contactCompare_mantel: Statistically Compare Two Contact Matrices
In contact: Creating Contact and Social Networks

Description Usage Arguments Value References Examples

Tests for similarity of the x.summary input to y.summary. Please note that this is a function of convience that is essentially a wrapper for the ape::mantel.test function, that allows users to easily compare contact networks created using our pipeline of contact:: functions. Please understand that ape::mantel.test does not allow for missing values in matrices, so all NAs will be treated as zeroes.

contactCompare_mantel(
  x.summary,
  y.summary,
  numPermutations = 1000,
  alternative.hyp = "two.sided",
  importBlocks = FALSE
)

`x.summary`	List or single-data frame output from the summarizeContacts function refering to the empirical data. Note that if x.summary is a list of data frames, only the first data frame will be used in the function.
`y.summary`	List or single-data frame output from the summarizeContacts function refering to the randomized data (i.e., NULL model contact-network edge weights). Note that if y.summary is a list of data frames, only the first data frame will be used in the function.
`numPermutations`	Integer. Number of times to permute the data. Defaults to 1000.
`alternative.hyp`	Character string. Describes the nature of the alternative hypothesis being tested when test == "mantel." Takes the values "two.sided," "less," or "greater." Defaults to "two.sided."
`importBlocks`	Logical. If true, each block in x.summary will be analyzed separately. Defaults to FALSE. Note that the "block" column must exist in .summary objects AND values must be identical (i.e., if block 100 exists in x.summary, it must also exist in y.summary), otherwise an error will be returned.

Output format is a single data frame with the following columns.

`method`	Statistical test used to determine significance.
`z.val`	z statistic.
`p.value`	p.values associated with each comparison.
`x.mean`	mean contacts in x.summary overall or by block (if applicable). Note that these means are calculated BEFORE any NAs are converted to zeroes (see note above)
`y.mean`	mean contacts in y.summary overall or by block (if applicable). Note that these means are calculated BEFORE any NAs are converted to zeroes (see note above)
`alternative.hyp`	The nature of the alternative hypothesis being tested.
`nperm`	Number of permutations used to generate p value.
`warning`	Denotes if any specific warning occurred during analysis.

Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Research, 27:209–220.

data(calves) #load data

calves.dateTime<-datetime.append(calves, date = calves$date,
                                 time = calves$time) #add dateTime column

calves.agg<-tempAggregate(calves.dateTime, id = calves.dateTime$calftag,
                       dateTime = calves.dateTime$dateTime, point.x = calves.dateTime$x,
                       point.y = calves.dateTime$y, secondAgg = 300, extrapolate.left = FALSE,
                       extrapolate.right = FALSE, resolutionLevel = "reduced", parallel = FALSE,
                       na.rm = TRUE, smooth.type = 1) #aggregate to 5-min timepoints

calves.dist<-dist2All_df(x = calves.agg, parallel = FALSE,
                       dataType = "Point", lonlat = FALSE) #calculate  inter-calf distances

calves.contact.block<-contactDur.all(x = calves.dist, dist.threshold=1,
                       sec.threshold=10, blocking = TRUE, blockUnit = "hours", blockLength = 1,
                       equidistant.time = FALSE, parallel = FALSE, reportParameters = TRUE)

emp.summary <- summarizeContacts(calves.contact.block, 
                                 importBlocks = TRUE) #empirical contact summ.



calves.agg.rand<-randomizePaths(x = calves.agg, id = "id",
                       dateTime = "dateTime", point.x = "x", point.y = "y", poly.xy = NULL,
                       parallel = FALSE, dataType = "Point", numVertices = 1, blocking = TRUE,
                       blockUnit = "mins", blockLength = 20, shuffle.type = 0, shuffleUnit = NA,
                       indivPaths = TRUE, numRandomizations = 2) #randomize calves.agg

calves.dist.rand<-dist2All_df(x = calves.agg.rand, point.x = "x.rand",
                       point.y = "y.rand", parallel = FALSE, dataType = "Point", lonlat = FALSE)

calves.contact.rand<-contactDur.all(x = calves.dist.rand,
                       dist.threshold=1, sec.threshold=10, blocking = TRUE, blockUnit = "hours",
                       blockLength = 1, equidistant.time = FALSE, parallel = FALSE,
                       reportParameters = TRUE) #NULL model contacts (list of 2)

rand.summary <- summarizeContacts(calves.contact.rand, avg = TRUE,
                                  importBlocks = TRUE) #NULL contact summary


contactCompare_mantel(x.summary = emp.summary, y.summary = rand.summary,
                    importBlocks = FALSE, numPermutations = 100,
                    alternative.hyp = "two.sided") #no blocking

contactCompare_mantel(x.summary = emp.summary, y.summary = rand.summary,
                    importBlocks = TRUE, numPermutations = 100,
                    alternative.hyp = "two.sided") #blocking