fe.sq: Forgotten Effects For Complete Graphs

In ElliottMardones/test10: foRgotten

Description

Perform the forgotten effects calculation proposed by Kaufmann and Gil-Aluja (1988) with multiple key informants. Parameters allow you to specify the significant degree of truth and the order of incidence that is required to be calculated for complete multi-expert graphs. The function returns the frequency of appearance of the forgotten effect, its mean incidence, the confidence intervals and the standard error in each order.

Usage

fe.sq(
  CC,
  thr = 0.5,
  maxOrder = 2,
  reps = 10000,
  parallel = c("multicore", "snow", "no"),
  ncpus = 1
)

Arguments

CC	Three-dimensional matrix, where each submatrix along the z-axis is a square and reflective incidence matrix, or a list of data.frames containing square and reflective incidence matrices. Each matrix represents a complete graph.
thr	Real between [0,1]: Defines the degree of truth for which the incidence is considered significant. By default thr = 0.5.
maxOrder	Positive integer greater than 1: Defines the maximum order of the forgotten effects. By default maxOrder = 2.
reps	The number of bootstrap replicas. By default reps = 10.000.
parallel	The type of parallel operation to use (if applicable). The options are "multicore", "snow" and "no". By default parallel = "no".
ncpus	Integer: Number of processes that will be used in the parallel implementation. By default ncpus = 1.

Details

The function extends the theory of forgotten effects proposed by Kaufmann and Gil-Aluja (1988), to find indirect cause-effect relationships from direct cause-effect relationships, in the case of multiple experts. The parallel and ncpus options are not available on Windows operating systems.

Value

The function returns a list with subsets of data. $boot: List of data.frame for each of the generated commands, contains the following components:

From	Indicates the origin of the forgotten effects relationships.
Through_x	Dynamic field representing the intermediate relationships of the forgotten effects. For example, for order n there will be "though_1" up to "though_ <n-1>" though_(n-1).
To	Indicates the end of the forgotten effects relationships.
Count	Number of times the forgotten effect was repeated.
Mean	Mean effect of the forgotten effect.
LCI	Lower Confidence Intervals.
UCI	Upper Confidence Intervals.
SE	Standard error.

$byExperts: List of data.frames for each of the generated orders that contains the incidence values for each of the relationships found by the expert, the components are:

From	Indicates the origin of the forgotten effects relationships.
Through_x	Dynamic field representing the intermediate relationships of the forgotten effects. For example, for order n there will be "though_1" up to "though_ <n-1>".
To	Indicates the end of the forgotten effects relationships.
Count	Number of times the forgotten effect was repeated.
Expert_x	Dynamic field that represent each of the entered experts.

References

Kaufmann, A., & Aluja, J. G. (1988). Modelos para la investigación de efectos olvidados. Milladoiro.

Canty A, Ripley BD (2021). boot: Bootstrap R (S-Plus) Functions. R package version 1.3-28.

Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695

Eddelbuettel D, François R (2011). "Rcpp: Seamless R and C++ Integration." Journal of Statistical Software, 40(8), 1–18.

Eddelbuettel D (2013). Seamless R and C++ Integration with Rcpp. Springer, New York.

Eddelbuettel D, Balamuta JJ (2018). "Extending extitR with extitC++: A Brief Introduction to extitRcpp." The American Statistician, 72(1), 28-36.6.

Examples

# To perform the calculation of the forgotten effects for a complete graph with a degree
# of truth equal to 0.5, maximum order of effects to calculate equal to 2 and 500 bootstrap
# replicates, use:
fe.sq( CC = AA, thr = 0.5, maxOrder = 2, reps = 500, parallel = "no", ncpus = 1)

ElliottMardones/test10 documentation built on Dec. 17, 2021, 6:26 p.m.