Description Usage Arguments Details Value Methods (by class) References Examples
Test on deviceevents using William DuMouchel's Empirical Bayes GammaPoisson Shrinker. From the family of disproportionality analyses (DPA) used to generate signals of disproportionate reporting (SDRs).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  gps(df, ...)
## S3 method for class 'mds_ts'
gps(df, ts_event = c(Count = "nA"), analysis_of = NA, ...)
## Default S3 method:
gps(
df,
analysis_of = NA,
eval_period = 1,
null_ratio = 1,
cred_interval = 0.9,
init_prior = c(0.2, 0.02, 2, 4, 1/3),
gamma_lower = 1e05,
gamma_upper = 20,
quantiles = c(0.05, 0.95),
cont_adj = 0,
...
)

df 
Required input data frame of class

... 
Further arguments passed onto 
ts_event 
Required if Default: 
analysis_of 
Optional string indicating the English description of what
was analyzed. If specified, this will override the name of the
Default: Example: 
eval_period 
Required positive integer indicating the number of unique times counting in reverse chronological order to sum over to create the 2x2 contingency table. Default: Example: 
null_ratio 
Numeric value representing the null relative reporting
ratio (RR), used with Default: 
cred_interval 
Numeric value between 0 and 1 representing the width of
the Bayesian posterior credible interval, where the lower bound of the
interval is assessed against the Default: 
init_prior 
A numeric vector of length 5 representing the
initialization parameters for the prior gamma mixture distribution in this
order: Default: 
gamma_lower 
Positive mumeric value representing the lower bound for the two alphas and betas of the prior during PORT optimization. Default: 
gamma_upper 
Positive mumeric value representing the upper bound for the two alphas and betas of the prior during PORT optimization. Default: 
quantiles 
Vector of quantiles between 0 and 1. Default: 
cont_adj 
Positive integer representing the continuity adjustment to be added to each cell of the 2x2 contingency table. A value greater than 0 allows for contingency tables with 0 cells to run the algorithm. Adding a continuity adjustment will adversely affect the algorithm estimates, user discretion is advised. See details for more. Default: 
null_ratio
and cred_interval
are used together to establish the
signal criteria. The null_ratio
is conceptually similar to the
relative reporting ratio under a null hypothesis of no signal. Common values
are 1
and, more conservatively (fewer false signals), 2
. The
cred_interval
is the posterior credibility interval used to test for a
signal. A value of 0.90
returns the 5
tests if the lower bound exceeds null_ratio
. Effectively,
cred_interval=0.90
conducts the wellknown EB05 test.
init_prior
specifies the initial guess for the 5 parameters of the
prior gamma mixture distribution as described in DuMouchel (1999, Eqs. 4, 7)
in the sequence: alpha1, beta1, alpha2, beta2, p. gamma_lower
specifies the optimization lower bound for the two alphas and betas.
gamma_upper
specifies similarly the upper bound. The initial guess,
upper and lower bounds are fed into PORT optimization using the
stats::nlminb()
routine.
cont_adj
provides the option to allow gps()
to proceed running,
however this is done at the user's discretion because there are adverse
effects of adding a positive integer to every cell of the contingency table.
By default, gps()
runs with 0 in the C cell only, but not in A, B, or
D. It has been suggested that 0.5 may be an appropriate value. However,
values <1 have been shown to be unstable using boxconstrained PORT
optimization, which is the only optimization considered in this release.
Overall, posterior distribution estimates have been shown to be unstable with
very low or 0 count cells.
For parameter ts_event
, in the uncommon case where the
deviceevent count (Cell A) variable is not "nA"
, the name of the
variable may be specified here. Note that the remaining 3 cells of the 2x2
contingency table (Cells B, C, D) must be the variables "nB"
,
"nC"
, and "nD"
respectively in df
. A named character
vector may be used where the name is the English description of what was
analyzed. Note that if the parameter analysis_of
is specified, it will
override this name. Example: ts_event=c("Count of Bone Cement
Leakages"="event_count")
A named list of class mdsstat_test
object, as follows:
Name of the test run
English description of what was analyzed
Named boolean of whether the test was run. The name contains the run status.
A standardized list of test run results: statistic
for the test statistic, lcl
and ucl
for the set
confidence bounds, p
for the pvalue, signal
status, and
signal_threshold
.
The test parameters
The data on which the test was run
mds_ts
: GPS on mds_ts data
default
: GPS on general data
DuMouchel W. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. The American Statistician, 53(3):177190, August 1999.
Ahmed I, Poncet A. PhViD: PharmacoVigilance Signal Detection, 2016. R package version 1.0.8.
Ihrie J, Canida T. openEBGM: EBGM Scores for Mining Large Contingency Tables, 2018. R package version 0.7.0.
1 2 3 4 5 6 7 8 9  # Basic Example
data < data.frame(time=c(1:25),
nA=as.integer(stats::rnorm(25, 25, 5)),
nB=as.integer(stats::rnorm(25, 50, 5)),
nC=as.integer(stats::rnorm(25, 100, 25)),
nD=as.integer(stats::rnorm(25, 200, 25)))
a1 < gps(data)
# Example using an mds_ts object
a2 < gps(mds_ts[[3]])

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