smnet (version 1.0)

predict.smnet: Predict From a Stream Network Model.

Description

Get predictions and standard errors at a fixed set of spatial locations and covariate values from a model fitted by smnet.

Usage

## S3 method for class 'smnet':
predict(object, newdata =  NULL,...)

Arguments

object

Object of class smnet, usually the result of a call to smnet.

newdata

New design matrix at which to make predictions, not required if the input data to smnet was an SSN object with an associated set of prediction points.

...

other arguments passed to predict.smnet

Value

• predictions
  Vector of predictions corresponding to prediction points in the SSN input object
• predictions.se
  Vector of prediction standard errors

See Also

smnet

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 = ""))

# fit the model using smnet
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")

# make some predictions
z<-predict(lmP)