meltt (version 0.4.3)

meltt_validate: Validation method to assess data integrated by meltt.

Description

Function to efficiently sample a subset of integrated data to generate performance statistics.

Usage

meltt_validate(object, description.vars = NULL, sample_prop = .1, within_window = TRUE,
	       spatial_window = NULL, temporal_window = NULL, reset = FALSE)

Value

Function automatically overwrites input "meltt" object; if validation set has been completely reviewed, then the function prints the performance statistics.

Arguments

object

object of class meltt.

description.vars

String vector referencing column names located in the input data. These are the variables that will be folded into the description being validated; if none are provided, taxonomy levels are used by default.

sample_prop

Argument establishes the proportion of of the total matched pairs that are sampled from. The size of this sample is then used to determine the size of the control group (i.e. all entries not flagged as matches, which are paired with other unique entries and matches. These entries should not be matches). For example, if sample_prop = .1 and this results in a sample of 20 matched pairs, then 20 control pairs will be extracted from the set, leading to a total sample of 40 observations to be reviewed. Input must exist within the range .01 and 1.

within_window

Use the same spatio-temporal window used in the initial data integration to calculate what counts as a "proximate event" for all entries in the control group. Default = TRUE. If set to FALSE, user must assign a new spatio-temporal window using spatial_window and temporal_window.

spatial_window

If within_window==FALSE, set new spatial window (in km).

temporal_window

If within_window==FALSE, set new temporal window (in days).

reset

If TRUE, the validation step will be reset and a new validation sample frame will be produced. Default = FALSE.

Author

Karsten Donnay and Eric Dunford.

Details

meltt_validate offers an efficient method of assessing the performance of meltt for a specific integration, by randomly sampling from a proportion of pairs of matching events flagged by the algorithm as the same event, and then sampling a "control group" of equal size from events that were identified as unique (offering both unique-unique and unique-match pairs). The function compiles the samples and then generates a shiny app to ease assessment. Once all entries in the sample have been assessed, the function then returns accuracy statistics in terms of a confusion matrix. Performance is determined by the difference in the qualitative assessment in comparison to the meltt integration.

References

Karsten Donnay, Eric T. Dunford, Erin C. McGrath, David Backer, David E. Cunningham. (2018). "Integrating Conflict Event Data." Journal of Conflict Resolution.

See Also

meltt, meltt_inspect

Examples

# \donttest{
data(crashMD)
output <- meltt(crash_data1, crash_data2, crash_data3,
		taxonomies = crash_taxonomies, twindow = 1, spatwindow = 3)
# }
if (FALSE) {
# app will activate to validate sample.
meltt_validate(output)

# for smaller sample, must reset to overwrite existing validation sample
meltt_validate(output, sample_prop=.1, reset = TRUE) 

# override of the validation to get a sense of the report
output$validation$validation_set$coding = 1 

meltt_validate(output)
}