input_class {onlineforecast}

Class for forecastmodel inputs

Description

R6 class for for forecastmodel inputs

Details

Holds variables and functions needed for an input, as added by forecastmodel$add_inputs().

Details of the class.

Public fields

- expr = NA: The expression as string for transforming the input.

- state_L = list(): The list holding potential state values kept by the function evaluated in the expression.

- state_i = integer(1): index counter for the state list.

Public methods

All public functions are described below and in examples a section for each is included:

$new(expr)

Create a new input with the expression expr.

$evaluate(data

Generate (transform) the input by evaluating the expr with the data (data.list) attached.

$state_reset()

Each function in the expressions (lp, fs, etc.) have the possibility to save a state, which can be read next time the are called.

Reset the state by deleting state_L and setting state_i to 0.

# After running model$inputs[[1]]$evaluate(D) # the lp() has saved it's state for next time model$inputs[[1]]$state_L # New data arrives Dnew <- subset(Dbuilding, 11, kseq=1:4) # So in lp() the state is read and it continues model$inputs[[1]]$evaluate(Dnew)

# If we want to reset the state, which is done in all _fit() functions (e.g. rls_fit), such that all transformations starts from scratch # Reset the state model$inputs[[1]]$evaluate(D) # Test resetting model$inputs[[1]]$state_reset() # Now there is no state model$inputs[[1]]$evaluate(Dnew) # So lp() starts by taking the first data point Dnew$Ta

$state_getval(initval)

Get the saved value in state. This function can be used in the beginning of transformation functions to get the current state value. First time called return the initval.

Note that since all transformation functions are called in the same order, then the state can be read and saved by keeping a counter internally, the value is saved in the field $state_i.

See example of use in lp().

$state_setval(val)

Set the state value, done in the end of a transformation function, see above.

See example of use in lp().

Examples

# new:

# An input is created in a forecastmodel
model <- forecastmodel$new()
model$add_inputs(Ta = "lp(Ta, a1=0.9)")
# The 'inputs' is now a list 
model$inputs
# With the input object
class(model$inputs[[1]])

# Now the transformation stage can be carried out to create the regression stage data
# Take a data.list subset for the example
D <- subset(Dbuilding, 1:10, kseq=1:4)
# Transform the data
model$inputs[[1]]$evaluate(D)
# What happens is simply that the expression is evaluated with the data
# (Note that since not done in the model some functions are missing)
eval(parse(text=model$inputs[[1]]$expr), D)