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R动态布朗运动模型栅格错误

2017-01-23 87 views
0

为了探索我的数据（在某些位置存在/缺失数据）我希望使用“移动”包使用动态布朗运动桥运动模型（dBBMM）对利用率分布进行建模。我试图弄清楚它是如何工作的，所以我创建了一个简单的代码来运行我的数据子集的dBBMM，以创建一些图。似乎相同的编码规范适用于1个子集，但不适用于其他规范。具体来说，我得到的错误返回R动态布朗运动模型栅格错误

Error in .local(object, raster, location.error, ext, ...) : 
    Lower x grid not large enough

这是数据的一个子集：

data.ss1<-structure(list(timestamp = structure(c(1455851760, 1455851880, 
1455852000, 1455852180, 1455857220, 1455857340, 1455915720, 1455915780, 
1455916020, 1455917760, 1455918240, 1455920100, 1455920520, 1455920700, 
1455920940, 1455921060, 1456786200, 1456786620, 1456788960, 1456789080, 
1456789200, 1456821540, 1456821660, 1456821960, 1457295480, 1457295600, 
1457296260, 1457296380, 1457296500, 1457296800, 1457319240, 1457319540, 
1457319660, 1457319780, 1457322900, 1457323020, 1457323140, 1457323320, 
1457323440, 1457325000, 1457325180, 1457325420, 1457325600, 1457325720, 
1457326560, 1457326680, 1457333340, 1457333700, 1457333820, 1457334000, 
1457334120, 1457334240, 1457334360, 1457353800, 1457353920, 1457354040, 
1457354280, 1457354400, 1457354700, 1457355780, 1457355960, 1457356080, 
1457356200, 1457356320, 1457364600, 1457364780, 1457365020, 1457365320, 
1457365500, 1457365620, 1457365740, 1457365860, 1457365980, 1457366100, 
1457366220, 1457407200, 1457407380, 1457407500, 1457407560, 1457407680, 
1457407800, 1457407920, 1457408040, 1457408160, 1457408280, 1457408340, 
1457408580, 1457408700, 1457408820, 1457408940, 1457409060, 1457409480, 
1457409600, 1457409780, 1457409900, 1457410320, 1457412540, 1457412660, 
1457412780, 1457412900), class = c("POSIXct", "POSIXt"), tzone = "US/Eastern"), 
    station = c(109655L, 109655L, 109655L, 109655L, 124083L, 
    124083L, 126404L, 126404L, 126404L, 126412L, 126412L, 126413L, 
    126413L, 126413L, 126413L, 126413L, 102307L, 102307L, 126413L, 
    126413L, 126413L, 126407L, 126407L, 126407L, 124086L, 124086L, 
    124086L, 124086L, 124086L, 124086L, 104668L, 104668L, 104668L, 
    104668L, 126408L, 126408L, 126408L, 126408L, 126408L, 126410L, 
    126410L, 126410L, 126410L, 126410L, 126410L, 126410L, 104668L, 
    104668L, 104668L, 104668L, 104668L, 104668L, 104668L, 124086L, 
    124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 
    124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 
    124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 
    124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 
    124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 
    124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 124086L, 
    124086L, 124086L, 124086L, 124086L), elasmo = structure(c(5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("7954", "19681", 
    "19690", "19691", "20180", "20182", "20184", "23156", "23794", 
    "23796", "27549", "27551"), class = "factor"), location = c("pier", 
    "pier", "pier", "pier", "new barge", "new barge", "bimini barge", 
    "bimini barge", "bimini barge", "west west round rock", "west west round rock", 
    "west north west turtle", "west north west turtle", "west north west turtle", 
    "west north west turtle", "west north west turtle", "south west south turtle", 
    "south west south turtle", "west north west turtle", "west north west turtle", 
    "west north west turtle", "mini wall", "mini wall", "mini wall", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "west round rock", "west round rock", "west round rock", 
    "west round rock", "northwest turtle rock", "northwest turtle rock", 
    "northwest turtle rock", "northwest turtle rock", "northwest turtle rock", 
    "west turtle", "west turtle", "west turtle", "west turtle", 
    "west turtle", "west turtle", "west turtle", "west round rock", 
    "west round rock", "west round rock", "west round rock", 
    "west round rock", "west round rock", "west round rock", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west", "mini barge west", 
    "mini barge west", "mini barge west"), Y = c(25.76453, 25.76453, 
    25.76453, 25.76453, 25.74273, 25.74273, 25.69792, 25.69792, 
    25.69792, 25.68681, 25.68681, 25.67408, 25.67408, 25.67408, 
    25.67408, 25.67408, 25.65654, 25.65654, 25.67408, 25.67408, 
    25.67408, 25.81482, 25.81482, 25.81482, 25.72441, 25.72441, 
    25.72441, 25.72441, 25.72441, 25.72441, 25.68646, 25.68646, 
    25.68646, 25.68646, 25.67416, 25.67416, 25.67416, 25.67416, 
    25.67416, 25.66495, 25.66495, 25.66495, 25.66495, 25.66495, 
    25.66495, 25.66495, 25.68646, 25.68646, 25.68646, 25.68646, 
    25.68646, 25.68646, 25.68646, 25.72441, 25.72441, 25.72441, 
    25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 
    25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 
    25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 
    25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 
    25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 
    25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 
    25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 25.72441, 
    25.72441, 25.72441), X = c(-79.29315, -79.29315, -79.29315, 
    -79.29315, -79.30235, -79.30235, -79.31699, -79.31699, -79.31699, 
    -79.32016, -79.32016, -79.31871, -79.31871, -79.31871, -79.31871, 
    -79.31871, -79.3227, -79.3227, -79.31871, -79.31871, -79.31871, 
    -79.28847, -79.28847, -79.28847, -79.30922, -79.30922, -79.30922, 
    -79.30922, -79.30922, -79.30922, -79.31012, -79.31012, -79.31012, 
    -79.31012, -79.30888, -79.30888, -79.30888, -79.30888, -79.30888, 
    -79.31092, -79.31092, -79.31092, -79.31092, -79.31092, -79.31092, 
    -79.31092, -79.31012, -79.31012, -79.31012, -79.31012, -79.31012, 
    -79.31012, -79.31012, -79.30922, -79.30922, -79.30922, -79.30922, 
    -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, 
    -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, 
    -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, 
    -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, 
    -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, 
    -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, 
    -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, -79.30922, 
    -79.30922)), .Names = c("timestamp", "station", "elasmo", 
"location", "Y", "X"), row.names = c(NA, 100L), class = "data.frame")

这是第二：

data.ss2<-structure(list(timestamp = structure(c(1414786140, 1414786740, 
1420747380, 1420750020, 1425956580, 1425956940, 1427796900, 1427797080, 
1453615800, 1453616040, 1453616400, 1453618020, 1453618920, 1453619580, 
1453619760, 1460017140, 1460017680, 1460017860, 1460141880, 1460142060, 
1460144040, 1460145300, 1460181840, 1460183100, 1460215860, 1460216100, 
1460378040, 1460378760, 1460403840, 1460404140, 1460456100, 1460456280, 
1460456460, 1460540340, 1460541600, 1460863560, 1460864160, 1460875860, 
1460876880, 1460882040, 1460883960, 1460887200, 1460887740, 1460928540, 
1460929200, 1460948160, 1460948340, 1460953920, 1460954220, 1461037440, 
1461038100, 1461041220, 1461041400, 1461041580, 1461043320, 1461043500, 
1461046260, 1461046440, 1461059340, 1461059700, 1461059820, 1461060000, 
1461060720, 1461061440, 1461061860, 1461062100, 1461062700, 1461062880, 
1461063060, 1461063240, 1461063420, 1461064140, 1461064320, 1461064500, 
1461064800, 1461065160, 1461065340, 1461065880, 1461066780, 1461066960, 
1461072060, 1461072300, 1461072900, 1461073080, 1461119580, 1461120240, 
1461124320, 1461124500, 1461124620, 1461166500, 1461166680, 1461166920, 
1461167100, 1461216840, 1461217560, 1461218880, 1461219060, 1461219660, 
1461219840, 1461221280), class = c("POSIXct", "POSIXt"), tzone = "US/Eastern"), 
    station = c(104667L, 104667L, 124097L, 124097L, 125904L, 
    125904L, 125907L, 125907L, 126408L, 126408L, 126408L, 126410L, 
    126410L, 126411L, 126411L, 126406L, 126406L, 126406L, 125906L, 
    125906L, 125906L, 125906L, 125904L, 126406L, 125904L, 125904L, 
    106809L, 106809L, 125906L, 125906L, 126408L, 126408L, 126408L, 
    126408L, 126408L, 126408L, 126408L, 126410L, 126410L, 126408L, 
    126408L, 126411L, 126411L, 104668L, 104668L, 104668L, 104668L, 
    126411L, 126411L, 104668L, 104668L, 126410L, 126410L, 126410L, 
    126411L, 126411L, 126411L, 126411L, 126404L, 126404L, 126404L, 
    126404L, 126404L, 126404L, 126404L, 126404L, 126404L, 126404L, 
    126404L, 126404L, 126404L, 126404L, 126404L, 126404L, 126404L, 
    126404L, 126404L, 126404L, 126404L, 126404L, 126404L, 126404L, 
    126404L, 126404L, 104668L, 104668L, 126410L, 126410L, 126410L, 
    126408L, 126408L, 126408L, 126408L, 126413L, 126413L, 104668L, 
    104668L, 104668L, 104668L, 126408L), elasmo = structure(c(11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L), .Label = c("7954", "19681", "19690", "19691", 
    "20180", "20182", "20184", "23156", "23794", "23796", "27549", 
    "27551"), class = "factor"), location = c("bimini barge", 
    "bimini barge", "northwest turtle rock", "northwest turtle rock", 
    "west turtle", "west turtle", "west west round rock", "west west round rock", 
    "northwest turtle rock", "northwest turtle rock", "northwest turtle rock", 
    "west turtle", "west turtle", "south west turtle", "south west turtle", 
    "south bimini 3", "south bimini 3", "south bimini 3", "south bimini 1", 
    "south bimini 1", "south bimini 1", "south bimini 1", "south bimini 3 south", 
    "south bimini 3", "south bimini 3 south", "south bimini 3 south", 
    "south bimini 1 south", "south bimini 1 south", "south bimini 1", 
    "south bimini 1", "northwest turtle rock", "northwest turtle rock", 
    "northwest turtle rock", "northwest turtle rock", "northwest turtle rock", 
    "northwest turtle rock", "northwest turtle rock", "west turtle", 
    "west turtle", "northwest turtle rock", "northwest turtle rock", 
    "south west turtle", "south west turtle", "west round rock", 
    "west round rock", "west round rock", "west round rock", 
    "south west turtle", "south west turtle", "west round rock", 
    "west round rock", "west turtle", "west turtle", "west turtle", 
    "south west turtle", "south west turtle", "south west turtle", 
    "south west turtle", "bimini barge", "bimini barge", "bimini barge", 
    "bimini barge", "bimini barge", "bimini barge", "bimini barge", 
    "bimini barge", "bimini barge", "bimini barge", "bimini barge", 
    "bimini barge", "bimini barge", "bimini barge", "bimini barge", 
    "bimini barge", "bimini barge", "bimini barge", "bimini barge", 
    "bimini barge", "bimini barge", "bimini barge", "bimini barge", 
    "bimini barge", "bimini barge", "bimini barge", "west round rock", 
    "west round rock", "west turtle", "west turtle", "west turtle", 
    "northwest turtle rock", "northwest turtle rock", "northwest turtle rock", 
    "northwest turtle rock", "west north west turtle", "west north west turtle", 
    "west round rock", "west round rock", "west round rock", 
    "west round rock", "northwest turtle rock"), Y = c(25.69792, 
    25.69792, 25.67416, 25.67416, 25.66495, 25.66495, 25.68681, 
    25.68681, 25.67416, 25.67416, 25.67416, 25.66495, 25.66495, 
    25.65662, 25.65662, 25.68256, 25.68256, 25.68256, 25.68266, 
    25.68266, 25.68266, 25.68266, 25.67332, 25.68256, 25.67332, 
    25.67332, 25.67316, 25.67316, 25.68266, 25.68266, 25.67416, 
    25.67416, 25.67416, 25.67416, 25.67416, 25.67416, 25.67416, 
    25.66495, 25.66495, 25.67416, 25.67416, 25.65662, 25.65662, 
    25.68646, 25.68646, 25.68646, 25.68646, 25.65662, 25.65662, 
    25.68646, 25.68646, 25.66495, 25.66495, 25.66495, 25.65662, 
    25.65662, 25.65662, 25.65662, 25.69792, 25.69792, 25.69792, 
    25.69792, 25.69792, 25.69792, 25.69792, 25.69792, 25.69792, 
    25.69792, 25.69792, 25.69792, 25.69792, 25.69792, 25.69792, 
    25.69792, 25.69792, 25.69792, 25.69792, 25.69792, 25.69792, 
    25.69792, 25.69792, 25.69792, 25.69792, 25.69792, 25.68646, 
    25.68646, 25.66495, 25.66495, 25.66495, 25.67416, 25.67416, 
    25.67416, 25.67416, 25.67408, 25.67408, 25.68646, 25.68646, 
    25.68646, 25.68646, 25.67416), X = c(-79.31699, -79.31699, 
    -79.30888, -79.30888, -79.31092, -79.31092, -79.32016, -79.32016, 
    -79.30888, -79.30888, -79.30888, -79.31092, -79.31092, -79.31325, 
    -79.31325, -79.2818, -79.2818, -79.2818, -79.30135, -79.30135, 
    -79.30135, -79.30135, -79.28196, -79.2818, -79.28196, -79.28196, 
    -79.30154, -79.30154, -79.30135, -79.30135, -79.30888, -79.30888, 
    -79.30888, -79.30888, -79.30888, -79.30888, -79.30888, -79.31092, 
    -79.31092, -79.30888, -79.30888, -79.31325, -79.31325, -79.31012, 
    -79.31012, -79.31012, -79.31012, -79.31325, -79.31325, -79.31012, 
    -79.31012, -79.31092, -79.31092, -79.31092, -79.31325, -79.31325, 
    -79.31325, -79.31325, -79.31699, -79.31699, -79.31699, -79.31699, 
    -79.31699, -79.31699, -79.31699, -79.31699, -79.31699, -79.31699, 
    -79.31699, -79.31699, -79.31699, -79.31699, -79.31699, -79.31699, 
    -79.31699, -79.31699, -79.31699, -79.31699, -79.31699, -79.31699, 
    -79.31699, -79.31699, -79.31699, -79.31699, -79.31012, -79.31012, 
    -79.31092, -79.31092, -79.31092, -79.30888, -79.30888, -79.30888, 
    -79.30888, -79.31871, -79.31871, -79.31012, -79.31012, -79.31012, 
    -79.31012, -79.30888)), .Names = c("timestamp", "station", 
"elasmo", "location", "Y", "X"), row.names = c(NA, 100L), class = "data.frame")

我的问题是，我不真的知道如何设置与扩展参数相结合的栅格参数。它给出给定指定模型的1个数据集的错误，但不是另一个。这些只是大型个人数据集的2个迷你子集，而且我有> 100个人。因此，如果我能够指定一个能够尽可能容纳所有数据集的模型，那将会很有用。我想我可以将ext参数设置为我的所有模型的大量参数，但我不确定这是否是理想和最佳方法。感谢您的任何帮助！

这是我使用的子集将R代码：

require(move) 

ss1 <- move(x=data.ss1$X, y=data.ss1$Y, time=as.POSIXct(data.ss1$timestamp, format="%Y-%m-%d %H:%M", tz="US/Eastern"), 
      proj=CRS("+proj=longlat +ellps=WGS84"),data=data.ss1, animal="20186") 

data.sAEQD1 <- spTransform(ss1, center=T) 

x_UD1 <- brownian.bridge.dyn(data.sAEQD1, window.size=31, ext=1.5, 
          margin=15, raster=100, 
          location.error=23) 
ss2 <- move(x=data.ss2$X, y=data.ss2$Y, time=as.POSIXct(data.ss2$timestamp, format="%Y-%m-%d %H:%M", tz="US/Eastern"), 
      proj=CRS("+proj=longlat +ellps=WGS84"),data=data.ss2, animal="27549") 

data.sAEQD2 <- spTransform(ss2, center=T) 

x_UD2 <- brownian.bridge.dyn(data.sAEQD2, window.size=31, ext=1.5, 
          margin=15, raster=100, 
          location.error=23)

来源

2017-01-23 FlyingDutch

回答

1

有几句话，首先，因为2.0版本的移动包设置在很大程度上（EXT）的不以很长的计算时间为代价，因此设置一个更高的数字（如2或3）不会造成伤害。

栅格参数设置用于UD计算的栅格分辨率。这对计算时间有重大影响。如果分辨率是两倍大的计算速度应该快4倍。

ext参数确定栅格延伸超出轨迹范围的程度。

鉴于这是来自记录站的数据，检查模型结果尤其重要，因为长时间的平稳性可能导致非常小的西格玛估计。我还注意到，在录音之间有时会有相当长的时间间隔（10天，2分钟录音），这通常会导致UD伸展得相当宽，因为动物的位置有很大的不确定性。这也会导致录音速度很慢，因为数值积分选择了一个小时间步长，设置time.step = .6左右可能会加快速度（默认情况下，time.step设置为最短间隔的1/15） 。

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2017-01-23 21:00:47 Bart

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