1,1,0),   c(rep(0,9),rep(1,9),0,1,0),   c(rep(0,9),rep(0,9),1,0,1),   c(rep(1,9),rep(0,9),0,0,1),   c(rep(1,9),rep(0,9),1,0,0),   c(rep(0,9),rep(1,9),1,0,0),   c(rep(1,9),rep(1,9),0,0,0),   c(rep(0,9),rep(0,9),1,1,1),   c(rep(0,9),rep(1,9),1,1,0),   c(rep(1,9),rep(0,9),1,0,1),   c(rep(1,9),rep(1,9),1,0,0))

then call glib() function, telling select specified columns x according model.set.

library(bma) model.glib <- glib(x,cij,error="poisson", link="log",models=model.set)

which results in error

error in glim(x, y, n, error = error, link = link, scale = scale) : x matrix not full rank

the function first checks whether matrix f.c.r, before evaluates columns select x via model.set. how circumvent this, or there other way include 18 models in glib() function?

thank in advance.


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