![variation partitioning environment space time rcode rscript variation partitioning environment space time rcode rscript](https://i.ebayimg.com/images/g/-ekAAOSw3PRfydOw/s-l64.jpg)
The first hierarchical category is to identify hierarchies in space–time series data. Title( main = paste( "DRF ", colnames( ame( DRF_MEM $ best $ MEM.This paper provides a short overview of space–time series clustering, which can be generally grouped into three main categories such as: hierarchical, partitioning-based, and overlapping clustering. Title( main = paste( "DRF ", colnames( ame( DRF_MEM $ best $ MEM.select))), line = 3, outer = F, adj = 1) Sr_value( DRF_coord, ame( DRF_MEM $ best $ MEM.select), ylim = ylim, xlim = xlim, grid = F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble ") Title( main = paste( "SSF ", colnames( ame( SSF_MEM $ best $ MEM.select))), line = 3, outer = F, adj = 1) Sr_value( SSF_coord, ame( SSF_MEM $ best $ MEM.select), ylim = ylim, xlim = xlim, grid = F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble ") Title( main = paste( "LARGE SCALE ", colnames( ame( Broad_MEM $ best $ MEM.select))), line = 3, outer = F, adj = 1) Sr_value( Broad_coord, ame( Broad_MEM $ best $ MEM.select), ylim = ylim, xlim = xlim, grid = F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble ") < 0.05) the variation in species occurrences. Performed when the whole predictor matrix can significantly explain (p The function fs() from package adespatial. Significantly explain species occurrences. Similarly to removing variables with VIF higher than 3, I built aįunction to select the most important variables, when they could Plot( m1, m2, pch = 20, xlab = "distance ", ylab = "spatial weights ") #Delaunay triangulation (type 1) #Gabriel graph (type 2) #Relative neighbours (type 3) #Minimum spanning tree (type 4) #Neighbourhood by distance (type 5) #K nearests neighbours (type 6) #Inverse distances (type 7) dist_UBA <- dist( UBA_coord)ĭistnb <- spdep ::nbdists( nb, as.matrix( UBA_coord))Ī_fdown <- 5 a_f_up <- 0.5 fdist <- lapply( distnb, function( x) 1 - x /max( dist_UBA)) #linear fdist <- lapply( distnb, function( x) 1 - ( x /max( dist_UBA)) ^ a_fdown) #fdown fdist <- lapply( distnb, function( x) 1 / x ^ a_f_up) #fup lw <- nb2listw( nb, style = 'B ', zero.policy = TRUE, glist = fdist)
![variation partitioning environment space time rcode rscript variation partitioning environment space time rcode rscript](https://i.ebayimg.com/images/g/-m4AAOSw-11f0x1S/s-l64.jpg)
#PLOTING WEIGHTS # nb <- chooseCN(coordinates( UBA_coord), type = 1, plot.nb = FALSE) = "red ", main = paste( "NI - ",names( NI_MEM $ best.id)), plot = TRUE, labels = NULL) NI_MEM_FS <- NI_MEM $ best $ MEM.select adegraphics ::s.label( NI_coord, nb = candidates_NI], NI_MEM <- lect( NI_pa, candidates = candidates_NI, MEM.autocor = c( "positive "), method = c( "FWD "), IC_MEM_FS <- dbmem( IC_coord, MEM.autocor = c( "positive "), silent = TRUE)Ĭandidates_NI <- listw.candidates( NI_coord, style = "B ", nb = c( "del ", "rel ", "pcnm "), IC_MEM <- lect( IC_pa, candidates = candidates_IC, MEM.autocor = c( "positive "), method = c( "FWD "),
![variation partitioning environment space time rcode rscript variation partitioning environment space time rcode rscript](https://i.ebayimg.com/images/g/aLoAAOSw7iFgGSCA/s-l400.jpg)
# candidates_IC <- listw.candidates( IC_coord, style = "B ", nb = c( "del ", "rel ", "pcnm "), ST_MEM_FS <- dbmem( ST_coord, MEM.autocor = c( "positive "), silent = TRUE) ST_MEM <- lect( ST_pa, candidates = candidates_ST, MEM.autocor = c( "positive "), method = c( "FWD "), # candidates_BER <- listw.candidates( BER_coord, style = "B ", nb = c( "del ", "rel ", "pcnm "),īER_MEM <- lect( BER_pa, candidates = candidates_BER, MEM.autocor = c( "positive "), method = c( "FWD "),īER_MEM_FS <- dbmem( BER_coord, MEM.autocor = c( "positive "), silent = TRUE)Ĭandidates_UBA <- listw.candidates( UBA_coord, style = "B ", nb = c( "del ", "rel ", "pcnm "),
![variation partitioning environment space time rcode rscript variation partitioning environment space time rcode rscript](https://i.ebayimg.com/images/g/VTkAAOSw-g5c68RW/s-l64.jpg)
ITA_MEM_FS <- dbmem( ITA_coord, MEM.autocor = c( "positive "), silent = TRUE) MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE) ITA_MEM <- lect( ITA_pa, candidates = candidates_ITA, MEM.autocor = c( "positive "), method = c( "FWD "), Those are the Delauney triangulation, the Relative neighbour and the PCNM # candidates_ITA <- listw.candidates( ITA_coord, style = "B ", nb = c( "del ", "rel ", "pcnm "), Also We restricted our graph conectivity matrix to only three that yields relatively different scenarios of conectance. We only used linear weights as we do not believe that a exponential decay would make sense at this scale (even relative large distances between sites are actually small). #We restricted our options for optimization because of the low statistical power (low replication) that we had at our small spatial scale.