GoricEvSyn_LLandPT: GORIC(A) evidence synthesis based on log likelihood and...

In rebeccakuiper/GoricEvSyn: GORIC(A) evidence synthesis (GoricEvSyn) aggregates the evidence for theory-based hypotheses from multiple studies that may use diverse designs to investigate the same central theory.

GORIC(A) evidence synthesis based on log likelihood and penalty values

Description

GORIC(A) evidence synthesis (GoricEvSyn) aggregates the evidence for theory-based hypotheses from multiple studies that may use diverse designs to investigate the same central theory. There is also an interactive web application on my website to perform GoricEvSyn: https://www.uu.nl/staff/RMKuiper/Websites%20%2F%20Shiny%20apps.

Usage

GoricEvSyn_LLandPT(
  TypeEv,
  S,
  LL,
  PT,
  Name_studies = 1:S,
  Name_Hypo = NULL,
  PrintPlot = T
)

Arguments

TypeEv	The type of evidence-synthesis approach: Equal-evidence approach (0) or Added-evidence approach (1). In case of an equal-evidence approach, aggregating evidence from, say, 5 studies with n=100 observations is the same as obtaining evidence from 1 study (as if it was possible) with n=500 observations (like meta-analysis does). In the added-evidence approach, the aggregated evidence from, says, 5 studies is stronger than as if the data were combined (as if that was possible).
S	The number of (primary) studies. That is, the results (evidence) of S studies will be aggregated.
LL	A matrix with log likelihood values of size S x 'NrHypos+1', where 'NrHypos+1' stands for the number of theory-based hypotheses plus a safeguard hypothesis (the complement or unconstrained). Notably, only when the set of hypotheses cover the whol space / all theories (e.g., positive versus negative effect), then you can do without a safeguard hypothesis.
PT	A matrix with penalty values of size S x 'NrHypos+1', where 'NrHypos+1' stands for the number of theory-based hypotheses plus a safeguard hypothesis (the complement or unconstrained). Notably, only when the set of hypotheses cover the whol space / all theories (e.g., positive versus negative effect), then you can do without a safeguard hypothesis.
Name_studies	Optional. Vector of S numbers or S characters to be printed at the x-axis of the plot with GORIC(A) weights. Default: Name_studies = 1:S.
Name_Hypo	Optional. Vector containing 'NrHypos+1' characters which will be used for labelling the hypothesis. Default: H1, H2, ....
PrintPlot	Optional. Indicator whether plot of GORIC(A) weigths should be printed (TRUE; default) or not (FALSE). The GORIC(A) weights per study are plotted and the cumulative GORIC(A) weights (where those for the last study are the final ones).

Value

The output comprises, among other things, the cumulative and final evidence for the theory-based hypotheses.

Examples

S <- 4
# Example based on S = 4 studies and 3 hypotheses:
# H0 <- "beta1 == 0"  # this hypothesis could have been left out
# Hpos <- "beta1 > 0"
# Hneg <- "beta1 < 0"
# Note that in this set the whole space is (all theories are) covered so the unconstrained is not needed as safeguard-hypothesis
LL <- myLLs
PT <- myPTs

# Added-evidence approach
TypeEv <- 1
GoricEvSyn_LLandPT(TypeEv, S, LL, PT)

# Equal-evidence approach
TypeEv <- 0
GoricEvSyn_LLandPT(TypeEv, S, LL, PT)

# Change labels on x-axis in GORIC(A) weigths plot and give names to hypotheses #
# For example, let us say that the studies come from the years 2015, 2016, 2017, 2019.
# Because of unequal spacing, you may want to use numbers instead of characters:
Name_studies <- c(2015, 2016, 2017, 2019)
Name_Hypo <- c("H0", "Hpos", "Hneg")
GoricEvSyn_LLandPT(TypeEv, S, LL, PT, Name_studies, Name_Hypo)

rebeccakuiper/GoricEvSyn documentation built on July 3, 2023, 6:41 a.m.