Collapses a SpatialStreamNetwork object into a data frame

Description

Collapses a SpatialStreamNetwork object into a data frame

Usage

collapse(ssn, par = "afvArea")

Arguments

Details

The parameters (par) has to be present in the observed data frame via ssn_get_data(n, name = "obs"). More details of the argument par can be found in the additive.function() from SSN .

Value

A data frame with the lat and long of the line segments in the network. The column line_id refers to the ID of the line.

Examples

#require("SSN2")
#path <- system.file("extdata/clearwater.ssn", package = "SSNbayes")
#ssn <- SSN2::ssn_import(path, predpts = "preds", overwrite  = TRUE)
#t.df <- collapse(ssn, par = 'afvArea')

Creates a list containing the stream distances and weights

Description

Creates a list containing the stream distances and weights

Usage

dist_weight_mat(path = path, net = 1, addfunccol = "addfunccol")

Arguments

Value

A list of matrices

Examples

path <- system.file("extdata/clearwater.ssn", package = "SSNbayes")
mat_all <- dist_weight_mat(path, net = 2, addfunccol='afvArea')

Creates a list of distances and weights between observed and prediction sites

Description

The output matrices are symmetric except the hydrologic distance matrix D.

Usage

dist_weight_mat_preds(path = path, net = 1, addfunccol = "addfunccol")

Arguments

Value

A list of matrices

Examples

## Not run: 
path <- system.file("extdata/clearwater.ssn", package = "SSNbayes")
mat_all_pred <- dist_weight_mat_preds(path, net = 2, addfunccol='afvArea')
## End(Not run)

Internal function used to perform spatio-temporal prediction in R using a stanfit object from ssnbayes()

Description

Use predict.ssnbayes() instead. It will take an observed and a prediction data frame. It requires the same number of observation/locations per day. It requires location id (locID) and points id (pid). The locID are unique for each site. The pid is unique for each observation. Missing values are allowed in the response but not in the covariates.

Usage

krig(
  object = object,
  mat_all_preds = mat_all_preds,
  nsamples = 10,
  start = 1,
  chunk_size = 50,
  obs_data = obs_data,
  pred_data = pred_data,
  net = net,
  seed = seed
)

Arguments

Value

A data frame

Author(s)

Edgar Santos-Fernandez

A simple modeling function using a formula and data

Description

A simple modeling function using a formula and data

A simple modeling function using a formula and data

Usage

mylm(formula, data)

mylm(formula, data)

Arguments

Value

A list of matrices

A list of matrices

Author(s)

Jay ver Hoef

Examples

options(na.action='na.pass')
data("iris")
out_list = mylm(formula = Petal.Length ~ Sepal.Length + Sepal.Width, data = iris)
options(na.action='na.pass')
data("iris")
out_list = mylm(formula = Petal.Length ~ Sepal.Length + Sepal.Width, data = iris)

Internal function used to perform spatio-temporal prediction in R using a stanfit object from ssnbayes()

Description

Use predict.ssnbayes() instead. It will take an observed and a prediction data frame. It requires the same number of observation/locations per day. It requires location id (locID) and points id (pid). The locID are unique for each site. The pid is unique for each observation. Missing values are allowed in the response but not in the covariates.

Usage

pred_ssnbayes(
  object = object,
  path = path,
  obs_data = obs_data,
  pred_data = pred_data,
  net = 1,
  nsamples = 100,
  addfunccol = "afvArea",
  locID_pred = locID_pred,
  chunk_size = chunk_size,
  seed = seed
)

Arguments

Value

A data frame

Author(s)

Edgar Santos-Fernandez

Examples

#pred <- pred_ssnbayes(path = path,
#obs_data = clear,
#stanfit = fit_ar,
#pred_data = preds,
#net = 2,
#nsamples = 100, # number of samples to use from the posterior in the stanfit object
#addfunccol = 'afvArea') # variable used for spatial weights

Performs spatio-temporal prediction in R using an ssnbayes object from a fitted model.

Description

It will take an observed and a prediction data frame. It requires the same number of observation/locations per day. It requires location id (locID) and points id (pid). The locID are unique for each site. The pid is unique for each observation. Missing values are allowed in the response but not in the covariates.

Usage

## S3 method for class 'ssnbayes'
predict(
  object = object,
  ...,
  path = path,
  obs_data = obs_data,
  pred_data = pred_data,
  net = net,
  nsamples = nsamples,
  addfunccol = addfunccol,
  locID_pred = locID_pred,
  chunk_size = chunk_size,
  seed = seed
)

Arguments

Details

The returned data frame is melted to produce a long dataset. See examples.

Value

A data frame with the location (locID), time point (date), plus the MCMC draws from the posterior from 1 to the number of iterations. The locID0 column is an internal consecutive location ID (locID) produced in the predictions, starting at max(locID(observed data)) + 1. It is used internally in the way predictions are made in chunks.

Author(s)

Edgar Santos-Fernandez

Examples

#require('SSNdata')
#clear_preds <- readRDS(system.file("extdata/clear_preds.RDS", package = "SSNdata"))
#clear_preds$y <- NA
#pred <- predict(object = fit_ar,
#                 path = path,
#                 obs_data = clear,
#                 pred_data = clear_preds,
#                 net = 2,
#                 nsamples = 100, # numb of samples from the posterior
#                 addfunccol = 'afvArea', # var for spatial weights
#                 locID_pred = locID_pred,
#                 chunk_size = 60)

Fits a mixed linear regression model using Stan

Description

It requires the same number of observation/locations per day. It requires location id (locID) and points id (pid). The locID are unique for each site. The pid is unique for each observation. Missing values are allowed in the response but not in the covariates.

Usage

ssnbayes(
  formula = formula,
  data = data,
  path = path,
  time_method = time_method,
  space_method = space_method,
  iter = 3000,
  warmup = 1500,
  chains = 3,
  refresh = max(iter/100, 1),
  net = 1,
  addfunccol = addfunccol,
  loglik = FALSE,
  seed = seed
)

Arguments

Details

Missing values are not allowed in the covariates and they must be imputed before using ssnbayes(). Many options can be found in https://cran.r-project.org/web/views/MissingData.html The pid in the data has to be consecutive from 1 to the number of observations. Users can use the SpatialStreamNetwork created with the SSN package. This will provide the spatial stream information used to compute covariance matrices. If that is the case, the data has to have point ids (pid) matching the ones in SSN distance matrices, so that a mapping can occur.

Value

A list with the model fit

It returns a ssnbayes object (similar to stan returns). It includes the formula used to fit the model. The output can be transformed into the stanfit class using class(fits) <- c("stanfit").

Author(s)

Edgar Santos-Fernandez

Examples

## Not run: 
#options(mc.cores = parallel::detectCores())
# Import SpatialStreamNetwork object
#path <- system.file("extdata/clearwater.ssn", package = "SSNbayes")
#n <- SSN2::ssn_import(path, predpts = "preds", overwrite  = TRUE)
## Imports a data.frame containing observations and covariates
#clear <- readRDS(system.file("extdata/clear_obs.RDS", package = "SSNbayes"))
#fit_ar <- ssnbayes(formula = y ~ SLOPE + elev + h2o_area + air_temp + sin + cos,
#                   data = clear,
#                   path = path,
#                   time_method = list("ar", "date"),
#                   space_method = list('use_ssn', c("Exponential.taildown")),
#                   iter = 2000,
#                   warmup = 1000,
#                   chains = 3,
#                   net = 2, # second network on the ssn object
#                   addfunccol='afvArea')
#space_method options examples
#use list('no_ssn', 'Exponential.Euclid', c('lon', 'lat')) if no ssn object is available

## End(Not run)