ScaledNormalData-class: Class '"ScaledNormalData"' and its constructor function...
In luck: Generalized iLUCK models

Description Usage Arguments Details Slots of the resulting object Methods for ScaledNormalData objects Author(s) References See Also Examples

ScaledNormalData objects represent data from a scaled Normal distribution (a Normal, or Gaussian, distribution with variance 1). For inferences based on this data in the framework of generalized iLUCK models, the class ScaledNormalLuckModel can be used. ScaledNormalData extends the class LuckModelData. Objects can be created using the constructor function ScaledNormallData() described below.

1 2	ScaledNormalData(arg1 = NULL, arg2 = NULL, data = NULL, mean = NULL, n = NULL, sim = FALSE)

`arg1`	Used for treatment of unnamed arguments only, see Details.
`arg2`	Used for treatment of unnamed arguments only, see Details.
`data`	A vector of observations x from a Normal distribution with variance 1, i.e., x ~ N(mu,1).
`mean`	The mean of n observations from a Normal distribution with variance 1. Must be supplied together with `n`.
`n`	The sample size of observations from a Normal distribution with variance 1 and mean given in `mean`.
`sim`	If `TRUE`, data are simulated according to `mean` and `n` and stored in the object. Defaults to `FALSE`.

ScaledNormalData objects can be created using one of the following ways:

By supplying a LuckModelData as the only (unnamed) argument.
By supplying the mean and the sample size of a sample in mean and n, respectively. one of the arguments may be unnamed.
By supplying a vector of observations in data.
By setting sim = TRUE, and supplying a mean and a sample size in mean and n, respectively, a vector of observations is simulated.

tauN:: A matrix with two named columns, with the first giving the sample statistic tau(x) = n*mean(x), and the second giving the corresponding sample size n.
rawData:: The data vector, if data was provided or simulated. From this data, τ(x) and n in tauN are calculated.

Methods for `ScaledNormalData` objects

There are methods to access or replace the contents of the slots:

tauN: signature(object = "ScaledNormalData")
tauN<-: signature(object = "ScaledNormalData")
tau: signature(object = "ScaledNormalData")
tau<-: signature(object = "ScaledNormalData")
n: signature(object = "ScaledNormalData")
n<-: signature(object = "ScaledNormalData")
rawData: signature(object = "ScaledNormalData")
rawData<-: signature(object = "ScaledNormalData")

When the slot rawData is replaced using rawData<-, the contents of the slot tauN are recalculated. Attempts to replace (parts of) tauN if the object contains data in slot rawData results in an error.
There is a method to display ScaledNormalData objects by text:

show: signature(object = "ScaledNormalData"): This is invoked when printing a ScaledNormalData object, or a ScaledNormalLuckModel object containing data.

Gero Walter

Gero Walter and Thomas Augustin (2009), Imprecision and Prior-data Conflict in Generalized Bayesian Inference, Journal of Statistical Theory and Practice 3:255-271.

luck for a general description of the package, and ScaledNormalLuckModel for the model using this data representation.

# generate a ScaledNormalData object
set.seed(8553)
x <- rnorm(10)
scndata1 <- ScaledNormalData(data = x)
# also possible:
scndata2 <- ScaledNormalData(mean = mean(x), n = length(x))
scndata3 <- ScaledNormalData(mean = 0, n = 10, sim = TRUE)
data1 <- LuckModelData(tau=20, n=10)
scndata4 <- ScaledNormalData(data1)
# access and replace slots
tauN(scndata1)
rawData(scndata1) <- rnorm(n = 20, mean=3)
scndata1
tau(scndata2)
tau(scndata2) <- 0
n(scndata2)
n(scndata2) <- 100