View source: R/allCombs_makefuzzy.r

allCombs | R Documentation |

The function `allCombs`

generates a data frame of all possible value configurations of `length(x)`

factors, the first factor having `x[1]`

values, the second `x[2]`

values etc. The factors are labeled using capital letters.

```
allCombs(x)
```

`x` |
Integer vector with values >0 |

In combination with `selectCases`

. `makeFuzzy`

, and `is.submodel`

, `allCombs`

is useful for simulating data, which are needed for inverse search trials benchmarking the output of `cna`

. In a nutshell, `allCombs`

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, which can be randomly generated using `randomConds`

), `makeFuzzy`

fuzzifies those data, and `is.submodel`

checks whether the models returned by `cna`

are true of the ground truth.

The cna package provides another function to the same effect, `full.ct`

, which is more flexible than `allCombs`

.

A data frame.

`selectCases`

, `makeFuzzy`

, `is.submodel`

, `randomConds`

, `full.ct`

```
# Generate all logically possible configurations of 5 dichotomous factors named "A", "B",
# "C", "D", and "E".
allCombs(c(2, 2, 2, 2, 2)) - 1
# allCombs(c(2, 2, 2, 2, 2)) generates the value space for values 1 and 2, but as it is
# conventional to use values 0 and 1 for Boolean factors, 1 must be subtracted from
# every value output by allCombs(c(2, 2, 2, 2, 2)) to yield a Boolean data frame.
# Generate all logically possible configurations of 5 multi-value factors named "A", "B",
# "C", "D", and "E", such that A can take on 3 values {1,2,3}, B 4 values {1,2,3,4},
# C 3 values etc.
dat0 <- allCombs(c(3, 4, 3, 5, 3))
head(dat0)
nrow(dat0) # = 3*4*3*5*3
# Generate all configurations of 5 dichotomous factors that are compatible with the causal
# chain (A*b + a*B <-> C)*(C*d + c*D <-> E).
dat1 <- allCombs(c(2, 2, 2, 2, 2)) - 1
(dat2 <- selectCases("(A*b + a*B <-> C)*(C*d + c*D <-> E)", dat1))
# Generate all configurations of 5 multi-value factors that are compatible with the causal
# chain (A=2*B=1 + A=3*B=3 <-> C=1)*(C=1*D=2 + C=4*D=4 <-> E=3).
dat1 <- allCombs(c(3, 3, 4, 4, 3))
dat2 <- selectCases("(A=2*B=1 + A=3*B=3 <-> C=1)*(C=1*D=2 + C=4*D=4 <-> E=3)", dat1)
nrow(dat1)
nrow(dat2)
# Generate all configurations of 5 fuzzy-set factors that are compatible with the causal
# structure A*b + C*D <-> E, such that con = .8 and cov = .8.
dat1 <- allCombs(c(2, 2, 2, 2, 2)) - 1
dat2 <- makeFuzzy(dat1, fuzzvalues = seq(0, 0.45, 0.01))
(dat3 <- selectCases1("A*b + C*D <-> E", con = .8, cov = .8, dat2))
# Inverse search for the data generating causal structure A*b + a*B + C*D <-> E from
# fuzzy-set data with non-perfect consistency and coverage scores.
set.seed(3)
groundTruth <- "A*b + a*B + C*D <-> E"
dat1 <- allCombs(c(2, 2, 2, 2, 2)) - 1
dat2 <- makeFuzzy(dat1, fuzzvalues = 0:4/10)
dat3 <- selectCases1(groundTruth, con = .8, cov = .8, dat2)
ana1 <- cna(dat3, outcome = "E", con = .8, cov = .8)
any(is.submodel(asf(ana1)$condition, groundTruth))
```

cna documentation built on Aug. 11, 2023, 1:09 a.m.

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