smnet (version 1.0)

get_adjacency: Construct an Adjacency Matrix

Description

Builds a sparse adjacency matrix from a user specified SSN data directory, by extracting and processing the binaryID.db table. The resulting output of this function is typically a required input for fitting additive network models to SSN objects using the main smnet function.

Usage

get_adjacency(ssn_directory, net)

Arguments

ssn_directory

Required character string indicating the path to the location of the .ssn directory which contains the binaryID.db table

net

Integer specifying the particular stream network of interest within the SSN object. Defaults to 1.

Value

• Square sparse matrix of class "spam" with row and column dimension equal to the number of stream segments. If the i^th column has non-zero elements $j_1$ and $j_2$ then this indicates that $j_1$ and $j_2$ are direct upstream neighbours of i.

  If the $i^\textrm{th}$ column has sum 1, then this indicates that $i$ has only one upstream neighbour, and therefore no confluence lies between them; by default the spatial penalties treat these differently.

See Also

smnet

Examples

# Set up an SSN object - this part taken 
# from the SSN:::SimulateOnSSN help file
set.seed(101)
## simulate a SpatialStreamNetwork object
raw1.ssn <- createSSN(n = 100,
    obsDesign = binomialDesign(50), predDesign = binomialDesign(50),
  importToR = TRUE, path = paste(tempdir(),"/sim1", sep = ""))

## create distance matrices, including between predicted and observed
createDistMat(raw1.ssn, "preds", o.write=TRUE, amongpred = TRUE)

## extract the observed and predicted data frames
raw1DFobs <- getSSNdata.frame(raw1.ssn, "Obs")
raw1DFpred <- getSSNdata.frame(raw1.ssn, "preds")

## add a continuous covariate randomly
raw1DFobs[,"X1"] <- rnorm(length(raw1DFobs[,1]))
raw1DFpred[,"X1"] <- rnorm(length(raw1DFpred[,1]))

## add a categorical covariate randomly
raw1DFobs[,"F1"] <- as.factor(sample.int(3,length(raw1DFobs[,1]), replace = TRUE))
raw1DFpred[,"F1"] <- as.factor(sample.int(3,length(raw1DFpred[,1]), replace = TRUE))

## simulate Gaussian data
sim1.out <- SimulateOnSSN(raw1.ssn,
	ObsSimDF = raw1DFobs,
	PredSimDF = raw1DFpred,
	PredID = "preds",
	formula = ~ X1 + F1,
	coefficients = c(1, .5, -1, 1),
	CorModels = c("Exponential.tailup", "Exponential.taildown"),
	use.nugget = TRUE,
	use.anisotropy = FALSE,
	CorParms = c(2, 5, 2, 5, 0.1),
	addfunccol = "addfunccol")

## extract the ssn.object
sim1.ssn <- sim1.out$ssn.object

## extract the observed and predicted data frames, now with simulated values
sim1DFobs <- getSSNdata.frame(sim1.ssn, "Obs")
sim1DFpred <- getSSNdata.frame(sim1.ssn, "preds")

## store simulated prediction values, and then create NAs in their place
sim1preds <- sim1DFpred[,"Sim_Values"]
sim1DFpred[,"Sim_Values"] <- NA
sim1.ssn <- putSSNdata.frame(sim1DFpred, sim1.ssn, "preds")

# create the adjacency matrix for use with SmoothNetwork
adjacency<-get_adjacency(paste(tempdir(),"/sim1", sep = ""))

lmP<-smnet(formula = Sim_Values~1 +
          network(adjacency = adjacency, weight = "addfunccol", netID = 1), 
          data.object = sim1.ssn)
          
plot(lmP, type = "segments")
plot(lmP, type = "nodes")