contactCompare_chisq: Compare Observed Contacts to a Random Distribution Using...

In contact: Creating Contact and Social Networks

Description

This function is used to determine if tracked individuals in an empirical dataset had more or fewer contacts with other tracked individuals/specified locations than would be expected at random. The function works by comparing an empirical contact distribution (generated using x.summary and x.potential) to a NULL distribution (generated using y.summary and y.potential) using a X-square goodness-of-fit test. Note that here, the NULL hypothesis is that empirical data are consistent with the NULL distribution, and the alternative hypothesis is that the data are NOT consistent. This function SHOULD NOT be used to compare two empirical networks using Chi-squared tests, as the function assumes x.summary and y.summary represent observed and expected values, respectively. Please note that this is a function of convience that is essentially a wrapper for the chisq.test function, that allows users to easily compare contact networks created using our pipeline of contact:: functions.

This function was inspired by the methods described by Spiegel et al. 2016. They determined individuals to be expressing social behavior when nodes had greater degree values than would be expected at random, with randomized contact networks derived from movement paths randomized according to their novel methodology (that can be implemented using our randomizePaths function). Here, users can also identify when more or fewer contacts (demonstrated by the sign of values in the "difference" column in the output) with specific individuals than would be expected at random, given a pre-determined p-value threshold. Such relationships suggest social affinities or aversions, respectively, may exist between specific individuals.

Note:The default tested column (i.e., categorical data column from which data is drawn to be compared to randomized sets herein) is "id." This means that contacts involving each individual (defined by a unique "id") will be compared to randomized sets. Users may not use any data column for analysis other than "id." If users want to use another categorical data column in analyses rather than "id," we recommend re-processing data (starting from the dist.all/distToArea functions), while specifying this new data as an "id." For example, users may annotate an illness status column to the empirical input, wherein they describe if the tracked individual displayed gastrointestinal ("gastr"), respiratory ("respr"), both ("both"), illness symptoms, or were consistently healthy ("hel") over the course of the tracking period. Users could set this information as the "id," and carry it forward as such through the data-processing pipeline. Ultimately, they could determine if each of these disease states affected contact rates, relative to what would be expected at random.

Take care to ensure that the same shuffle.type is denoted as was originally used to randomize individuals' locations (assuming the randomizePaths function was used to do so). This is important for two reasons: 1.) If there was no y.potential input, the function assumes that x.potential is relevant to the random set as well. This is a completely fair assumption when importBlocks == FALSE or when the shuffleUnit == 0. In cases when the shuffle.type is 1 or 2, however, this assumption can lead to erroneous results and/or errors in the function. 2.) In the randomizePaths function, setting shuffle.type == 2 produces only 1 shuffle.unit's worth of data (e.g., 1 day), rather than a dataset with the same length of x. As such, there may be a different number of blocks in y compared to x. Here we assume that the mean randomized durations per block in y.summary and y.potential, are representative of mean randomized durations per block across each shuffle unit (e.g., day 1 is representative of day 3, etc.).#'

Finally, if X-square expected values will be very small, approximations of p may not be correct (and in fact, all estimates will be poor). It may be best to weight these tests differently. In the event that this is the case, contactCompare_binom may be used to obtain more-accurate estimates.

Usage

contactCompare_chisq(
  x.summary,
  y.summary,
  x.potential,
  y.potential = NULL,
  importBlocks = FALSE,
  shuffle.type = 1,
  pairContacts = TRUE,
  totalContacts = TRUE,
  popLevelOutput = FALSE,
  parallel = FALSE,
  nCores = (parallel::detectCores()/2),
  ...
)

Arguments

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.
x.potential	List or single-data frame output from the potentialDurations function refering to the empirical data. Note that if x.potential is a list of data frames, potential contact durations used in the function will be determined by averaging those reported in each list entry.
y.potential	List or single-data frame output from the potentialDurations function refering to the randomized data. Note that if y.potential is a list of data frames, potential contact durations used in the function will be determined by averaging those reported in each list entry. If NULL, reverts to x.potential. Defaults to NULL.
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 AND .potential objects, and values must be identical (i.e., if block 100 exists in x inputs, it must also exist in y inputs), otherwise an error will be returned.
shuffle.type	Integer. Describes which shuffle.type (from the randomizePaths function) was used to randomize the y.summary data set(s). Takes the values "0," "1," or "2." This is important because there are different assumptions associated with each shuffle.type.
pairContacts	Logical. If TRUE individual id columns from x.summary and y.summary inputs will be included in analyses. Defaults to TRUE.
totalContacts	Logical. If TRUE totalDegree and totalContactDurations columns from x.summary and y.summary inputs will be included in analyses. Defaults to TRUE.
popLevelOutput	Logical. If TRUE a secondary output describing population-level comparisons will be appended to the standard, individual-level function output.
parallel	Logical. If TRUE, sub-functions within the summarizeContacts wrapper will be parallelized. Note that the only sub-function parallelized here is called ONLY when importBlocks == TRUE.
nCores	Integer. Describes the number of cores to be dedicated to parallel processes. Defaults to half of the maximum number of cores available (i.e., (parallel::detectCores()/2)).
...	Other arguments to be passed to the chisq.test function.

Value

Output format is dependent on popLevelOutput value.

If popLevelOut == FALSE output will be a single two data frame containing individual-level pairwise analyses of node degree, total edge weight (i.e., the sum of all observed contacts involving each individual), and specific dyad weights (e.g., contacts between individuals 1 and 2). The data frame contains the following columns:

id	the id of the specific individual.
metric	designation of what is being compared (e.g., totalDegree, totalContactDurations, individual 2, etc.). Content will change depending on which data frame is being observed.
method	Statistical test used to determine significance.
X.squared	Test statistic associated with the comparison.
p.val	p.values associated with each comparison.
df	Degrees of freedom associated with the statistical test.
contactDurations.x	Describes the number of observed events in x.summary.
contactDurations.y	Describes the number of observed events in y.summary.
noContactDurations.x	Describes the number of empirical events that were not observed given the total number of potential events in x.potential.
noContactDurations.y	Describes the number of random events that were not observed given the total number of potential events in y.potential.
difference	The absolute value given by subtracting contactDurations.y from contactDurations.x.
warning	Denotes if any specific warning occurred during analysis.
block.x	Denotes the specific time block from x.(Only if importBlocks == TRUE)
block.start.x	Denotes the specific timepoint at the beginning of each time block. (Only if importBlocks == TRUE)
block.end.x	Denotes the specific timepoint at the end of each time block. (Only if importBlocks == TRUE)
block.y	Denotes the specific time block from y.(Only if importBlocks == TRUE)
block.start.y	Denotes the specific timepoint at the beginning of each time block. (Only if importBlocks == TRUE)
block.end.y	Denotes the specific timepoint at the end of each time block. (Only if importBlocks == TRUE)

If popLevelOutput == TRUE, output will be a list of two data frames: The one described above, and second describing the population-level comparisons. Columns in each data frame are identical.

References

Agresti, A. 2007. An introduction to categorical data analysis, 2nd ed. New York: John Wiley & Sons. 38.

Farine, D.R., 2017. A guide to null models for animal social network analysis. Methods in Ecology and Evolution 8:1309-1320. https://doi.org/10.1111/2041-210X.12772.

Spiegel, O., Leu, S.T., Sih, A., and C.M. Bull. 2016. Socially interacting or indifferent neighbors? Randomization of movement paths to tease apart social preference and spatial constraints. Methods in Ecology and Evolution 7:971-979. https://doi.org/10.1111/2041-210X.12553.

Examples

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.
emp.potential <- potentialDurations(calves.dist, blocking = TRUE, 
                                    blockUnit = "hours", blockLength = 1, 
                                    distFunction = "dist2All_df") 

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
rand.potential <- potentialDurations(calves.dist.rand, blocking = TRUE, 
                                     blockUnit = "hours", blockLength = 1, 
                                     distFunction = "dist2All_df") 


contactCompare_chisq(x.summary = emp.summary, y.summary = rand.summary, 
                     x.potential = emp.potential, y.potential = rand.potential,
                     importBlocks = FALSE, shuffle.type = 0, 
                     popLevelOut = TRUE, parallel = FALSE) #no blocking

contactCompare_chisq(x.summary = emp.summary, y.summary = rand.summary, 
                     x.potential = emp.potential, y.potential = rand.potential,
                     importBlocks = TRUE, shuffle.type = 0, 
                     popLevelOut = TRUE, parallel = FALSE) #blocking
   

contact documentation built on May 17, 2021, 5:07 p.m.