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 required input for fitting spatial additive network models to `SSN`

objects using the main `smnet`

function.

1 | ```
get_adjacency(ssn_directory, netID)
``` |

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

`netID` |
Integer specifying the particular stream network of interest within the |

List with two elements.

`adjacency` |
Sparse adjacency matrix of class " |

`bid` |
Character vector of binary identifiers for each stream segment, used only for automatic calculation of Shreve's stream order within smnet |

Alastair Rushworth

`smnet`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | ```
# Set up an SSN object - this part taken
# from the SSN:::SimulateOnSSN help file
example_network<- createSSN(
n = 50,
obsDesign = binomialDesign(200),
predDesign = binomialDesign(50),
importToR = TRUE,
path = paste(tempdir(),"/example_network",sep = ""),
treeFunction = iterativeTreeLayout
)
# plot the simulated network structure with prediction locations
# plot(example_network, bty = "n", xlab = "x-coord", ylab = "y-coord")
## create distance matrices, including between predicted and observed
createDistMat(example_network, "preds", o.write=TRUE, amongpred = TRUE)
## extract the observed and predicted data frames
observed_data <- getSSNdata.frame(example_network, "Obs")
prediction_data <- getSSNdata.frame(example_network, "preds")
## associate continuous covariates with the observation locations
# data generated from a normal distribution
obs <- rnorm(200)
observed_data[,"X"] <- obs
observed_data[,"X2"] <- obs^2
## associate continuous covariates with the prediction locations
# data generated from a normal distribution
pred <- rnorm(50)
prediction_data[,"X"] <- pred
prediction_data[,"X2"] <- pred^2
## simulate some Gaussian data that follows a 'tail-up' spatial process
sims <- SimulateOnSSN(
ssn.object = example_network,
ObsSimDF = observed_data,
PredSimDF = prediction_data,
PredID = "preds",
formula = ~ 1 + X,
coefficients = c(1, 10),
CorModels = c("Exponential.tailup"),
use.nugget = TRUE,
CorParms = c(10, 5, 0.1),
addfunccol = "addfunccol")$ssn.object
## extract the observed and predicted data frames, now with simulated values
sim1DFpred <- getSSNdata.frame(sims, "preds")
sim1preds <- sim1DFpred[,"Sim_Values"]
sim1DFpred[,"Sim_Values"] <- NA
sims <- putSSNdata.frame(sim1DFpred, sims, "preds")
# create the adjacency matrix for use with smnet
adjacency <- get_adjacency(
paste(tempdir(), "/example_network", sep = ""),
net = 1
)
# not run - plot the adjacency matrix
# display(adjacency[[1]])
# sometimes it is useful to see which varaibles are valid network weights
# in the data contained within the SSN object
show_weights(sims, adjacency)
# fit a penalised spatial model to the stream network data
# Sim_Values are quadratic in the X covariate. To highlight
# the fitting of smooth terms, this is treated as non-linear
# and unknown using m().
mod_smn <- smnet(formula = Sim_Values ~ m(X) + m(X2) +
network(adjacency = adjacency, weight = "shreve"),
data.object = sims, netID = 1)
# not run - plot different summaries of the model
plot(mod_smn, type = "covariates")
plot(mod_smn, type = "segments", weight = 4, shadow = 2)
plot(mod_smn, type = "full", weight = 4, shadow = 2)
# obtain predictions at the prediction locations and plot
# against true values
preds <- predict(mod_smn, newdata = getSSNdata.frame(sims, "preds"))
plot(preds$predictions, sim1preds)
# obtain summary of the fitted model
summary(mod_smn)
``` |

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