On 3/18/20 12:44 AM, Wilcox, Chris (O&A, Hobart) wrote:
Hi all,
I am trying to fit a model with a markov random field smooth in mgcv. I am having some trouble with getting it to run, and in particular I am getting the message Error in initial.sp(w * x, S, off) : S[[1]] matrix is not +ve definite. After reading everything I could find on mrf, it sounds like there was a bug that was brought up with Simon Wood in 2012, due to differences between windows and linux, with the linus machine stopping due to this error, while windows was not. I have not been able to find much else on it. Any suggestions would be much appreciated. There is reproducible code below. Thanks Chris library(mgcv) #create data
     Country <- 
as.factor(c("Australia","Australia","Australia","Australia","Australia","Australia","Bangladesh","Bangladesh","Bangladesh",
     
"Bangladesh","Bangladesh","Bangladesh","Cambodia","Cambodia","Cambodia","Cambodia","Cambodia","Cambodia",
     "China","China","China","China","China","China","East Timor","East Timor","East 
Timor",
     "East Timor","East Timor","East 
Timor","HighSeas1","HighSeas1","HighSeas1","HighSeas1","HighSeas1","HighSeas1",
     
"HighSeas2","HighSeas2","HighSeas2","HighSeas2","HighSeas2","HighSeas2","China","China","China","China","China","China",
     
"India","India","India","India","India","India","Indonesia","Indonesia","Indonesia","Indonesia","Indonesia","Indonesia",
     
"Malaysia","Malaysia","Malaysia","Malaysia","Malaysia","Malaysia","Myanmar","Myanmar","Myanmar","Myanmar","Myanmar",
     
"Myanmar","Philippines","Philippines","Philippines","Philippines","Philippines","Philippines","South
 Korea","South Korea",
     "South Korea","South Korea","South Korea","South 
Korea","China","China","China","China","China","China",
     "Sri Lanka","Sri Lanka","Sri Lanka","Sri Lanka","Sri Lanka","Sri 
Lanka","Taiwan","Taiwan","Taiwan","Taiwan",
     
"Taiwan","Taiwan","Thailand","Thailand","Thailand","Thailand","Thailand","Thailand","Vietnam","Vietnam","Vietnam","Vietnam",
     "Vietnam","Vietnam"))
Count <- c(0,0,3,5,1,5,0,0,0,0,0,1,0,0,0,0,0,3,0,0,2,1,0,6,0,0,0,1,0,0,0,1,0,0,0,0
     
,0,0,20,0,1,0,0,0,0,0,0,2,0,0,6,3,3,10,1,1,18,11,8,11,0,1,2,2,1,14,0,0,0,1,0,0
     
,0,0,4,3,9,16,0,0,3,0,0,1,0,0,1,0,0,0,0,0,33,18,8,16,0,0,0,0,0,2,0,1,14,6,8,2
     ,0,0,0,0,1,1)
Data <- data.frame(Count,Country)


I'm not seeing any spatial data being defined, so I'm puzzled by the expectation that this is yet a markov random field problem. You appear to be following the last part of the example code in ?smooth.construct.mrf.smooth.spec {mgcv} without constructing your data set to match the structure of the `columb` example dataset.

str(columb)

#------------------

'data.frame': 49 obs. of  8 variables:
 $ area      : num  0.3094 0.2593 0.1925 0.0838 0.4889 ...
 $ home.value: num  80.5 44.6 26.4 33.2 23.2 ...
 $ income    : num  19.53 21.23 15.96 4.48 11.25 ...
 $ crime     : num  15.7 18.8 30.6 32.4 50.7 ...
 $ open.space: num  2.851 5.297 4.535 0.394 0.406 ...
 $ district  : Factor w/ 49 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ x         : num  8.83 8.33 9.01 8.46 9.01 ...
 $ y         : num  14.4 14 13.8 13.7 13.3 ...


You are also committing a common R-beginner error in accessing columns of a data object directly in a formula while failing to use a data argument for a regression call.

--

David.

#create neighbour matrix
     NB <- list()
     NB$'East Timor' <- c(1,2,15)
     NB$Australia <- c(1,2,15)
     NB$'Sri Lanka' <-c(3,12,16)
     NB$Bangladesh <-c(4,12,13,16)
     NB$Philippines <- c(5,6,11,14,15,17)
     NB$Taiwan <- c(5,6,11)
     NB$Thailand <- c(7,8,10,12,13,14,15)
     NB$Vietnam <- c(7,8,10,11,14,15)
     NB$`South Korea` <- c(9,11)
     NB$Cambodia <- c(7,8,10)
     NB$China <- c(5,6,8,9,11,14)
     NB$India <- c(3,4,7,12,13,15,16)
     NB$Myanmar <- c(4,7,12,13,16)
     NB$Malaysia <- c(5,7,8,11,14,15)
     NB$Indonesia <- c(1,2,5,7,8,12,14,15)
     NB$HighSeas2 <- c(3,4,12,13,16)
     NB$HighSeas1 <- c(5,17)
#check levels and names match
     all.equal(sort(names(NB)), sort(levels(Data$Country)))
#try fitting GAM
     m1 <- gam(Data$Count ~ s(Data$Country, bs = 'mrf', xt = list(nb = NB)))
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