vignettes/MCIAnalysis.md
In umd-lib/animalmove: Analysis of animal population-level movement patterns
Movement Coordination Index
Load package library
library(lubridate)
library(animalmove)
library(plyr)
library(ggplot2)
Subsample Data
Buffalo dataset has been saved in the package data directory , and loaded on the package load.
Raw buffalo dataset contains unaltered original data.
data(buffalo)
head(buffalo)
## event.id visible timestamp location.long location.lat
## 1 10210419 true 2005-02-17 05:05:00.000 31.77 -24.54
## 2 10210423 true 2005-02-17 05:05:00.000 31.77 -24.54
## 3 10210428 true 2005-02-17 06:08:00.000 31.76 -24.54
## 4 10210434 true 2005-02-17 06:08:00.000 31.76 -24.54
## 5 10210456 true 2005-02-17 07:05:00.000 31.76 -24.55
## 6 10210458 true 2005-02-17 08:05:00.000 31.76 -24.55
## behavioural.classification comments manually.marked.outlier sensor.type
## 1 0 24.3 NA gps
## 2 0 24.3 NA gps
## 3 0 29.5 NA gps
## 4 0 29.5 NA gps
## 5 0 35.8 NA gps
## 6 0 37.3 NA gps
## individual.taxon.canonical.name tag.local.identifier
## 1 Syncerus caffer #1764820
## 2 Syncerus caffer #1764820
## 3 Syncerus caffer #1764820
## 4 Syncerus caffer #1764820
## 5 Syncerus caffer #1764820
## 6 Syncerus caffer #1764820
## individual.local.identifier
## 1 Queen
## 2 Queen
## 3 Queen
## 4 Queen
## 5 Queen
## 6 Queen
## study.name utm.easting
## 1 Kruger African Buffalo, GPS tracking, South Africa 375051
## 2 Kruger African Buffalo, GPS tracking, South Africa 375051
## 3 Kruger African Buffalo, GPS tracking, South Africa 374851
## 4 Kruger African Buffalo, GPS tracking, South Africa 374851
## 5 Kruger African Buffalo, GPS tracking, South Africa 374527
## 6 Kruger African Buffalo, GPS tracking, South Africa 374492
## utm.northing utm.zone study.timezone study.local.timestamp
## 1 7285726 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 2 7285726 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 3 7285502 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 4 7285502 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 5 7284538 36S South Africa Standard Time 2005-02-17 09:05:00.000
## 6 7284644 36S South Africa Standard Time 2005-02-17 10:05:00.000
nrow(buffalo)
## [1] 28410
currentnames <- colnames(buffalo)
currentnames
## [1] "event.id" "visible"
## [3] "timestamp" "location.long"
## [5] "location.lat" "behavioural.classification"
## [7] "comments" "manually.marked.outlier"
## [9] "sensor.type" "individual.taxon.canonical.name"
## [11] "tag.local.identifier" "individual.local.identifier"
## [13] "study.name" "utm.easting"
## [15] "utm.northing" "utm.zone"
## [17] "study.timezone" "study.local.timestamp"
names(buffalo)[names(buffalo) == "timestamp"] <- "time"
names(buffalo)[names(buffalo) == "utm.easting"] <- "x"
names(buffalo)[names(buffalo) == "utm.northing"] <- "y"
names(buffalo)[names(buffalo) == "tag.local.identifier"] <- "id"
names(buffalo)[names(buffalo) == "individual.taxon.canonical.name"] <- "pop.type"
# Data Conversion
buffalo$time <- as.POSIXct(strptime(buffalo$time, format = "%Y-%m-%d %H:%M",
tz = "GMT"))
# Display new names
newnames <- colnames(buffalo)
newnames
## [1] "event.id" "visible"
## [3] "time" "location.long"
## [5] "location.lat" "behavioural.classification"
## [7] "comments" "manually.marked.outlier"
## [9] "sensor.type" "pop.type"
## [11] "id" "individual.local.identifier"
## [13] "study.name" "x"
## [15] "y" "utm.zone"
## [17] "study.timezone" "study.local.timestamp"
head(buffalo)
## event.id visible time location.long location.lat
## 1 10210419 true 2005-02-17 05:05:00 31.77 -24.54
## 2 10210423 true 2005-02-17 05:05:00 31.77 -24.54
## 3 10210428 true 2005-02-17 06:08:00 31.76 -24.54
## 4 10210434 true 2005-02-17 06:08:00 31.76 -24.54
## 5 10210456 true 2005-02-17 07:05:00 31.76 -24.55
## 6 10210458 true 2005-02-17 08:05:00 31.76 -24.55
## behavioural.classification comments manually.marked.outlier sensor.type
## 1 0 24.3 NA gps
## 2 0 24.3 NA gps
## 3 0 29.5 NA gps
## 4 0 29.5 NA gps
## 5 0 35.8 NA gps
## 6 0 37.3 NA gps
## pop.type id individual.local.identifier
## 1 Syncerus caffer #1764820 Queen
## 2 Syncerus caffer #1764820 Queen
## 3 Syncerus caffer #1764820 Queen
## 4 Syncerus caffer #1764820 Queen
## 5 Syncerus caffer #1764820 Queen
## 6 Syncerus caffer #1764820 Queen
## study.name x y
## 1 Kruger African Buffalo, GPS tracking, South Africa 375051 7285726
## 2 Kruger African Buffalo, GPS tracking, South Africa 375051 7285726
## 3 Kruger African Buffalo, GPS tracking, South Africa 374851 7285502
## 4 Kruger African Buffalo, GPS tracking, South Africa 374851 7285502
## 5 Kruger African Buffalo, GPS tracking, South Africa 374527 7284538
## 6 Kruger African Buffalo, GPS tracking, South Africa 374492 7284644
## utm.zone study.timezone study.local.timestamp
## 1 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 2 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 3 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 4 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 5 36S South Africa Standard Time 2005-02-17 09:05:00.000
## 6 36S South Africa Standard Time 2005-02-17 10:05:00.000
Number of rows in the buffalo data & data set structure
length(table((buffalo$id)))
## [1] 6
str(buffalo)
## 'data.frame': 28410 obs. of 18 variables:
## $ event.id : int 10210419 10210423 10210428 10210434 10210456 10210458 10210483 10210535 10210544 10210558 ...
## $ visible : chr "true" "true" "true" "true" ...
## $ time : POSIXct, format: "2005-02-17 05:05:00" "2005-02-17 05:05:00" ...
## $ location.long : num 31.8 31.8 31.8 31.8 31.8 ...
## $ location.lat : num -24.5 -24.5 -24.5 -24.5 -24.5 ...
## $ behavioural.classification : int 0 0 0 0 0 0 0 0 0 0 ...
## $ comments : num 24.3 24.3 29.5 29.5 35.8 37.3 38.9 39.6 40 38.1 ...
## $ manually.marked.outlier : logi NA NA NA NA NA NA ...
## $ sensor.type : chr "gps" "gps" "gps" "gps" ...
## $ pop.type : chr "Syncerus caffer" "Syncerus caffer" "Syncerus caffer" "Syncerus caffer" ...
## $ id : chr "#1764820" "#1764820" "#1764820" "#1764820" ...
## $ individual.local.identifier: chr "Queen" "Queen" "Queen" "Queen" ...
## $ study.name : chr "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" ...
## $ x : num 375051 375051 374851 374851 374527 ...
## $ y : num 7285726 7285726 7285502 7285502 7284538 ...
## $ utm.zone : chr "36S" "36S" "36S" "36S" ...
## $ study.timezone : chr "South Africa Standard Time" "South Africa Standard Time" "South Africa Standard Time" "South Africa Standard Time" ...
## $ study.local.timestamp : chr "2005-02-17 07:05:00.000" "2005-02-17 07:05:00.000" "2005-02-17 08:08:00.000" "2005-02-17 08:08:00.000" ...
Subsample data within time interval
We select at most 6 individuals within 2009, time interval 50 hours, and accuracy 50 hours, and subsampling scheme for Realized Mobility Index
mci.subsample.data <- subsample(dat = buffalo, start = c("2005-02-17 00:00:00"),
end = "2006-12-31 00:00:00", interval = c("50 hours"), accuracy = c("3 hours"),
minIndiv = 3, maxIndiv = 6, mustIndiv = NULL, index.type = "mci")
## [1] "50 hours"
## [1] "3 hours"
## [1] "2005-02-17 00:00:00"
## 'data.frame': 8 obs. of 9 variables:
## $ numberOfIndividuals: int 3 4 5 6 5 5 4 3
## $ pairsOfCompleteSets: int 32 19 8 7 9 18 37 49
## $ completeSets : int 34 21 10 9 10 20 39 52
## $ scanInterval : chr "50 hours" "50 hours" "50 hours" "50 hours" ...
## $ scanAccuracy : chr "3 hours" "3 hours" "3 hours" "3 hours" ...
## $ firstScantime : chr "2005-02-17 00:00:00" "2005-02-17 00:00:00" "2005-02-17 00:00:00" "2005-02-17 00:00:00" ...
## $ firstOverlap : chr "2005-07-27 10:00" "2005-08-23 12:00" "2005-09-15 10:00" "2005-09-17 12:00" ...
## $ lastOverlap : chr "2005-10-06 06:00" "2005-10-06 06:00" "2005-10-06 06:00" "2005-10-06 06:00" ...
## $ lastScantime : chr "2006-12-31 00:00:00" "2006-12-31 00:00:00" "2006-12-31 00:00:00" "2006-12-31 00:00:00" ...
buffalo.indiv <- Individuals(mci.subsample.data, id = "id", time = "time", x = "x",
y = "y", group.by = "pop.type", proj4string = CRS("+proj=utm +zone=28 +datum=WGS84"))
Analysis
Compute MCI
mci.buffalo <- mci.index(buffalo.indiv, group.by = c("pop.type"), time.lag = c("time.lag"))
mci.buffalomci.buffalo <- mci.index(buffalo.indiv, group.by = c("pop.type"),
time.lag = c("time.lag"))
mci.buffalo
## An object of class "MCIndex"
## Slot "data":
## scantimes id pop.type mci.index
## 1 2005-09-17 12:00:00 #1764820 Syncerus caffer 0.9954
## 2 2005-09-17 12:00:00 #1764823 Syncerus caffer 0.9985
## 3 2005-09-17 12:00:00 #1764826 Syncerus caffer 0.9959
## 4 2005-09-17 12:00:00 #1764829 Syncerus caffer 0.9964
## 5 2005-09-17 12:00:00 #1764832 Syncerus caffer 0.9977
## 6 2005-09-17 12:00:00 #1764835 Syncerus caffer 0.9954
## 7 2005-09-19 14:00:00 #1764820 Syncerus caffer 0.9954
## 8 2005-09-19 14:00:00 #1764823 Syncerus caffer 0.9985
## 9 2005-09-19 14:00:00 #1764826 Syncerus caffer 0.9959
## 10 2005-09-19 14:00:00 #1764829 Syncerus caffer 0.9964
## 11 2005-09-19 14:00:00 #1764832 Syncerus caffer 0.9977
## 12 2005-09-19 14:00:00 #1764835 Syncerus caffer 0.9954
## 13 2005-09-23 18:00:00 #1764820 Syncerus caffer 0.9954
## 14 2005-09-23 18:00:00 #1764823 Syncerus caffer 0.9985
## 15 2005-09-23 18:00:00 #1764826 Syncerus caffer 0.9959
## 16 2005-09-23 18:00:00 #1764829 Syncerus caffer 0.9964
## 17 2005-09-23 18:00:00 #1764832 Syncerus caffer 0.9977
## 18 2005-09-23 18:00:00 #1764835 Syncerus caffer 0.9954
## 19 2005-09-25 20:00:00 #1764820 Syncerus caffer 0.9954
## 20 2005-09-25 20:00:00 #1764823 Syncerus caffer 0.9985
## 21 2005-09-25 20:00:00 #1764826 Syncerus caffer 0.9959
## 22 2005-09-25 20:00:00 #1764829 Syncerus caffer 0.9964
## 23 2005-09-25 20:00:00 #1764832 Syncerus caffer 0.9977
## 24 2005-09-25 20:00:00 #1764835 Syncerus caffer 0.9954
## 25 2005-09-27 22:00:00 #1764820 Syncerus caffer 0.9954
## 26 2005-09-27 22:00:00 #1764823 Syncerus caffer 0.9985
## 27 2005-09-27 22:00:00 #1764826 Syncerus caffer 0.9959
## 28 2005-09-27 22:00:00 #1764829 Syncerus caffer 0.9964
## 29 2005-09-27 22:00:00 #1764832 Syncerus caffer 0.9977
## 30 2005-09-27 22:00:00 #1764835 Syncerus caffer 0.9954
## 31 2005-09-30 00:00:00 #1764820 Syncerus caffer 0.9954
## 32 2005-09-30 00:00:00 #1764823 Syncerus caffer 0.9985
## 33 2005-09-30 00:00:00 #1764826 Syncerus caffer 0.9959
## 34 2005-09-30 00:00:00 #1764829 Syncerus caffer 0.9964
## 35 2005-09-30 00:00:00 #1764832 Syncerus caffer 0.9977
## 36 2005-09-30 00:00:00 #1764835 Syncerus caffer 0.9954
## 37 2005-10-02 02:00:00 #1764820 Syncerus caffer 0.9954
## 38 2005-10-02 02:00:00 #1764823 Syncerus caffer 0.9985
## 39 2005-10-02 02:00:00 #1764826 Syncerus caffer 0.9959
## 40 2005-10-02 02:00:00 #1764829 Syncerus caffer 0.9964
## 41 2005-10-02 02:00:00 #1764832 Syncerus caffer 0.9977
## 42 2005-10-02 02:00:00 #1764835 Syncerus caffer 0.9954
## 43 2005-10-04 04:00:00 #1764820 Syncerus caffer 0.9954
## 44 2005-10-04 04:00:00 #1764823 Syncerus caffer 0.9985
## 45 2005-10-04 04:00:00 #1764826 Syncerus caffer 0.9959
## 46 2005-10-04 04:00:00 #1764829 Syncerus caffer 0.9964
## 47 2005-10-04 04:00:00 #1764832 Syncerus caffer 0.9977
## 48 2005-10-04 04:00:00 #1764835 Syncerus caffer 0.9954
## 49 2005-10-06 06:00:00 #1764820 Syncerus caffer 0.9954
## 50 2005-10-06 06:00:00 #1764823 Syncerus caffer 0.9985
## 51 2005-10-06 06:00:00 #1764826 Syncerus caffer 0.9959
## 52 2005-10-06 06:00:00 #1764829 Syncerus caffer 0.9964
## 53 2005-10-06 06:00:00 #1764832 Syncerus caffer 0.9977
## 54 2005-10-06 06:00:00 #1764835 Syncerus caffer 0.9954
cexValue = 2
boxplot(mci.index ~ factor(pop.type), data = mci.buffalo, col = "green", border = NULL,
outline = F, lwd = 2, boxwex = 0.5, cex = cexValue, cex.lab = cexValue,
cex.axis = cexValue, frame = F, ylab = "Movement coordination index", xlab = NULL)
Compute ANOVA
if (length(unique(buffalo.indiv$pop.type)) > 1) {
anova.model <- aov.mci(mci.buffalo)
anova.model
}
Tukey Test
if (length(unique(buffalo.indiv$pop.type)) > 1) {
TukeyHSD(anova.model)
TukeyHSD(mci.buffalo)
}
Kruskal Test
if (length(unique(buffalo.indiv$pop.type)) > 1) {
kruskal.test(mci.buffalo)
kruskalmc(mci.buffalo)
}
Summary
if (length(unique(buffalo.indiv$pop.type)) > 1) {
summary(mci.buffalo)
}
Extra Plotting
library(RColorBrewer)
g = 11
my.cols <- rev(brewer.pal(g, "RdYlBu"))
Smooth scatter
require(KernSmooth)
smoothScatter(mci.subsample.data$location.long, mci.subsample.data$location.lat,
nrpoints = 0.3 * 1e+05, colramp = colorRampPalette(my.cols), pch = 19, cex = 0.3,
col = "green1")
Kernel density using MASS
library(MASS)
z <- kde2d(mci.subsample.data$location.long, mci.subsample.data$location.lat,
n = 50)
plot(mci.subsample.data$location.long, mci.subsample.data$location.lat, xlab = "X",
ylab = "Y", pch = 19, cex = 0.3, col = "gray60")
contour(z, drawlabels = FALSE, nlevels = g, col = my.cols, add = TRUE, lwd = 2)
abline(h = mean(mci.subsample.data$location.long), v = mean(mci.subsample.data$location.lat),
lwd = 2, col = "black")
legend("topleft", paste("r=", round(cor(mci.subsample.data$location.long, mci.subsample.data$location.lat),
2)), bty = "n")
umd-lib/animalmove documentation built on May 3, 2019, 2:24 p.m.
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Movement Coordination Index
Load package library
library(lubridate)
library(animalmove)
library(plyr)
library(ggplot2)
Subsample Data
Buffalo dataset has been saved in the package data directory , and loaded on the package load.
Raw buffalo dataset contains unaltered original data.
data(buffalo)
head(buffalo)
## event.id visible timestamp location.long location.lat
## 1 10210419 true 2005-02-17 05:05:00.000 31.77 -24.54
## 2 10210423 true 2005-02-17 05:05:00.000 31.77 -24.54
## 3 10210428 true 2005-02-17 06:08:00.000 31.76 -24.54
## 4 10210434 true 2005-02-17 06:08:00.000 31.76 -24.54
## 5 10210456 true 2005-02-17 07:05:00.000 31.76 -24.55
## 6 10210458 true 2005-02-17 08:05:00.000 31.76 -24.55
## behavioural.classification comments manually.marked.outlier sensor.type
## 1 0 24.3 NA gps
## 2 0 24.3 NA gps
## 3 0 29.5 NA gps
## 4 0 29.5 NA gps
## 5 0 35.8 NA gps
## 6 0 37.3 NA gps
## individual.taxon.canonical.name tag.local.identifier
## 1 Syncerus caffer #1764820
## 2 Syncerus caffer #1764820
## 3 Syncerus caffer #1764820
## 4 Syncerus caffer #1764820
## 5 Syncerus caffer #1764820
## 6 Syncerus caffer #1764820
## individual.local.identifier
## 1 Queen
## 2 Queen
## 3 Queen
## 4 Queen
## 5 Queen
## 6 Queen
## study.name utm.easting
## 1 Kruger African Buffalo, GPS tracking, South Africa 375051
## 2 Kruger African Buffalo, GPS tracking, South Africa 375051
## 3 Kruger African Buffalo, GPS tracking, South Africa 374851
## 4 Kruger African Buffalo, GPS tracking, South Africa 374851
## 5 Kruger African Buffalo, GPS tracking, South Africa 374527
## 6 Kruger African Buffalo, GPS tracking, South Africa 374492
## utm.northing utm.zone study.timezone study.local.timestamp
## 1 7285726 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 2 7285726 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 3 7285502 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 4 7285502 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 5 7284538 36S South Africa Standard Time 2005-02-17 09:05:00.000
## 6 7284644 36S South Africa Standard Time 2005-02-17 10:05:00.000
nrow(buffalo)
## [1] 28410
currentnames <- colnames(buffalo)
currentnames
## [1] "event.id" "visible"
## [3] "timestamp" "location.long"
## [5] "location.lat" "behavioural.classification"
## [7] "comments" "manually.marked.outlier"
## [9] "sensor.type" "individual.taxon.canonical.name"
## [11] "tag.local.identifier" "individual.local.identifier"
## [13] "study.name" "utm.easting"
## [15] "utm.northing" "utm.zone"
## [17] "study.timezone" "study.local.timestamp"
names(buffalo)[names(buffalo) == "timestamp"] <- "time"
names(buffalo)[names(buffalo) == "utm.easting"] <- "x"
names(buffalo)[names(buffalo) == "utm.northing"] <- "y"
names(buffalo)[names(buffalo) == "tag.local.identifier"] <- "id"
names(buffalo)[names(buffalo) == "individual.taxon.canonical.name"] <- "pop.type"
# Data Conversion
buffalo$time <- as.POSIXct(strptime(buffalo$time, format = "%Y-%m-%d %H:%M",
tz = "GMT"))
# Display new names
newnames <- colnames(buffalo)
newnames
## [1] "event.id" "visible"
## [3] "time" "location.long"
## [5] "location.lat" "behavioural.classification"
## [7] "comments" "manually.marked.outlier"
## [9] "sensor.type" "pop.type"
## [11] "id" "individual.local.identifier"
## [13] "study.name" "x"
## [15] "y" "utm.zone"
## [17] "study.timezone" "study.local.timestamp"
head(buffalo)
## event.id visible time location.long location.lat
## 1 10210419 true 2005-02-17 05:05:00 31.77 -24.54
## 2 10210423 true 2005-02-17 05:05:00 31.77 -24.54
## 3 10210428 true 2005-02-17 06:08:00 31.76 -24.54
## 4 10210434 true 2005-02-17 06:08:00 31.76 -24.54
## 5 10210456 true 2005-02-17 07:05:00 31.76 -24.55
## 6 10210458 true 2005-02-17 08:05:00 31.76 -24.55
## behavioural.classification comments manually.marked.outlier sensor.type
## 1 0 24.3 NA gps
## 2 0 24.3 NA gps
## 3 0 29.5 NA gps
## 4 0 29.5 NA gps
## 5 0 35.8 NA gps
## 6 0 37.3 NA gps
## pop.type id individual.local.identifier
## 1 Syncerus caffer #1764820 Queen
## 2 Syncerus caffer #1764820 Queen
## 3 Syncerus caffer #1764820 Queen
## 4 Syncerus caffer #1764820 Queen
## 5 Syncerus caffer #1764820 Queen
## 6 Syncerus caffer #1764820 Queen
## study.name x y
## 1 Kruger African Buffalo, GPS tracking, South Africa 375051 7285726
## 2 Kruger African Buffalo, GPS tracking, South Africa 375051 7285726
## 3 Kruger African Buffalo, GPS tracking, South Africa 374851 7285502
## 4 Kruger African Buffalo, GPS tracking, South Africa 374851 7285502
## 5 Kruger African Buffalo, GPS tracking, South Africa 374527 7284538
## 6 Kruger African Buffalo, GPS tracking, South Africa 374492 7284644
## utm.zone study.timezone study.local.timestamp
## 1 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 2 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 3 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 4 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 5 36S South Africa Standard Time 2005-02-17 09:05:00.000
## 6 36S South Africa Standard Time 2005-02-17 10:05:00.000
Number of rows in the buffalo data & data set structure
length(table((buffalo$id)))
## [1] 6
str(buffalo)
## 'data.frame': 28410 obs. of 18 variables:
## $ event.id : int 10210419 10210423 10210428 10210434 10210456 10210458 10210483 10210535 10210544 10210558 ...
## $ visible : chr "true" "true" "true" "true" ...
## $ time : POSIXct, format: "2005-02-17 05:05:00" "2005-02-17 05:05:00" ...
## $ location.long : num 31.8 31.8 31.8 31.8 31.8 ...
## $ location.lat : num -24.5 -24.5 -24.5 -24.5 -24.5 ...
## $ behavioural.classification : int 0 0 0 0 0 0 0 0 0 0 ...
## $ comments : num 24.3 24.3 29.5 29.5 35.8 37.3 38.9 39.6 40 38.1 ...
## $ manually.marked.outlier : logi NA NA NA NA NA NA ...
## $ sensor.type : chr "gps" "gps" "gps" "gps" ...
## $ pop.type : chr "Syncerus caffer" "Syncerus caffer" "Syncerus caffer" "Syncerus caffer" ...
## $ id : chr "#1764820" "#1764820" "#1764820" "#1764820" ...
## $ individual.local.identifier: chr "Queen" "Queen" "Queen" "Queen" ...
## $ study.name : chr "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" ...
## $ x : num 375051 375051 374851 374851 374527 ...
## $ y : num 7285726 7285726 7285502 7285502 7284538 ...
## $ utm.zone : chr "36S" "36S" "36S" "36S" ...
## $ study.timezone : chr "South Africa Standard Time" "South Africa Standard Time" "South Africa Standard Time" "South Africa Standard Time" ...
## $ study.local.timestamp : chr "2005-02-17 07:05:00.000" "2005-02-17 07:05:00.000" "2005-02-17 08:08:00.000" "2005-02-17 08:08:00.000" ...
Subsample data within time interval
We select at most 6 individuals within 2009, time interval 50 hours, and accuracy 50 hours, and subsampling scheme for Realized Mobility Index
mci.subsample.data <- subsample(dat = buffalo, start = c("2005-02-17 00:00:00"),
end = "2006-12-31 00:00:00", interval = c("50 hours"), accuracy = c("3 hours"),
minIndiv = 3, maxIndiv = 6, mustIndiv = NULL, index.type = "mci")
## [1] "50 hours"
## [1] "3 hours"
## [1] "2005-02-17 00:00:00"
## 'data.frame': 8 obs. of 9 variables:
## $ numberOfIndividuals: int 3 4 5 6 5 5 4 3
## $ pairsOfCompleteSets: int 32 19 8 7 9 18 37 49
## $ completeSets : int 34 21 10 9 10 20 39 52
## $ scanInterval : chr "50 hours" "50 hours" "50 hours" "50 hours" ...
## $ scanAccuracy : chr "3 hours" "3 hours" "3 hours" "3 hours" ...
## $ firstScantime : chr "2005-02-17 00:00:00" "2005-02-17 00:00:00" "2005-02-17 00:00:00" "2005-02-17 00:00:00" ...
## $ firstOverlap : chr "2005-07-27 10:00" "2005-08-23 12:00" "2005-09-15 10:00" "2005-09-17 12:00" ...
## $ lastOverlap : chr "2005-10-06 06:00" "2005-10-06 06:00" "2005-10-06 06:00" "2005-10-06 06:00" ...
## $ lastScantime : chr "2006-12-31 00:00:00" "2006-12-31 00:00:00" "2006-12-31 00:00:00" "2006-12-31 00:00:00" ...
buffalo.indiv <- Individuals(mci.subsample.data, id = "id", time = "time", x = "x",
y = "y", group.by = "pop.type", proj4string = CRS("+proj=utm +zone=28 +datum=WGS84"))
Analysis
Compute MCI
mci.buffalo <- mci.index(buffalo.indiv, group.by = c("pop.type"), time.lag = c("time.lag"))
mci.buffalomci.buffalo <- mci.index(buffalo.indiv, group.by = c("pop.type"),
time.lag = c("time.lag"))
mci.buffalo
## An object of class "MCIndex"
## Slot "data":
## scantimes id pop.type mci.index
## 1 2005-09-17 12:00:00 #1764820 Syncerus caffer 0.9954
## 2 2005-09-17 12:00:00 #1764823 Syncerus caffer 0.9985
## 3 2005-09-17 12:00:00 #1764826 Syncerus caffer 0.9959
## 4 2005-09-17 12:00:00 #1764829 Syncerus caffer 0.9964
## 5 2005-09-17 12:00:00 #1764832 Syncerus caffer 0.9977
## 6 2005-09-17 12:00:00 #1764835 Syncerus caffer 0.9954
## 7 2005-09-19 14:00:00 #1764820 Syncerus caffer 0.9954
## 8 2005-09-19 14:00:00 #1764823 Syncerus caffer 0.9985
## 9 2005-09-19 14:00:00 #1764826 Syncerus caffer 0.9959
## 10 2005-09-19 14:00:00 #1764829 Syncerus caffer 0.9964
## 11 2005-09-19 14:00:00 #1764832 Syncerus caffer 0.9977
## 12 2005-09-19 14:00:00 #1764835 Syncerus caffer 0.9954
## 13 2005-09-23 18:00:00 #1764820 Syncerus caffer 0.9954
## 14 2005-09-23 18:00:00 #1764823 Syncerus caffer 0.9985
## 15 2005-09-23 18:00:00 #1764826 Syncerus caffer 0.9959
## 16 2005-09-23 18:00:00 #1764829 Syncerus caffer 0.9964
## 17 2005-09-23 18:00:00 #1764832 Syncerus caffer 0.9977
## 18 2005-09-23 18:00:00 #1764835 Syncerus caffer 0.9954
## 19 2005-09-25 20:00:00 #1764820 Syncerus caffer 0.9954
## 20 2005-09-25 20:00:00 #1764823 Syncerus caffer 0.9985
## 21 2005-09-25 20:00:00 #1764826 Syncerus caffer 0.9959
## 22 2005-09-25 20:00:00 #1764829 Syncerus caffer 0.9964
## 23 2005-09-25 20:00:00 #1764832 Syncerus caffer 0.9977
## 24 2005-09-25 20:00:00 #1764835 Syncerus caffer 0.9954
## 25 2005-09-27 22:00:00 #1764820 Syncerus caffer 0.9954
## 26 2005-09-27 22:00:00 #1764823 Syncerus caffer 0.9985
## 27 2005-09-27 22:00:00 #1764826 Syncerus caffer 0.9959
## 28 2005-09-27 22:00:00 #1764829 Syncerus caffer 0.9964
## 29 2005-09-27 22:00:00 #1764832 Syncerus caffer 0.9977
## 30 2005-09-27 22:00:00 #1764835 Syncerus caffer 0.9954
## 31 2005-09-30 00:00:00 #1764820 Syncerus caffer 0.9954
## 32 2005-09-30 00:00:00 #1764823 Syncerus caffer 0.9985
## 33 2005-09-30 00:00:00 #1764826 Syncerus caffer 0.9959
## 34 2005-09-30 00:00:00 #1764829 Syncerus caffer 0.9964
## 35 2005-09-30 00:00:00 #1764832 Syncerus caffer 0.9977
## 36 2005-09-30 00:00:00 #1764835 Syncerus caffer 0.9954
## 37 2005-10-02 02:00:00 #1764820 Syncerus caffer 0.9954
## 38 2005-10-02 02:00:00 #1764823 Syncerus caffer 0.9985
## 39 2005-10-02 02:00:00 #1764826 Syncerus caffer 0.9959
## 40 2005-10-02 02:00:00 #1764829 Syncerus caffer 0.9964
## 41 2005-10-02 02:00:00 #1764832 Syncerus caffer 0.9977
## 42 2005-10-02 02:00:00 #1764835 Syncerus caffer 0.9954
## 43 2005-10-04 04:00:00 #1764820 Syncerus caffer 0.9954
## 44 2005-10-04 04:00:00 #1764823 Syncerus caffer 0.9985
## 45 2005-10-04 04:00:00 #1764826 Syncerus caffer 0.9959
## 46 2005-10-04 04:00:00 #1764829 Syncerus caffer 0.9964
## 47 2005-10-04 04:00:00 #1764832 Syncerus caffer 0.9977
## 48 2005-10-04 04:00:00 #1764835 Syncerus caffer 0.9954
## 49 2005-10-06 06:00:00 #1764820 Syncerus caffer 0.9954
## 50 2005-10-06 06:00:00 #1764823 Syncerus caffer 0.9985
## 51 2005-10-06 06:00:00 #1764826 Syncerus caffer 0.9959
## 52 2005-10-06 06:00:00 #1764829 Syncerus caffer 0.9964
## 53 2005-10-06 06:00:00 #1764832 Syncerus caffer 0.9977
## 54 2005-10-06 06:00:00 #1764835 Syncerus caffer 0.9954
cexValue = 2
boxplot(mci.index ~ factor(pop.type), data = mci.buffalo, col = "green", border = NULL,
outline = F, lwd = 2, boxwex = 0.5, cex = cexValue, cex.lab = cexValue,
cex.axis = cexValue, frame = F, ylab = "Movement coordination index", xlab = NULL)
Compute ANOVA
if (length(unique(buffalo.indiv$pop.type)) > 1) {
anova.model <- aov.mci(mci.buffalo)
anova.model
}
Tukey Test
if (length(unique(buffalo.indiv$pop.type)) > 1) {
TukeyHSD(anova.model)
TukeyHSD(mci.buffalo)
}
Kruskal Test
if (length(unique(buffalo.indiv$pop.type)) > 1) {
kruskal.test(mci.buffalo)
kruskalmc(mci.buffalo)
}
Summary
if (length(unique(buffalo.indiv$pop.type)) > 1) {
summary(mci.buffalo)
}
Extra Plotting
library(RColorBrewer)
g = 11
my.cols <- rev(brewer.pal(g, "RdYlBu"))
Smooth scatter
require(KernSmooth)
smoothScatter(mci.subsample.data$location.long, mci.subsample.data$location.lat,
nrpoints = 0.3 * 1e+05, colramp = colorRampPalette(my.cols), pch = 19, cex = 0.3,
col = "green1")
Kernel density using MASS
library(MASS)
z <- kde2d(mci.subsample.data$location.long, mci.subsample.data$location.lat,
n = 50)
plot(mci.subsample.data$location.long, mci.subsample.data$location.lat, xlab = "X",
ylab = "Y", pch = 19, cex = 0.3, col = "gray60")
contour(z, drawlabels = FALSE, nlevels = g, col = my.cols, add = TRUE, lwd = 2)
abline(h = mean(mci.subsample.data$location.long), v = mean(mci.subsample.data$location.lat),
lwd = 2, col = "black")
legend("topleft", paste("r=", round(cor(mci.subsample.data$location.long, mci.subsample.data$location.lat),
2)), bty = "n")
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