cna (version 2.2.3)

full.tt: Generate all logically possible value configurations of a given set of factors

Description

full.tt generates a truthTab with all logically possible value configurations of the factors defined in the input x. It is more flexible than allCombs. x can be a truthTab, a data frame, an integer, a list specifying the factors' value ranges, or a character vector featuring all admissible factor values.

Usage

full.tt(x, ...)
# S3 method for default
full.tt(x, type = c("cs", "mv", "fs"), ...)
# S3 method for truthTab
full.tt(x, ...)
# S3 method for tti
full.tt(x, ...)

Arguments

x

A truthTab, a data frame, an integer, a list specifying the factors' value ranges, or a character vector featuring all admissible factor values (see the details and examples below).

type

Character vector specifying the type of x: "cs" (crisp-set), "mv" (multi-value), or "fs" (fuzzy-set); passed to truthTab; only required if x is a data frame or matrix.

…

Further arguments passed to methods.

Value

A truthTab of type "cs" or "mv" with the full enumeration of combinations of the factor values.

Details

full.tt generates all logically possible value configurations of the factors defined in x, which can either be a character vector or an integer or a list or a data frame or a matrix.

• If x is a character vector, it can be a condition of any of the three types of conditions, boolean, atomic or complex (see condition). x must contain at least one factor. Factor names and admissible values are guessed from the Boolean formulas. If x contains multi-value factors, only those values are considered admissible that are explicitly contained in x. Accordingly, in case of multi-value factors, full.tt should be given the relevant factor definitions by means of a list (see below).

• If x is an integer and <=26, the output will be a full truth table of type "cs" with x factors. The first x capital letters of the alphabet will be used as the names of the factors.

• If x is a list, x is expected to have named elements each of which provides the factor names with corresponding vectors enumerating their admissible values (i.e. their value ranges). These values must be integers.

• If x is a truthTab, data frame, or matrix, colnames(x) are interpreted as factor names and the rows as enumerating the admissible values (i.e. as value ranges). If x is a data frame or a matrix, x is first converted to a truthTab (the function truthTab is called with type as specified in full.tt), and the truthTab method of full.tt is then applied to the result. The truthTab method uses all factors and factor values occurring in the truthTab. If x is of type "fs", 0 and 1 are taken as the admissible values.

In combination with selectCases, full.tt is useful for simulating data, which are needed for inverse search trials benchmarking the output of cna. While full.tt generates the space of all logically possible configurations of the factors in an analyzed factor set, selectCases selects those configurations from this space that are compatible with a given data-generating causal structure (i.e. the ground truth), that is, it selects the empirically possible configurations.

The method for class "tti" is for internal use only.

See Also

truthTab, selectCases, allCombs

Examples

# NOT RUN {
# x is a character vector.
full.tt("A + B*c")
full.tt("A=1*C=3 + B=2*C=1 + A=3*B=1") 
full.tt(c("A + b*C", "a*D"))
full.tt("!A*-(B + c) + F")

# x is a data frame.
full.tt(d.educate)
full.tt(d.jobsecurity, type = "fs")
full.tt(d.pban, type = "mv")

# x is a truthTab.
full.tt(cstt(d.educate))
full.tt(fstt(d.jobsecurity))
full.tt(mvtt(d.pban))

# x is an integer.
full.tt(6)

# x is a list.
full.tt(list(A = 0:1, B = 0:1, C = 0:1))  # cs
full.tt(list(A = 1:2, B = 0:1, C = 1:4))  # mv

# Simulating crisp-set data.
groundTruth.1 <- "(A*b + C*d <-> E)*(E*H + I*k <-> F)"
fullData <- full.tt(groundTruth.1)
idealData <- selectCases(groundTruth.1, fullData) 
# Introduce 20% data fragmentation.
fragData <- idealData[-sample(1:nrow(idealData), nrow(idealData)*0.2), ] 
# Introduce 10% random noise.
realData <- rbind(tt2df(fullData[sample(1:nrow(fullData), nrow(fragData)*0.1), ]), fragData)

# Simulating multi-value data.
# }
# NOT RUN {
groundTruth.2 <- "(JO=3 + TS=1*PE=3 <-> ES=1)*(ES=1*HI=4 + IQ=2*KT=5 <-> FA=1)"
fullData <- full.tt(list(JO=1:3, TS=1:2, PE=1:3, ES=1:2, HI=1:4, IQ=1:5, KT=1:5, FA=1:2))
idealData <- selectCases(groundTruth.2, fullData) 
# Introduce 20% data fragmentation.
fragData <- idealData[-sample(1:nrow(idealData), nrow(idealData)*0.2), ] 
# Introduce 10% random noise.
realData <- rbind(tt2df(fullData[sample(1:nrow(fullData), nrow(fragData)*0.1), ]), fragData)
# }