Generate a paramter frame

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Description

A parameter frame is a data.frame where the rows correspond to different parameter specifications. The columns are divided into three parts. (1) the meta-information columns (e.g. index, value, constraint, etc.), (2) the attributes of an objective function (e.g. data contribution and prior contribution) and (3) the parameters.

Usage

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parframe(x = NULL, parameters = colnames(x), metanames = NULL,
  obj.attributes = NULL)

## S3 method for class 'parframe'
x[i = NULL, j = NULL, drop = FALSE]

## S3 method for class 'parframe'
subset(x, ...)

Arguments

x

data.frame.

parameters

character vector, the names of the parameter columns.

metanames

character vector, the names of the meta-information columns.

obj.attributes

character vector, the names of the objective function attributes.

i

row index in any format

j

column index in any format

drop

logical. If TRUE the result is coerced to the lowest possible dimension

...

additional arguments

Details

Parameter frames can be subsetted either by [ , ] or by subset. If [ , index] is used, the names of the removed columns will also be removed from the corresponding attributes, i.e. metanames, obj.attributes and parameters.

Value

An object of class parframe, i.e. a data.frame with attributes for the different names. Inherits from data.frame.

See Also

profile, mstrust

Examples

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## Generate a prediction function
regfn <- c(y = "sin(a*time)")

g <- Y(regfn, parameters = "a")
x <- Xt(condition = "C1")

## Generate data
data <- datalist(
  C1 = data.frame(
    name = "y",
    time = 1:5,
    value = sin(1:5) + rnorm(5, 0, .1),
    sigma = .1
  )
)

## Initialize parameters and time 
pars <- c(a = 1)
times <- seq(0, 5, .1)

plot((g*x)(times, pars), data)

## Do many fits from random positions and store them into parlist
out <- as.parlist(lapply(1:50, function(i) {
  trust(normL2(data, g*x), pars + rnorm(length(pars), 0, 1), rinit = 1, rmax = 10)
}))

summary(out)

## Reduce parlist to parframe
parframe <- as.parframe(out)
plotValues(parframe)

## Reduce parframe to best fit
bestfit <- as.parvec(parframe)
plot((g*x)(times, bestfit), data)

## Plot array with all solutions
plotArray(parframe, g*x, times, data)

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