SimulateOnSSN: Simulating Data on Spatial Stream Networks

In SSN: Spatial Modeling on Stream Networks

Simulating Data on Spatial Stream Networks

Description

This function works on objects of class SpatialStreamNetwork to simulate data with spatially autocorrelated errors from models as described in Ver Hoef and Peterson (2010) and Peterson and Ver Hoef (2010). It works with simulated or real stream networks. It can simulate from Gaussian (normal), Poisson and binomial distributions.

Usage

SimulateOnSSN(ssn.object, ObsSimDF, PredSimDF = NULL, PredID = NULL,
  formula, coefficients,
  CorModels = c("Exponential.tailup", "Exponential.taildown", "Exponential.Euclid"),
  use.nugget = TRUE, use.anisotropy = FALSE,
  CorParms = c(1, 10000, 1, 10000, 1, 10000, 0.1),
  addfunccol = NULL, useTailDownWeight = FALSE, family = "Gaussian", mean.only=FALSE)

Arguments

ssn.object	an object of class SpatialStreamNetwork
ObsSimDF	a data frame used to replace the existing observed sites data frame in ssn.object. It is safest to first extract the point.data data.frame from ssn.object, then add covariate values to the extracted data.frame. See the examples section.
PredSimDF	a data frame used to replace the existing prediction site data frame in ssn.object It is safest to first extract the point.data data.frame from ssn.object, then add covariate values to the extracted data frame. See the examples section. The covariate names should match those from ObsSimDF.
PredID	a string representing the ID (name) of the prediction slot in the ssn.object. The ith name is accessed using the call ssn.object@predpoints@ID[i].
formula	a one-sided formula to the right of, and including, the ~. This is similar to linear model formula but works in reverse. It will create a design matrix based on the formula and covariates in the ObsSimDF and PredSimDF.
coefficients	a vector of numeric values representing the coefficients. The formula creates the design matrix, and these coefficients are multiplied by the columns in the design matrix. If the design matrix is X, and coefficients are beta, then the mean values are created as X %*% beta. Note that this presumes some knowledge about how R will create design matrices from formulas.
CorModels	a character vector of spatial autocorrelation model names for stream networks. The individual models should be of different "types". It can be "NULL" for a non-spatial model, or it can contain any of the tailup models: "Exponential.tailup" (default), "LinearSill.tailup", "Spherical.tailup", "Mariah.tailup", and/or one of the taildown models: "Exponential.taildown" (default), "LinearSill.taildown", "Spherical.taildown", "Mariah.taildown", or one of the Euclidean distance models: "Spherical.Euclid", "Gaussian.Euclid", "Exponential.Euclid" (default), "Cauchy.Euclid". The 4 tailup and taildown models are described in Ver Hoef and Peterson (2010) and the 4 Euclidean distance models are standard spatial autocorrelation models. If this is NULL, then use.nugget = TRUE will impose independence between observations, or a classical non-spatial linear model.
use.nugget	logical. Add a nugget effect, default is TRUE. This can be thought of as a variance component for independent errors, adding a variance component only along the diagonal of the covariance matrix.
use.anisotropy	logical. Use anistropy for the Euclidean distance based spatial model in CorModels. Not implemented at the current time.
CorParms	a vector of numeric covariance parameters. Each of the CorModels will generally have two parameters, a partial sill and a range (in that order, and in the order as specified by CorModels). If use.nugget = TRUE, then a final CorParms parameter should be added for the nugget effect.
addfunccol	for the tailup models, weights are need to be used to account for dendritic branching in the network. This is achieved using an additive function and is described in Ver Hoef and Peterson (2010). The name of the variable in the ssn.object that is to be used to define weights should be given here. See example below.
useTailDownWeight	Use weighting in the tail-down models in the same way as for tail-up models. Logical that defaults to FALSE.
family	the error distribution and link function to be used in the model. This is a character string that is either "Gaussian" (default), "Poisson", or "Binomial."
mean.only	Logical that defaults to FALSE.

Details

Models are specified symbolically in a manner similar to lm and other model-fitting functions in R, but here the formula is right-handed (e.g. ~ x1 + x2 + x3, where x1, x2, x3 are the 'terms'). If the formula is specified as ~ terms, data will be simulated as Sim_Values ~ terms, where Sim_Values is the (numeric) response vector and terms is a series of fixed effect linear predictors for Sim_Values. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second. See model.matrix for additional details. The terms in the formula are re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on. A formula has an implied intercept term. To remove this use either ~ x - 1 or ~ 0 + x. See formula for more details about allowable formulae.

The observed data data.frame used for simulating is contained in the slot ssn.object@obspoints@SSNPoints[[1]]@point.data and can be easily accessed using getSSNdata.frame. The function putSSNdata.frame can be used to to put it back after it has been modified. Likewise, the predicted data data.frame used for simulating is contained in stored in ssn.object@predpoints@SSNPoints[[i]]@point.data, where i is the ith prediction data set within ssn.object; generally i = 1, but is not a limit on the number of prediction datasets that may be included. Calls to getSSNdata.frame and putSSNdata.frame may be used to access the prediction site data.frames as well.

Value

Output from SimulateOnSSN contains three list items.

ssn.object	the input SSN that now has simulated data in the observed and/or prediction data.frames. Within these data.frames, the simulated data have a column heading called "Sim_Values"
FixedEffects	a data.frame of the ordered column names for the design matrix that was created. The first column is the column name of the design matrix, and the second column is the coefficient used for that fixed effect for simulation. This can be used to ensure that the coefficients are being used in the way that they were intended.
CorParms	a data.frame of the ordered variance component model parameters. No matter the order of the Corparms input argument, the covariance parameters are applied in the follwoing order, if specifed in CorParms: tailup model (partial sill then range), taildown (partiall sill then range), Euclidean model (partial sill then range), random effects variance components ordered alphanumerically, and finally the nugget. This can be used to ensure that the CorParms are being applied in the way that they were intended.

Author(s)

Jay Ver Hoef [e-mail]

References

Peterson, E. E. and Ver Hoef, J. M. (2010) A mixed-model moving-average approach to geostatistical modeling in stream networks. Ecology 91(3), 644–651.

Ver Hoef, J. M. and Peterson, E. E. (2010) A moving average approach for spatial statistical models of stream networks (with discussion). Journal of the American Statistical Association 105, 6–18. DOI: 10.1198/jasa.2009.ap08248. Rejoinder pgs. 22 - 24.

Examples

#######################################
## example 1: Gaussian data, 2 networks
#######################################

library(SSN)
set.seed(101)
## simulate a SpatialStreamNetwork object
raw1.ssn <- createSSN(n = c(10,10),
    obsDesign = binomialDesign(c(50,50)), predDesign = binomialDesign(c(100,100)),
	importToR = TRUE, path = paste(tempdir(),"/sim1", sep = ""))
plot(raw1.ssn)

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

## look at the column names of each of the data frames
names(raw1.ssn)

## 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")

## Columns of design matrix, coefficients argument applied to these
sim1.out$FixedEffects

## 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")
sim1DFobs[,"Sim_Values"]
sim1DFpred <- getSSNdata.frame(sim1.ssn, "preds")
sim1DFpred[,"Sim_Values"]

## plot the simulated observed values
plot(sim1.ssn, "Sim_Values")

## 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")

# NOT RUN, IT TAKES A MINUTE OR SO
## fit a model to see how well we estimate simulation parameters
#fitSimGau <- glmssn(Sim_Values ~ X1 + F1, ssn.object = sim1.ssn,
#	 CorModels = c("Exponential.tailup", "Exponential.taildown"),
#	 addfunccol = "addfunccol")
# LOAD A STORED VERSION INSTEAD
data(modelFits)
#make sure fitSimGau has the correct path, will vary for each users installation
#predictions depend on distance matrix created earlier with createDistMat function
#path of this lsn directory was created with createSSN
fitSimGau$ssn.object@path <- paste(tempdir(),"/sim1", sep = "")

summary(fitSimGau)

## make predictions
pred1.ssn <- predict(fitSimGau,"preds")
par(bg = "grey60")
plot(pred1.ssn, color.palette = terrain.colors(10))
par(bg = "white")

## compare predicted values to simulated values
pred1DF <- getSSNdata.frame(pred1.ssn, "preds")
plot(sim1preds, pred1DF[,"Sim_Values"], xlab = "True", ylab = "Predicted",
	pch = 19)

######################################
## example 2: Binomial data, 1 network
######################################
## NOT RUN takes about 10 seconds
#set.seed(102)
## simulate a SpatialStreamNetwork object
#raw2.ssn <- createSSN(n = 20,
#	obsDesign = binomialDesign(100), predDesign = binomialDesign(200),
#	importToR = TRUE, path = paste(tempdir(),"/sim2", sep = ""))
#plot(raw2.ssn)

## create distance matrices, including between predicted and observed
#createDistMat(raw2.ssn, "preds", o.write=TRUE, amongpred = TRUE)

## look at the column names of each of the data frames
#names(raw2.ssn)

## extract the observed and predicted data frames
#raw2DFobs <- getSSNdata.frame(raw2.ssn, "Obs")
#raw2DFpred <- getSSNdata.frame(raw2.ssn, "preds")

## add a continuous covariate randomly
#raw2DFobs[,"X1"] <- rnorm(length(raw2DFobs[,1]))
#raw2DFpred[,"X1"] <- rnorm(length(raw2DFpred[,1]))

## add a categorical covariate randomly
#raw2DFobs[,"F1"] <- as.factor(sample.int(3,length(raw2DFobs[,1]), replace = TRUE))
#raw2DFpred[,"F1"] <- as.factor(sample.int(3,length(raw2DFpred[,1]), replace = TRUE))

## simulate Binomial data
#sim2.out <- SimulateOnSSN(raw2.ssn,
#	ObsSimDF = raw2DFobs,
#	PredSimDF = raw2DFpred,
#	PredID = "preds",
#	formula = ~ X1 + F1,
#	coefficients = c(0, .5, -1, 1),
#	CorModels = c("Exponential.tailup", "Exponential.taildown",
#		"Exponential.Euclid"),
#	use.nugget = TRUE,
#	use.anisotropy = FALSE,
#	CorParms = c(.5, 5, .5, 5, .5, 2, 0.01),
#	addfunccol = "addfunccol",
#	family = "Binomial")

## Columns of design matrix, coefficients argument applied to these
#sim2.out$FixedEffects

## extract the ssn.object
#sim2.ssn <- sim2.out$ssn.object

## extract the observed and predicted data frames, now with simulated values
#sim2DFobs <- getSSNdata.frame(sim2.ssn, "Obs")
#sim2DFobs[,"Sim_Values"]
#sim2DFpred <- getSSNdata.frame(sim2.ssn, "preds")
#sim2DFpred[,"Sim_Values"]

## plot the simulated observed values
#plot(sim2.ssn, "Sim_Values", nclasses = 2, color.palette = c("blue","red"),
#	breaktype = "user", brks = cbind(c(-.5,.5),c(.5, 1.5)))

## store simulated prediction values, and then create NAs in their place
#sim2preds <- sim2DFpred[,"Sim_Values"]
#sim2DFpred[,"Sim_Values"] <- NA
#sim2.ssn <- putSSNdata.frame(sim2DFpred, sim2.ssn, "preds")

# NOT RUN, IT TAKES A MINUTE OR SO
## fit a model to see how well we estimate simulation parameters
#fitSimBin <- glmssn(Sim_Values ~ X1 + F1,
# 	ssn.object = sim2.ssn, EstMeth = "REML", family = "Binomial",
# 	CorModels = "Exponential.taildown",
# 	addfunccol = "addfunccol")
# LOAD A STORED VERSION INSTEAD
#data(modelFits)
#make sure fitSimBin has the correct path, will vary for each users installation
#predictions depend on distance matrix created earlier with createDistMat function
#path of this lsn directory was created with createSSN
#fitSimBin$ssn.object@path <- paste(tempdir(),"/sim2", sep = "")
#summary(fitSimBin)

## make predictions
#predSimBin <- predict(fitSimBin,"preds")
#par(bg = "grey60")
#plot(predSimBin, color.palette = terrain.colors(10))
#par(bg = "white")

## compare predicted values to simulated values
#pred2DF <- getSSNdata.frame(predSimBin, "preds")
#table(sim2preds, (pred2DF[,"Sim_Values"]>0)*1)

#####################################
## example 3: Poisson data, 1 network
#####################################
## NOT RUN Similar to Binomial Data
#set.seed(104)
## simulate a SpatialStreamNetwork object
#raw3.ssn <- createSSN(n = 20,
#	obsDesign = binomialDesign(100), predDesign = binomialDesign(200),
#	importToR = TRUE, path = paste(tempdir(),"/sim3", sep = ""))
#plot(raw3.ssn)

## create distance matrices, including between predicted and observed
#createDistMat(raw3.ssn, "preds", o.write=TRUE, amongpred = TRUE)

## look at the column names of each of the data frames
#names(raw3.ssn)

## extract the observed and predicted data frames
#raw3DFobs <- getSSNdata.frame(raw3.ssn, "Obs")
#raw3DFpred <- getSSNdata.frame(raw3.ssn, "preds")

## add a continuous covariate randomly
#raw3DFobs[,"X1"] <- rnorm(length(raw3DFobs[,1]))
#raw3DFpred[,"X1"] <- rnorm(length(raw3DFpred[,1]))
## add a categorical covariate randomly

#raw3DFobs[,"F1"] <- as.factor(sample.int(3,length(raw3DFobs[,1]), replace = TRUE))
#raw3DFpred[,"F1"] <- as.factor(sample.int(3,length(raw3DFpred[,1]), replace = TRUE))

## simulate Poisson data
#sim3.out <- SimulateOnSSN(raw3.ssn,
#	ObsSimDF = raw3DFobs,
#	PredSimDF = raw3DFpred,
#	PredID = "preds",
#	formula = ~ X1 + F1,
#	coefficients = c(1, .5, -1, 1),
#	CorModels = c("Exponential.taildown"),
#	use.nugget = TRUE,
#	use.anisotropy = FALSE,
#	CorParms = c(.5, 5, 0.01),
#	addfunccol = "addfunccol",
#	family = "Poisson")

## Columns of design matrix, coefficients argument applied to these
#sim3.out$FixedEffects

## extract the ssn.object
#sim3.ssn <- sim3.out$ssn.object

## extract the observed and predicted data frames, now with simulated values
#sim3DFobs <- getSSNdata.frame(sim3.ssn, "Obs")
#sim3DFobs[,"Sim_Values"]
#sim3DFpred <- getSSNdata.frame(sim3.ssn, "preds")
#sim3DFpred[,"Sim_Values"]

## plot the simulated observed values
#plot(sim3.ssn, "Sim_Values")

## store simulated prediction values, and then create NAs in their place
#sim3preds <- sim3DFpred[,"Sim_Values"]
#sim3DFpred[,"Sim_Values"] <- NA
#sim3.ssn <- putSSNdata.frame(sim3DFpred, sim3.ssn, "preds")

# NOT RUN, IT TAKES A MINUTE OR SO
## fit a model to see how well we estimate simulation parameters
#fitSimPoi <- glmssn(Sim_Values ~ X1 + F1,
# 	ssn.object = sim3.ssn, EstMeth = "REML", family = "Poisson",
# 	CorModels = "Exponential.taildown",
# 	addfunccol = "addfunccol")
# LOAD A STORED VERSION INSTEAD
#data(modelFits)
#make sure fitSimPoi has the correct path, will vary for each users installation
#predictions depend on distance matrix created earlier with createDistMat function
#path of this lsn directory was created with createSSN
#fitSimPoi$ssn.object@path <- paste(tempdir(),"/sim3", sep = "")
#summary(fitSimPoi)

## make predictions
#pred3.ssn <- predict(fitSimPoi,"preds")
#par(bg = "grey60")
#plot(pred3.ssn, color.palette = terrain.colors(10))
#par(bg = "white")

## compare predicted values to simulated values
#pred3DF <- getSSNdata.frame(pred3.ssn, "preds")

#plot(log(sim3preds+.1), pred3DF[,"Sim_Values"], xlab = "True", ylab = "Estimated",
#	pch = 19)

SSN documentation built on March 7, 2023, 5:30 p.m.