smnet (version 1.0)

network: Specify Network Smoother in Formulae

Description

This function specifies all of the information required to smooth parameters over the segments of a stream network using an adjacency matrix, and a vector of flow weights.

Usage

network(adjacency = NULL, weight = NULL, netID = 1, locs = NULL)

Arguments

adjacency

A sparse adjacency matrix describing the flow connections across the network. adjacency must be of class "spam", and is typically obtained from a call to get_adjacency.

weight

Either a numeric vector of flow weights or a character string indicating the column name of a numeric vector of flow weights contained in the data.object that has been passed to smnet. When a vector of flow

netID

Integer identifying the specific network of interest within the SSN object supplied to smnet. Ignored if the input data to smnet is a data.frame and not an SSN object.

locs

Either a character string referring to a column of stream segment locations inside the data.object that has been passed to smnet. Otherwise a numeric vector of stream segment locations, these should be arranged so that they correspond to the

Value

• A list combining the processed input components above. For internal use within smnet.
• adjacency
  Sparse adjacency matrix
• weight
  Numeric vector of flow weights
• netID
  Integer identifying network of interest
• locs
  Vector of stream segments locations

See Also

smnet, get_adjacency

Examples

# Set up an SSN object - this part taken 
# from the SSN:::SimulateOnSSN help file
library(SSN)
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")