impest: Imprecise Estimation
In impimp: Imprecise Imputation for Statistical Matching

Description Usage Arguments Details Value References See Also Examples

Estimate the probability of some events based on data obtained by imprecise imputation

1	impest(data, event, constraints = NULL)

`data`	a data.frame obtained as result from an imprecise imputation e.g. by a call to `impimp`.
`event`	a list of objects of class `"impimp_event"`, specifiying the event of interest. See 'Details'.
`constraints`	a list of so-called logical constraints or fixed zeros. Each element must be an object of class `"impimp_event"`. See 'Details' .

event should be a list of objects of class "impmp_event", where the set union of impimp_events is the actual event of interest.

By specifying constraints one can exlude combinations of imputed values which are deemed impossible, so called ‘logical constraints’ or ‘fixed zeros’. constraints should be a list of objects of class "impimp_event".

An object of class "impimp_event" is obtained as a result of a call to impimp_event.

For both event and constraints holds that overlapping in the resulting events generated by the individual impimp_events does not have any side effects, besides a potential decrease in performance.

A numeric vector of length 2, where the first component contains the lower and the second component the upper probability of the event of interest.

Endres, E., Fink, P. and Augustin, T. (2018), Imprecise Imputation: A Nonparametric Micro Approach Reflecting the Natural Uncertainty of Statistical Matching with Categorical Data, Department of Statistics (LMU Munich): Technical Reports, No. 214

impimp, impimp_event for sepcifying constraints and events; impestcond for the estimation of conditional probabilities

A <- data.frame(x1 = c(1,0), x2 = c(0,0),
                y1 = c(1,0), y2 = c(2,2))
B <- data.frame(x1 = c(1,1,0), x2 = c(0,0,0),
                z1 = c(0,1,1), z2 = c(0,1,2))
AimpB <- impimp(A, B, method = "variable_wise")
BimpA <- impimp(B, A, method = "variable_wise")
AB <- rbindimpimp(AimpB, BimpA)

## P(Z1=1, Z2=0)
myevent1 <- list(impimp_event(z1 = 1, z2 = 0))
impest(AB, event = myevent1)

## P[(Z1,Z2) in {(1,0),(0,1),(1,1)}]
myevent2 <- list(impimp_event(z1 = 1,z2 = 0),
                 impimp_event(z1 = c(0,1), z2 = 1))
impest(AB, event = myevent2)