Description Usage Arguments Author(s) See Also Examples

Plot linear, univariate and bivariate smooth effects and network smooth terms that resulting from a call to `smnet`

.

1 2 3 4 |

`x` |
An object of class |

`type` |
Character string identifying the type of plot to be produced. The default, " |

`se` |
Logical. When TRUE and |

`res` |
Logical. When TRUE, partial residuals are shown on plots of linear and smooth component. Ignored when |

`weight` |
Positive real number that scales stream segment widths (as determined using Shreve order) to indicate relative size of stream segments. Ignored when |

`sites` |
Logical indicating whether locations of observation sites should be added to spatial plots. Ignored when |

`sites.col` |
Single colour to plot observation locations. If not provided, points will be coloured according to the default legend and average observation at each location. |

`sites.cex` |
Expansion factor for the size of plotted observation points |

`network.col` |
Single colour to represent all stream segments. By default, network is coloured according to fitted values from model. Ignored when |

`shadow` |
Positive scalar that adds a black outline to spatial stream segment plots, useful if the colour scale has many light colours. Ignored when |

`key` |
Logical. Plots a colour legend for network plots. Ignored when |

`legend.text` |
Character annotation to add to color scale. Ignored if |

`legend.range` |
Range of values represented by the color scale. Ignored if |

`...` |
Other arguments passed to plot |

Alastair Rushworth

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# Set up an SSN object - this part taken
# from the SSN:::SimulateOnSSN help file
example_network_4<- createSSN(
n = 50,
obsDesign = binomialDesign(200),
predDesign = binomialDesign(50),
importToR = TRUE,
path = paste(tempdir(),"/example_network_4",sep = ""),
treeFunction = iterativeTreeLayout
)
# plot the simulated network structure with prediction locations
# plot(example_network_4, bty = "n", xlab = "x-coord", ylab = "y-coord")
## create distance matrices, including between predicted and observed
createDistMat(example_network_4, "preds", o.write=TRUE, amongpred = TRUE)
## extract the observed and predicted data frames
observed_data <- getSSNdata.frame(example_network_4, "Obs")
prediction_data <- getSSNdata.frame(example_network_4, "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_4,
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")$ssn.object
## extract the observed and predicted data frames, now with simulated values
sim1DFpred <- getSSNdata.frame(sims, "preds")
sim1preds <- sim1DFpred[,"Sim_Values"]
sim1DFpred[,"Sim_Values"] <- NA
sims <- putSSNdata.frame(sim1DFpred, sims, "preds")
# create the adjacency matrix for use with smnet
adjacency <- get_adjacency(
paste(tempdir(), "/example_network_4", sep = ""),
net = 1
)
# not run - plot the adjacency matrix
# display(adjacency[[1]])
# sometimes it is useful to see which varaibles are valid network weights
# in the data contained within the SSN object
show_weights(sims, adjacency)
# fit a penalised spatial model to the stream network data
# Sim_Values are quadratic in the X covariate. To highlight
# the fitting of smooth terms, this is treated as non-linear
# and unknown using m().
mod_smn <- smnet(formula = Sim_Values ~ m(X) + m(X2) +
network(adjacency = adjacency, weight = "shreve"),
data.object = sims, netID = 1)
# not run - plot different summaries of the model
plot(mod_smn, type = "covariates")
plot(mod_smn, type = "segments", weight = 4, shadow = 2)
plot(mod_smn, type = "full", weight = 4, shadow = 2)
# obtain predictions at the prediction locations and plot
# against true values
preds <- predict(mod_smn, newdata = getSSNdata.frame(sims, "preds"))
plot(preds$predictions, sim1preds)
# obtain summary of the fitted model
summary(mod_smn)
``` |

smnet documentation built on May 29, 2017, 7:35 p.m.

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