I'm trying to create pretty x10^ notation for my graphs and I've tried to follow this thread, R scientific notation in plots, where the package sfmisc works just fine. However, no matter how I format my data the eaxis(2) (intended to format the axis) has zero effect on my plot.
What am I missing?
test <- structure(list(y = c(57074, 55293, 52530, 53874, 56858, 53661,
55945, 54193, 55163, 57583, 54338, 54641, 54614, 54193, 52836,
55719, 55002, 55507, 53366, 51949, 53235, 55353, 55474, 54739,
55133, 53775, 52727, 53495, 52972, 55992, 53175, 54166, 55292,
54872, 53192, 54397, 53942, 55366, 53018, 52682, 53622, 55850,
55789, 54557, 54848, 56765, 52820, 53870, 55348, 53204, 51285,
53042, 54367, 52928, 52964, 52627, 56657, 55148, 58890, 53701,
54805, 54079, 54131, 57278, 56781, 55465, 56775, 52513, 55431,
55771, 54583, 57358, 54072, 55033, 53156, 53696, 52911, 54076,
54462, 56536, 53085, 52569, 53266, 54299, 55848, 54259, 53627,
52437, 52220, 52433, 50191, 52412, 50372, 50910, 50455, 50815,
50701, 50746, 49721, 51859, 49399, 51059, 52265, 52060, 52218,
52301, 50035, 49646, 52039, 48850, 51266, 52174, 49523, 51270,
55213, 54284, 54485, 54310, 54911, 54777, 57283, 55636, 55980,
56536, 55678, 55285, 55472, 51623, 53241, 54765, 53099, 53019,
51083, 54019, 54929, 52155, 53065, 52312, 52634, 53296, 56518,
54127, 54694, 55895, 55742, 53929, 55745, 53512, 54497, 51416,
52731, 53866, 53657, 53252, 54001, 53491, 52997, 52558, 52332,
54119, 55268, 51572, 51772, 59194, 54984, 55794, 54978, 52114,
54799, 55598, 53137, 52842, 53786, 53036, 53468, 54336, 52490,
53004, 57766, 54496, 52993, 56228, 56760, 54302, 54288, 54655,
53952, 57342, 54309, 52621, 55790, 57443, 56504, 53485, 51602,
54750, 55084, 54772, 53335, 51547, 55785, 52702, 53834, 53291,
49956, 52136, 53654, 57252, 56039, 52538, 55018, 55865, 54330,
54124, 51033, 55412, 54307, 54392, 54136, 53871, 53653, 54696,
53771, 52851, 54716, 54913, 56345, 54308, 55249, 55373, 55690,
54914, 56746, 53988, 57099, 54371, 56105, 57137, 59156, 55028,
55581, 55535, 56482, 55476, 53783, 56046, 53198, 53563, 55850,
55683, 54875, 55898, 53729, 54716, 54212, 54092, 55754, 56013,
55246, 53031, 54555, 55019, 53641, 54781, 55561, 54474, 51939,
53266, 54739, 56521, 54364, 54790, 55421, 55069, 57094, 54506,
58930, 54327, 54123, 54634, 57260, 55882, 55877, 54139, 54970,
53891, 53422, 53744, 55637, 57497, 57123, 53079, 53758, 56498,
56829, 54675, 54230, 59734, 54357, 55031, 52837, 54500, 55159,
55749, 56107, 54042, 56369, 53032, 54262, 52873, 54448, 57216,
51369, 57130, 54384, 56405, 55455, 55829, 54439, 54263, 56004,
55406, 56198, 55726, 57043, 56563, 56367, 53822, 53942, 54641,
56275, 57232, 52531, 58216, 52519, 54866, 53658, 55304, 55083,
56328, 57085, 57240, 53078, 54329, 54543, 55873, 55025, 54464,
55384, 56595, 52406, 52001, 57799, 57266, 54401, 51738, 54082,
52939, 55056, 56213, 56398, 53303, 53542, 54507, 57726, 53327,
54101, 53053, 55762, 54932, 56123, 54218, 52915, 54611, 54422,
53886, 54051, 52986, 55194, 55142, 53504, 53295, 54768, 55053,
49884, 54736, 55189, 54206, 53849, 53789, 54086, 55643, 51360,
54165, 51264, 53291, 57359, 56053, 55976, 57091, 55735, 53533,
56725, 52441, 54994, 54917, 54974, 54096, 55083, 52477, 56169,
56359, 54426, 55544, 51978, 54035, 56876, 57854, 55146, 56639,
51805, 55647, 56116, 56271, 53695, 56423, 54165, 54180, 56357,
54237, 56700, 53289, 56073, 55123, 55444, 55155, 52194, 54319,
57223, 56239, 54243, 53742, 54085, 57219, 53422, 53783, 53100,
55414, 54684, 57014, 60754, 55931, 58489, 55270, 53538, 55047,
56150, 54381, 54541, 54766, 56071, 53033, 54239, 56986, 55029,
54969, 56272, 56499, 53772, 55771, 53209, 58113, 56394, 54774,
53949, 56244, 53717, 54025, 56384, 55484, 56039, 56903, 55552,
53577, 54915, 53726, 53180, 55989, 54755, 55858, 56673, 53346,
55448, 53865, 54942, 54082, 52907, 54950, 52756, 55881, 55814,
52827, 53975, 54206, 55546, 56302, 53321, 55201, 55058, 53752,
53509, 54106, 53114, 53601, 51200, 53728, 58390, 54606, 50244,
54820, 51741, 54901, 56244, 54045, 54321, 48708, 48849, 50568,
51046, 49521, 55125, 55263, 51129, 52098, 53514, 53308, 53484,
56802, 52715, 52036, 54830, 55266, 55201, 54889, 52897, 55511,
53511, 54425, 54776, 55302, 54407, 56235, 55209, 54099, 55264,
53536, 54010, 54778, 56237, 55288, 54575, 54293, 57440, 52406,
55186, 55728, 54548, 53119, 53555, 53402, 54795, 57850, 55597,
55913, 52279, 53603, 55519, 55947, 53623, 51781, 52208, 56492,
55805, 54583, 52270, 51461, 52020, 54355, 51711, 53720, 52368,
54067, 57222, 55416, 53904, 52201, 54070, 54909, 54515, 55301,
54601, 54641, 55289, 53188, 56281, 56059, 56244, 54388, 54634,
54453, 52560, 53829, 49529, 49866, 52235, 50962, 52210, 49999,
52891, 53887, 54851, 53912, 52974, 55562, 51006, 52187, 55993,
54228, 50763, 52544, 55946, 53833, 51390, 55123, 54710, 52034,
52058, 51769, 52550, 54152, 53859, 53770, 55579, 55697, 53553,
53328, 58090, 55302, 55555, 54815, 55887, 57412, 52425, 53808,
53989, 54318, 55453, 53097, 51731, 49651, 56463, 54621, 55636,
53732, 53388, 55845, 55520, 56869, 54371, 55273, 55443, 56710,
54771, 56720, 56277, 53507, 55654, 54104, 58184, 55087, 54472,
56111, 56677, 54988, 54142, 53161, 55053, 53725, 54838, 53708,
54175, 55121, 51161, 56567, 56086, 56392, 56494, 55197, 54527,
55227), x = structure(c(" 1", " 2", " 3", " 4", " 5", " 6",
" 7", " 8", " 9", " 10", " 11", " 12", " 13", " 14", " 15",
" 16", " 17", " 18", " 19", " 20", " 21", " 22", " 23", " 24",
" 25", " 26", " 27", " 28", " 29", " 30", " 31", " 32", " 33",
" 34", " 35", " 36", " 37", " 38", " 39", " 40", " 41", " 42",
" 43", " 44", " 45", " 46", " 47", " 48", " 49", " 50", " 51",
" 52", " 53", " 54", " 55", " 56", " 57", " 58", " 59", " 60",
" 61", " 62", " 63", " 64", " 65", " 66", " 67", " 68", " 69",
" 70", " 71", " 72", " 73", " 74", " 75", " 76", " 77", " 78",
" 79", " 80", " 81", " 82", " 83", " 84", " 85", " 86", " 87",
" 88", " 89", " 90", " 91", " 92", " 93", " 94", " 95", " 96",
" 97", " 98", " 99", "100", "101", "102", "103", "104", "105",
"106", "107", "108", "109", "110", "111", "112", "113", "114",
"115", "116", "117", "118", "119", "120", "121", "122", "123",
"124", "125", "126", "127", "128", "129", "130", "131", "132",
"133", "134", "135", "136", "137", "138", "139", "140", "141",
"142", "143", "144", "145", "146", "147", "148", "149", "150",
"151", "152", "153", "154", "155", "156", "157", "158", "159",
"160", "161", "162", "163", "164", "165", "166", "167", "168",
"169", "170", "171", "172", "173", "174", "175", "176", "177",
"178", "179", "180", "181", "182", "183", "184", "185", "186",
"187", "188", "189", "190", "191", "192", "193", "194", "195",
"196", "197", "198", "199", "200", "201", "202", "203", "204",
"205", "206", "207", "208", "209", "210", "211", "212", "213",
"214", "215", "216", "217", "218", "219", "220", "221", "222",
"223", "224", "225", "226", "227", "228", "229", "230", "231",
"232", "233", "234", "235", "236", "237", "238", "239", "240",
"241", "242", "243", "244", "245", "246", "247", "248", "249",
"250", "251", "252", "253", "254", "255", "256", "257", "258",
"259", "260", "261", "262", "263", "264", "265", "266", "267",
"268", "269", "270", "271", "272", "273", "274", "275", "276",
"277", "278", "279", "280", "281", "282", "283", "284", "285",
"286", "287", "288", "289", "290", "291", "292", "293", "294",
"295", "296", "297", "298", "299", "300", "301", "302", "303",
"304", "305", "306", "307", "308", "309", "310", "311", "312",
"313", "314", "315", "316", "317", "318", "319", "320", "321",
"322", "323", "324", "325", "326", "327", "328", "329", "330",
"331", "332", "333", "334", "335", "336", "337", "338", "339",
"340", "341", "342", "343", "344", "345", "346", "347", "348",
"349", "350", "351", "352", "353", "354", "355", "356", "357",
"358", "359", "360", "361", "362", "363", "364", "365", "366",
"367", "368", "369", "370", "371", "372", "373", "374", "375",
"376", "377", "378", "379", "380", "381", "382", "383", "384",
"385", "386", "387", "388", "389", "390", "391", "392", "393",
"394", "395", "396", "397", "398", "399", "400", "401", "402",
"403", "404", "405", "406", "407", "408", "409", "410", "411",
"412", "413", "414", "415", "416", "417", "418", "419", "420",
"421", "422", "423", "424", "425", "426", "427", "428", "429",
"430", "431", "432", "433", "434", "435", "436", "437", "438",
"439", "440", "441", "442", "443", "444", "445", "446", "447",
"448", "449", "450", "451", "452", "453", "454", "455", "456",
"457", "458", "459", "460", "461", "462", "463", "464", "465",
"466", "467", "468", "469", "470", "471", "472", "473", "474",
"475", "476", "477", "478", "479", "480", "481", "482", "483",
"484", "485", "486", "487", "488", "489", "490", "491", "492",
"493", "494", "495", "496", "497", "498", "499", "500", "501",
"502", "503", "504", "505", "506", "507", "508", "509", "510",
"511", "512", "513", "514", "515", "516", "517", "518", "519",
"520", "521", "522", "523", "524", "525", "526", "527", "528",
"529", "530", "531", "532", "533", "534", "535", "536", "537",
"538", "539", "540", "541", "542", "543", "544", "545", "546",
"547", "548", "549", "550", "551", "552", "553", "554", "555",
"556", "557", "558", "559", "560", "561", "562", "563", "564",
"565", "566", "567", "568", "569", "570", "571", "572", "573",
"574", "575", "576", "577", "578", "579", "580", "581", "582",
"583", "584", "585", "586", "587", "588", "589", "590", "591",
"592", "593", "594", "595", "596", "597", "598", "599", "600",
"601", "602", "603", "604", "605", "606", "607", "608", "609",
"610", "611", "612", "613", "614", "615", "616", "617", "618",
"619", "620", "621", "622", "623", "624", "625", "626", "627",
"628", "629", "630", "631", "632", "633", "634", "635", "636",
"637", "638", "639", "640", "641", "642", "643", "644", "645",
"646", "647", "648", "649", "650", "651", "652", "653", "654",
"655", "656", "657", "658", "659", "660", "661", "662", "663",
"664", "665", "666", "667", "668", "669", "670", "671", "672",
"673", "674", "675", "676", "677", "678", "679", "680", "681",
"682", "683", "684", "685", "686", "687", "688", "689", "690",
"691", "692", "693", "694", "695", "696", "697", "698", "699",
"700"), class = "AsIs")), .Names = c("y", "x"), row.names = c(NA,
-700L), class = "data.frame")
plot(x = test$x, y = test$y, type = "l",
xaxt="n", yaxt="n")
require("sfsmisc")
eaxis(2)
Something like this can be easily done with ggplot2 as #DaveRGP suggested, e.g.:
library(ggplot2)
library(scales)
test$x <- as.numeric(as.character(test$x))
test$y <- as.numeric(as.character(test$y))
ggplot(data = test, aes(x = x, y = y)) + geom_line() +
scale_y_continuous(trans = "log10", breaks = trans_breaks("log10", function(x) 10 ^ (x)),
labels = trans_format("log10", math_format(10 ^ .x)))
Related
I am running a random forest comparing PC_Countto multiple variables
rf_h_count_km <- randomForest (
formula = PC_count ~NLCD+Pop_Focal+NLUD+Population+Urban+Slope+NLCD21_f+NLCD22_f+NLCD23_f+NLCD24_f+Density+PC,
data = training,
method = 'rf',
tuneGrid = tuneGrid,
ntree = 100,
nodesize = 5,
importance = TRUE,
trControl = ctrl,
metric = "ROC"
)
The random forest ran successfully, but now I am trying to convert the output into a prediction raster. Seeing that the predictor variable is otained from a dataframe and the independent variables from a raster stack I have not found a way to do it directly
#predict the response variable with training data
l<-raster::predict(rf_h_count_km, type="response", data=testing, index=2, na.rm=TRUE, progress="window", overwrite=TRUE)
I converted the output array into datframe format
x<-as.data.frame(l)
names(x) <- c("try")
Secondly, I created an empty raster with the dimensions needed
r = raster(ncols=225, nrows=176, xmn=-1788075, xmx=-1584465, ymn=1481625, ymx=1717755, res=1000)
Thirdly, I tried adding the values from the dataframe to the raster. However, this produces an error
> raster::values(r)<-x$try
Error in setValues(x, value) :
length(values) is not equal to ncell(x), or to 1
Is there any way that I could add these values into the raster without producing the error?
Below is a segment of the 1-column dataframe
> dput(head(x,500))
structure(list(try = c(1.25482243953606, 1.29704685733086, 1.59604755116753,
1.27323310015838, 1.65523336838096, 1.16055734781969, 1.91659455992709,
1.52214228365861, 1.39167234672577, 1.39345512204005, 1.14707832355435,
1.38027338524104, 1.43416846811582, 1.19860901400402, 1.36468712553493,
1.39169436843932, 1.331203829109, 1.92116276432308, 1.76695170198865,
1.29033362603409, 1.49713229698021, 1.43769464696184, 1.68270039388572,
1.44802123045305, 1.48484344231137, 1.5923432415293, 1.89790742744141,
1.32407450302128, 1.41150242816569, 1.56883137580953, 1.39132737542988,
1.34693405858518, 1.70515171668636, 1.787306308579, 1.53811871225996,
1.54649976632651, 1.37557485902264, 1.42460519475178, 1.84602421808187,
1.46107494748112, 1.48574351291447, 1.55515920286892, 1.47435149169411,
2.06774644973887, 1.67011263561363, 1.53676870286262, 1.51437261683124,
1.65344434977873, 1.52521033303975, 1.33428311000463, 1.86538942543929,
1.1728691498302, 1.17687652159234, 1.80537815643604, 1.49692701357964,
1.00050456141199, 1.47365424021922, 1.47114102470081, 1.39277787775097,
0.865697705973826, 1.50096649740468, 1.13385260841033, 1.65271338239792,
1.46884690867827, 1.481906677787, 1.14199273373277, 1.68774387988497,
1.51872838325436, 1.38328649616044, 1.67114602131701, 1.54848407844392,
1.3996624303754, 1.71789148484286, 1.33823346156167, 1.25566170818395,
1.55020641523366, 1.17128373170123, 1.26915332769989, 1.66256952986062,
1.50038471501031, 1.30659761867545, 1.38389413400123, 1.59603869890816,
1.28059405159913, 1.49876041289129, 1.45692568883059, 1.82472848165875,
1.23544716197641, 1.30844258992342, 1.62576923439109, 1.26939004412273,
1.58507687431416, 1.31481901026701, 1.4575809162353, 1.53809208050283,
1.16766113344713, 1.56314563435862, 1.55411214306813, 1.78357145366888,
1.4329858089636, 1.43396598308919, 1.39465154299149, 1.48451992338695,
1.24684337200367, 1.96110469737173, 1.16625138690017, 0.775810330702323,
1.00045993259336, 1.37686548806939, 1.62733354682162, 1.68759141765908,
1.628166880637, 1.94206520355245, 1.61238598818564, 1.96885931093973,
1.34625059573652, 1.31831686519609, 1.53290631646635, 1.57775601413376,
1.58363330493889, 1.89319841817133, 0.782837422055985, 1.64643379323501,
1.66712161497969, 1.29699891021702, 1.47429558125218, 1.68691877809511,
1.74608775523785, 1.9991622606389, 1.56607510448966, 1.6309907225318,
1.46078033728118, 1.17142033440308, 1.40255145198544, 1.33112985551097,
1.15166500669548, 1.80419274569205, 1.62729203127811, 1.83685487185426,
1.58635321656375, 1.82779066393067, 1.34477410566094, 1.49887518851983,
1.50264666884424, 1.30559742049033, 1.70529841717443, 1.36461045525143,
1.35656649147087, 1.37880956124176, 1.32825774615472, 1.4743468228756,
1.68848953739118, 1.5714969472275, 1.28051735582015, 1.70382287292112,
1.48697666114366, 1.52509854662891, 2.00215110778257, 1.51573583601317,
1.52652734904295, 1.83725972310248, 1.43565914024433, 1.65558396263679,
1.28093702179809, 1.34220737362985, 1.45510583039298, 1.34188715043272,
1.59560616172484, 1.18911884147528, 1.76384130089723, 1.14960238421033,
1.36430236202792, 1.62519404962719, 1.41158597502356, 0.797289908270849,
1.15520086855207, 1.42517561255296, 1.00047571138336, 1.31365223086707,
1.4129217354065, 1.29535822063539, 1.67078827797151, 1.54029946373479,
1.84666725485803, 1.31394353069208, 1.30563028604674, 1.81450886504348,
1.19491662297257, 1.27229869878179, 1.28963013410991, 1.43762367763379,
1.16928002123748, 0.76442403537918, 1.28823684878266, 1.74785996977029,
1.64959703066888, 1.5509430052318, 1.45843245579434, 1.63183302686946,
1.59733661443473, 0.760843648977963, 1.36684005161429, 1.84519682070654,
1.50049023315098, 1.7050215606835, 1.62763853632365, 1.56200633371077,
1.51191419294519, 1.6116950079444, 1.76167964082101, 1.6283596872148,
1.60698192017333, 1.66810316469347, 1.16126887133296, 1.38968215164831,
1.48455441438511, 0.660013152279427, 1.53085391999465, 1.82842047029258,
1.1661862475647, 1.00044570589583, 1.70599622205831, 1.38431550482051,
1.50410961983431, 1.61241963890188, 1.68592745635435, 1.36708568021482,
1.49941207893968, 1.48543479320907, 1.35809437513195, 1.55296348681227,
1.59819516191763, 1.48481302388378, 1.4061746887853, 1.36929246790332,
1.39266730855954, 1.46027138849061, 1.53273782134419, 1.16136716380526,
1.56032756345299, 1.76303556273634, 1.30009474488364, 0.713431047919119,
1.27230670052624, 1.74416452489221, 1.61122408130062, 1.89634699686347,
1.47106580278593, 1.28149781048158, 1.55039187691515, 1.70574909049325,
1.30713212157724, 1.52846086526416, 1.67346330500358, 1.43438661455238,
1.18645458508454, 1.63936712727192, 1.41112353469103, 1.2761061429259,
1.27580024677754, 1.3072009173607, 0.76482905669465, 1.48493548485036,
1.62951834284185, 1.26147044940371, 1.51789060097793, 1.41333216708036,
1.33639249077728, 1.45542697933075, 1.43760708672506, 1.71199034891044,
1.26027699097399, 1.32370075969687, 1.14957580510731, 1.42015421688412,
1.80770319259332, 1.56027185359019, 1.26335891477022, 1.14435742981936,
1.21496018704641, 1.1581564288297, 1.53379063258365, 1.51294803803159,
1.30732487742837, 1.43717349783508, 1.43486086288793, 1.50187858120187,
1.15120725634633, 1.35400674186641, 1.55364027339135, 1.94536872847578,
1.710630763992, 1.71391827834412, 1.00050653319808, 1.30922140163975,
1.66640189638562, 1.70458770847952, 1.41382683700093, 1.54886023547989,
1.62626167586269, 1.91473409741524, 1.63101761509542, 1.67304098596854,
1.43182019801906, 1.14567627381978, 1.35238386442408, 1.64751122114616,
1.34873996399922, 1.37023850292472, 1.53564601207952, 1.57505619181632,
1.36708995300055, 1.56941164647469, 1.16925475135763, 0.842871188459787,
1.3812699305909, 1.41386414196727, 1.16059409444905, 1.76201853912225,
1.44222009921562, 1.64997144444006, 1.38376159924388, 1.61003199654573,
1.57499790571938, 1.15201686470987, 1.76676689613511, 1.8096908759832,
1.20486311868743, 1.68728039392101, 1.42500419002418, 1.44665607038376,
1.58123291895195, 1.14005656747451, 1.48678222431433, 1.44661082228836,
1.61171967442807, 1.30729777317031, 1.42286763782785, 1.40553596608522,
1.55064563919909, 1.70051648043645, 1.69303294109564, 1.5515686820675,
1.49226483353159, 1.43554567483609, 1.30857962097702, 1.41632801810122,
1.66756491810872, 1.44526232593268, 1.48719674319771, 1.42309996893368,
1.6580257935528, 1.5829194496966, 1.64235861450852, 1.44895677424997,
1.61443646370592, 1.68792404047021, 1.56680875163758, 1.63772239523653,
1.38406187171508, 1.18445033651953, 1.80761413246068, 1.52670027322229,
1.13013642070425, 1.56261574480113, 1.6888386513478, 1.00051404040559,
1.53839461415883, 1.58065043297887, 1.45576137913913, 1.49989463209582,
1.28055237635367, 1.50010654014454, 1.42137860072691, 1.56511565854225,
1.70639749591321, 1.41508801317881, 1.73233307347998, 1.13841982707047,
1.4400662466764, 1.80565275760731, 0.77520490363639, 1.61527499647451,
1.32672943129696, 1.76607190217432, 1.27583861087918, 1.73820202046061,
1.51335423345056, 1.30921314993083, 1.89626987520185, 1.4473406270278,
1.35322135964277, 2.01512208326191, 1.50393203460877, 1.68770896622325,
0.805436684457629, 1.37740579297073, 1.76790174733803, 1.6562649800791,
1.34223556479203, 1.15246311383752, 1.68485931618636, 1.40568909806569,
0.784724015738059, 1.49886040116948, 1.8272016947891, 1.76469931390133,
1.26688645784432, 1.72643807856103, 1.43467491769798, 1.26459859241559,
1.68797176508759, 1.46051637830131, 1.49869552936372, 1.14441765225327,
1.57745957616188, 1.28307917058032, 1.74574595151118, 1.35334508600179,
1.59272719674702, 1.19935222312585, 1.35880915261704, 1.15239419992794,
1.4044441578425, 1.39219305326424, 1.44629956051605, 2.01162723040148,
1.4711194451495, 1.62984928328262, 1.29120536667572, 1.7437705740116,
1.32490447606648, 1.73704376707315, 1.60847808850637, 1.38597284699774,
1.59628605075663, 1.43444007060914, 1.3244765426713, 1.36864321432182,
1.60706449020901, 1.14400820678228, 1.28098051291041, 1.59293724647213,
1.20693559821733, 0.762694890744079, 1.00051265006564, 1.15238864138768,
1.94340119252715, 1.18820627573647, 1.36578984019797, 1.78610658908017,
1.6139282437022, 1.56664750472243, 1.28225859203438, 1.351183496751,
1.74883140168085, 1.98997982109868, 1.36718566083602, 1.81044434104092,
1.27607390816209, 1.45917517513078, 1.46192984151842, 1.72610404482554,
1.6265388077098, 1.55430788004514, 1.44485886502457, 1.73699539353876,
1.48594712331721, 1.42357556702341, 1.32457308337899, 1.55064010993579,
1.42291188618139, 1.64818926255211, 1.13841846135019, 1.95209192401635,
1.66937085634394, 1.55376682098252, 1.624705399401, 1.30640121517372,
1.28092432425333, 1.42589541938036, 1.41146701872041, 1.14830789689793,
1.3160788417034, 1.58225727505092, 1.16056978361285, 1.4883577330535,
1.41232141834121, 1.52493787348832, 1.62487746013324, 1.43269182322572,
1.55049696656887, 1.53525920970974, 1.31609276967797, 1.68663626612582,
1.46992801989381, 1.51568282463528, 1.56295971478745, 1.43171059023851,
1.48486531481598)), row.names = c("4", "5", "8", "9", "10", "13",
"15", "16", "17", "18", "19", "20", "21", "22", "23", "26", "27",
"28", "29", "31", "34", "35", "37", "39", "40", "41", "42", "43",
"44", "45", "46", "47", "48", "49", "50", "51", "54", "55", "56",
"57", "59", "60", "61", "62", "63", "64", "66", "67", "68", "71",
"72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82",
"83", "84", "85", "86", "87", "88", "89", "90", "92", "93", "94",
"95", "96", "97", "98", "99", "100", "102", "103", "104", "105",
"106", "107", "108", "109", "111", "112", "113", "114", "115",
"117", "118", "119", "120", "121", "123", "124", "125", "126",
"127", "128", "130", "131", "132", "133", "134", "135", "136",
"137", "138", "139", "141", "143", "144", "145", "147", "148",
"150", "152", "153", "154", "156", "158", "159", "162", "163",
"165", "168", "169", "172", "174", "175", "176", "177", "178",
"179", "180", "182", "183", "184", "185", "186", "187", "188",
"190", "191", "194", "196", "197", "198", "199", "202", "204",
"205", "206", "208", "209", "211", "212", "213", "214", "215",
"216", "217", "218", "219", "220", "221", "223", "224", "227",
"228", "229", "230", "231", "232", "233", "236", "238", "239",
"240", "241", "242", "243", "244", "245", "248", "251", "254",
"255", "257", "258", "259", "260", "262", "263", "264", "265",
"266", "268", "270", "271", "272", "273", "274", "276", "278",
"279", "280", "281", "282", "283", "284", "285", "286", "287",
"288", "289", "290", "292", "293", "295", "296", "297", "300",
"301", "304", "307", "309", "310", "312", "314", "315", "316",
"317", "318", "319", "320", "321", "322", "323", "324", "325",
"326", "327", "328", "330", "331", "332", "333", "334", "336",
"339", "340", "345", "346", "349", "350", "351", "352", "353",
"355", "356", "358", "359", "360", "362", "363", "364", "365",
"366", "367", "368", "369", "370", "371", "372", "373", "376",
"377", "378", "380", "381", "382", "383", "384", "385", "386",
"391", "392", "395", "396", "398", "399", "400", "403", "404",
"405", "406", "407", "408", "409", "410", "411", "413", "414",
"415", "417", "418", "419", "420", "421", "422", "424", "426",
"427", "428", "429", "430", "431", "433", "434", "435", "436",
"437", "441", "442", "443", "446", "447", "449", "450", "451",
"453", "454", "457", "458", "459", "460", "463", "465", "466",
"467", "468", "469", "470", "471", "472", "473", "475", "476",
"477", "478", "479", "480", "481", "482", "483", "484", "486",
"487", "489", "490", "491", "493", "494", "495", "498", "499",
"500", "502", "503", "506", "508", "509", "510", "511", "512",
"513", "514", "515", "516", "517", "520", "521", "522", "523",
"524", "525", "526", "527", "528", "529", "530", "531", "532",
"533", "534", "537", "539", "541", "543", "545", "546", "548",
"549", "550", "551", "552", "554", "555", "556", "558", "559",
"560", "561", "562", "563", "564", "565", "566", "567", "568",
"569", "570", "571", "572", "573", "574", "575", "576", "577",
"579", "580", "581", "582", "583", "584", "585", "586", "588",
"589", "591", "592", "593", "596", "598", "599", "601", "602",
"604", "606", "608", "609", "610", "611", "614", "615", "616",
"617", "618", "619", "620", "621", "623", "626", "627", "628",
"629", "630", "632", "633", "634", "635", "636", "637", "638",
"639", "640", "641", "642", "644", "645", "646", "647", "649",
"650", "651", "653", "654", "655", "656", "657", "658", "659",
"660", "662", "663", "664"), class = "data.frame")
Here is how you can do that with terra (the replacement for raster)
library(terra)
logo <- rast(system.file("ex/logo.tif", package="terra"))
names(logo) <- c("red", "green", "blue")
p <- matrix(c(48, 48, 48, 53, 50, 46, 54, 70, 84, 85, 74, 84, 95, 85,
66, 42, 26, 4, 19, 17, 7, 14, 26, 29, 39, 45, 51, 56, 46, 38, 31,
22, 34, 60, 70, 73, 63, 46, 43, 28), ncol=2)
a <- matrix(c(22, 33, 64, 85, 92, 94, 59, 27, 30, 64, 60, 33, 31, 9,
99, 67, 15, 5, 4, 30, 8, 37, 42, 27, 19, 69, 60, 73, 3, 5, 21,
37, 52, 70, 74, 9, 13, 4, 17, 47), ncol=2)
xy <- rbind(cbind(1, p), cbind(0, a))
e <- extract(logo, xy[,2:3])
v <- data.frame(cbind(pa=xy[,1], e[,-1]))
library(randomForest)
rfm <- randomForest(formula=pa~., data=v)
p <- predict(logo, rfm)
I copied this from the help file ?terra::predict. The same example for raster is available in raster::predict.
Note that the first argument to predict should be a SpatRaster (or a Raster* for the raster package). What you do:
l <- raster::predict(rf_h_count_km,...)
is wrong because that just gives you the predictions for the model training data, as you do not provide the raster data to predict to.
((and raster::predict is a generic function that will select the appropriate implementation based on the first argument. In this case it will just go to randomForest::predict))
Unless you trained your model with all your raster data, in which case you could use something like
rfma <- randomForest(formula=red~., data=as.data.frame(logo))
r <- rast(logo, nlyr=1)
values(r) <- predict(rfma)
I have a curve likes the following:
As can be seen, there are two upper bound and lower bound curves on the above (see the following image for the approx visualization!):
I have tried to find the lower bound by the following code:
lower_bound = rbind()
for(i in 2:(length(data[,1])-1))
{
if(data[i,2] < data[i+1,2] && data[i,2] < data[i-1,2])
{
lower_bound = rbind2(lower_bound, c(data[i,1], data[i,2]))
}
}
But, the problem is there are some local minima in the middle, so the result is not what I've expected (as follow).
Hence the question is how can I find the lower bound of such a curve in R? Is there any known algorithm to find the lower bound?
About the minimal reproducible example, I've included 500 of data (to make sense) in the following:
structure(c(3.54246575342466, 5.75616438356164, 6.54246575342466,
9.73424657534247, 7.75616438356164, 9.75616438356164, 1.41917808219178,
2.75342465753425, 3.16986301369863, 2.27123287671233, 3.74520547945205,
5.74520547945205, 4.29315068493151, 3.37260273972603, 8.37534246575343,
3.33698630136986, 5.04657534246575, 6.92328767123288, 1.33972602739726,
6.34246575342466, 26.358904109589, 2.34794520547945, 4.35068493150685,
7.35342465753425, 27.3671232876712, 1.15068493150685, 2.14794520547945,
4.15342465753425, 6.15342465753425, 9.15616438356164, 19.1616438356164,
29.1698630136986, 2.58904109589041, 4.5972602739726, 9.6, 29.613698630137,
3.10684931506849, 8.11780821917808, 3.35068493150685, 8.37260273972603,
5.11506849315068, 28.1315068493151, 5.36986301369863, 28.386301369863,
4.87945205479452, 4.46027397260274, 5.35616438356164, 25.3698630136986,
25.8684931506849, 5.85479452054795, 1.85205479452055, 4.37260273972603,
6.37260273972603, 9.37534246575343, 19.3835616438356, 29.3890410958904,
18.3808219178082, 1.36986301369863, 8.37534246575343, 3.37260273972603,
5.85479452054795, 28.3890410958904, 2.87671232876712, 4.87945205479452,
7.37808219178082, 8.17260273972603, 28.186301369863, 4.29315068493151,
6.96164383561644, 26.3479452054795, 7.96438356164384, 1.53972602739726,
1.58630136986301, 5.04657534246575, 16.6356164383562, 18.4684931506849,
25.0602739726027, 15.6493150684932, 24.8547945205479, 25.3506849315068,
26.1068493150685, 16.0547945205479, 2.16712328767123, 19.2657534246575,
20.6849315068493, 1.04383561643836, 3.04383561643836, 23.0575342465753,
3.29315068493151, 4.13150684931507, 2.04383561643836, 22.0575342465753,
22.8904109589041, 2.87671232876712, 5.54246575342466, 25.5561643835616,
1.04931506849315, 6.05205479452055, 26.0657534246575, 2.09041095890411,
7.09315068493151, 27.1068493150685, 2.09041095890411, 4.09315068493151,
6.09315068493151, 9.0958904109589, 19.1013698630137, 29.1095890410959,
1.83287671232877, 1.42739726027397, 3.43013698630137, 9.3041095890411,
2.72328767123288, 8.58082191780822, 2.8958904109589, 4.92054794520548,
9.92328767123288, 6.92054794520548, 19.9315068493151, 29.9369863013699,
20.9315068493151, 2.25205479452055, 22.2657534246575, 3.40821917808219,
23.4219178082192, 8.21095890410959, 3.20821917808219, 5.20821917808219,
18.8027397260274, 19.8054794520548, 21.7232876712329, 2.25205479452055,
22.8054794520548, 24.4301369863014, 4.41643835616438, 25.2684931506849,
5.25479452054795, 6.33698630136986, 26.3506849315068, 2.87671232876712,
4.29315068493151, 2.78630136986301, 1.2958904109589, 1.75890410958904,
2.29041095890411, 7.2958904109589, 3.44657534246575, 4.5041095890411,
9.73424657534247, 8.21643835616438, 6.29315068493151, 7.50684931506849,
3.75616438356164, 7.96438356164384, 9.96438356164384, 29.9780821917808,
17.3452054794521, 26.8547945205479, 5.5041095890411, 7.17260273972603,
6.13150684931507, 3.62739726027397, 5.79452054794521, 7.7972602739726,
5.75616438356164, 3.09041095890411, 8.0958904109589, 9.63013698630137,
4.93424657534247, 6.93424657534247, 9.93698630136986, 19.9452054794521,
6.42191780821918, 8.96438356164384, 28.9780821917808, 15.7643835616438,
18.7205479452055, 1.20547945205479, 4.16986301369863, 24.1835616438356,
25.2520547945205, 26.3095890410959, 9.61917808219178, 12.3424657534247,
14.2986301369863, 23.7698630136986, 3.71232876712329, 8.71506849315069,
3.58082191780822, 1.25205479452055, 5.96164383561644, 3.46027397260274,
8.46301369863014, 7.88219178082192, 5.54246575342466, 6.46027397260274,
4.79452054794521, 3.20821917808219, 15.5506849315068, 9.5972602739726,
20.6, 2.09041095890411, 1.87671232876712, 22.2191780821918, 5.67397260273973,
25.7260273972603, 26.9753424657534, 7.2958904109589, 27.5205479452055,
8.17260273972603, 28.3095890410959, 29.2246575342466, 3.87945205479452,
9.13424657534247, 5.84109589041096, 3.96164383561644, 4.54246575342466,
9.92602739726027, 29.9397260273973, 16.1397260273973, 16.1780821917808,
16.1397260273973, 18.7643835616438, 18.7643835616438, 20.2191780821918,
20.6383561643836, 20.8904109589041, 1.16712328767123, 21.2657534246575,
1.33424657534247, 2.04383561643836, 2.20547945205479, 4.75616438356164,
2.92054794520548, 23.2219178082192, 4.09315068493151, 4.24931506849315,
5.13150684931507, 5.96164383561644, 6.20821917808219, 27.3917808219178,
29.2246575342466, 9.75890410958904, 8.75890410958904, 28.2712328767123,
8.42465753424658, 28.4383561643836, 2.37260273972603, 4.37534246575343,
4.75616438356164, 1.67123287671233, 3.96164383561644, 2.37260273972603,
5.04657534246575, 2.25205479452055, 5.62739726027397, 4.29315068493151,
1.29041095890411, 3.5041095890411, 5.5041095890411, 5.09315068493151,
3.83561643835616, 8.46301369863014, 3.42191780821918, 1.33424657534247,
4.5041095890411, 4.79452054794521, 6.37534246575343, 7.21095890410959,
9.37808219178082, 29.3534246575342, 3.54246575342466, 28.6904109589041,
8.2958904109589, 8.54246575342466, 4.62739726027397, 12.4082191780822,
19.4712328767123, 20.972602739726, 3.67397260273973, 4.42191780821918,
4.46027397260274, 25.3890410958904, 5.87945205479452, 25.8931506849315,
2.20547945205479, 27.2246575342466, 7.88219178082192, 1.43561643835616,
2.43561643835616, 4.46027397260274, 6.46027397260274, 9.46301369863014,
19.4712328767123, 29.4767123287671, 2.45753424657534, 9.63013698630137,
7.88219178082192, 23.641095890411, 5.13150684931507, 6.46027397260274,
9.7972602739726, 29.8109589041096, 3.21095890410959, 8.21643835616438,
4.92328767123288, 3.25479452054795, 2.62465753424658, 3.71232876712329,
5.75616438356164, 25.7698630136986, 6.20821917808219, 1.20547945205479,
4.96164383561644, 2.09041095890411, 4.13150684931507, 2.45753424657534,
27.4767123287671, 7.46301369863014, 3.2, 18.2164383561644, 1.1972602739726,
5.20821917808219, 8.21095890410959, 28.2246575342466, 19.972602739726,
7.50684931506849, 9.92602739726027, 3.75616438356164, 1.38904109589041,
3.39178082191781, 19.4082191780822, 2.04383561643836, 24.0602739726027,
5.75616438356164, 26.4356164383562, 6.42191780821918, 28.0630136986301,
8.21095890410959, 18.2164383561644, 6.37534246575343, 3.96164383561644,
6.87945205479452, 7.75890410958904, 5.54246575342466, 7.75890410958904,
2.19452054794521, 27.213698630137, 29.227397260274, 8.2, 5.87945205479452,
1.12876712328767, 3.12876712328767, 7.54520547945206, 2.74246575342466,
4.74520547945205, 6.74520547945205, 9.74794520547945, 29.7616438356164,
3.58904109589041, 8.53150684931507, 18.5369863013699, 28.5452054794521,
4.96164383561644, 19.3479452054795, 1.04657534246575, 21.386301369863,
3.96164383561644, 5.42191780821918, 3.62739726027397, 7.13150684931507,
2.54246575342466, 27.5616438356164, 18.0547945205479, 4.16986301369863,
9.25753424657534, 4.76986301369863, 15.2164383561644, 6.37534246575343,
8.17808219178082, 3.73424657534247, 1.26849315068493, 6.27123287671233,
6.2986301369863, 19.3945205479452, 17.1397260273973, 1.20547945205479,
6.20821917808219, 3.37534246575342, 5.25479452054795, 4.75616438356164,
6.54246575342466, 3.16986301369863, 2.04383561643836, 4.25479452054795,
1.04383561643836, 7.71506849315069, 5.09315068493151, 4.42191780821918,
6.71232876712329, 3.04383561643836, 1.67945205479452, 3.30958904109589,
3.96164383561644, 2.92054794520548, 7.92602739726027, 27.9397260273973,
2.83835616438356, 7.46301369863014, 8.21095890410959, 7.23835616438356,
27.2520547945205, 2.53972602739726, 6.58904109589041, 9.96438356164384,
29.9780821917808, 16.4301369863014, 18.3671232876712, 19.2657534246575,
20.386301369863, 2.37534246575342, 6.46027397260274, 26.4739726027397,
6.62739726027397, 4.75616438356164, 6.71232876712329, 28.8958904109589,
15.5123287671233, 14.8465753424658, 14.8465753424658, 2.0027397260274,
3.20821917808219, 3.71232876712329, 1.6958904109589, 2.07671232876712,
7.75616438356164, 2.88493150684931, 4.29315068493151, 1.84931506849315,
8.8958904109589, 1.12328767123288, 5.38630136986301, 5.12602739726027,
8.25205479452055, 3.24931506849315, 1.38904109589041, 5.89315068493151,
6.60821917808219, 1.5013698630137, 3.46027397260274, 6.5041095890411,
7.75890410958904, 6.54246575342466, 9.37808219178082, 3.96164383561644,
5.04657534246575, 7.04657534246575, 8.13424657534247, 6.13150684931507,
9.37808219178082, 7.96438356164384, 11.0493150684932, 12.213698630137,
2.12876712328767, 6.67397260273973, 4.46027397260274, 24.4739726027397,
3.75616438356164, 8.75890410958904, 18.7643835616438, 8.46301369863014,
5.13150684931507, 9.88219178082192, 9.33150684931507, 5.96164383561644,
7.25753424657534, 5.62739726027397, 7.50684931506849, 4.84109589041096,
3.46027397260274, 3.92054794520548, 18.5095890410959, 7.13150684931507,
8.59178082191781, 3.42191780821918, 6.75616438356164, 4.75616438356164,
2.95890410958904, 3.96164383561644, 20.4328767123288, 1.41917808219178,
21.4328767123288, 22.1041095890411, 2.09041095890411, 23.2219178082192,
3.20821917808219, 3.75616438356164, 24.7698630136986, 25.4739726027397,
4.46027397260274, 2.75970346198464, 3.45063169924232, 3.64711503029937,
4.21614538082358, 4.46897200885451, 4.78330432863798, 2.44945159376164,
3.0246987676982, 3.18637219409113, 2.82704067767991, 3.39633877759394,
4.01211492772477, 3.58227507536714, 3.26211339894416, 4.58021400951056,
3.24894671597498, 3.81640051029768, 4.29811641034397, 2.41235239871336,
4.16381972504066, 4.82320604897032, 2.85924829032196, 3.60102245001335,
4.38947242891732, 4.83225858506487, 2.32276057802484, 2.77466777269801,
3.53613679638981, 4.11732178923756, 4.70239811454869, 5.02684596680901,
4.89394362719873, 2.95858956427959, 3.67972641713649, 4.76329504168897,
4.91952178329268, 3.16243766754998, 4.53553009720828, 3.25401782240246,
4.57975033696973, 3.83648225102819, 4.85066377882221, 3.90946630290976,
4.85924427553214, 3.7665702648057, 3.63632959242775, 3.90561093988307,
4.82873462476434, 4.25993172383506, 3.47661513525158, 2.08147939877802,
3.04373161945452, 3.60670989847648, 4.16882946922235, 4.45461902145002,
4.34162315280691, 4.48802758727908, 1.86206866940942, 4.01581962612883,
2.69771901556242, 3.47661513525158, 4.29494911958988, 2.50903075277185,
3.20217588142397, 3.83011128813872, 3.980829920259, 4.28800748360142,
3.58227507536714, 4.30653857989399, 4.82319396445967, 4.50784277427558,
1.94072216406505, 1.96202863703256, 3.25200612691594, 4.52865722521467,
4.48533445537866, 4.26859229653523, 4.53731981080683, 4.27200767176305,
4.26457120140531, 4.2589693893104, 4.53525135174211, 2.21846989417729,
4.45880330075475, 4.40569121171677, 1.7068990376281, 2.57392106736465,
4.31836655689863, 2.66826592342152, 2.96442641830962, 2.16545787147435,
4.35324929683238, 4.32386522416303, 2.50903075277185, 3.39298854017977,
4.26232802572813, 1.70955300282675, 3.52741837222827, 4.25907996756536,
2.18557386001823, 3.77060768900683, 4.26395740412398, 2.18557386001823,
2.95157192131744, 3.53780855712753, 4.12926524629975, 4.46454130003024,
4.32641578537161, 2.63737387449928, 2.45327077112083, 3.28325793775465,
4.41181850146463, 3.01267661647382, 4.30273228804522, 3.0809392572752,
3.77893332429253, 4.80391883502991, 4.29751273896985, 4.9988952974639,
4.94101980513708, 4.96053762255352, 2.81894331471379, 4.91003396772332,
3.27522101695249, 4.87134979305318, 4.55194984744145, 3.20084942592193,
3.86347792664764, 5.03902446306499, 5.00360541053749, 4.93013307063464,
2.81894331471379, 4.89111202391337, 4.84484889948005, 3.62226986596664,
4.83000471370643, 3.87684000847915, 4.16249148956975, 4.82319682171546,
3.07342513615359, 3.58227507536714, 3.03776390795916, 2.39174353424389,
2.60441382154721, 2.83511983796416, 4.3776430683761, 3.28927099619789,
3.65030540624117, 4.78053976420514, 3.98851220598087, 3.84027419541323,
4.10888701756863, 3.08864056496118, 4.50784277427558, 4.80886282388512,
4.94393108371142, 5.0798798303101, 4.82563811883379, 3.9468402869224,
4.35189109140176, 4.11184051977588, 3.35459034230467, 4.02517710657812,
4.47676297113471, 4.01502608262676, 2.59176865382458, 3.96722952163555,
4.76721313996148, 3.21864402185455, 3.73613392685585, 4.2411779069777,
4.43398643591996, 3.61855452792053, 4.10980345553821, 4.31985435471796,
5.10135192405871, 5.04171773535286, 2.34892141444876, 3.5416097666887,
4.85054794533227, 4.83022204838818, 4.82316539945316, 4.76579152488111,
4.70585851301182, 4.77956222001954, 4.54973985879503, 3.38475186086735,
4.63579216766113, 3.33790973940492, 2.37103458556849, 3.75717008349914,
2.98272273951349, 4.28337045807013, 4.49267828838454, 3.9573829235615,
4.19209673281346, 3.74079199043232, 3.20084942592193, 5.10187826143602,
4.76293750094991, 4.97336905684238, 2.74996824339996, 2.65677488819793,
4.91172311677223, 3.99307443262087, 4.82526350378577, 4.8268073878022,
4.3776430683761, 4.83510606972691, 4.54522430364073, 4.85652654574101,
4.89686171640296, 2.87874743905149, 4.13483923719495, 4.03742401175551,
3.47139417367879, 3.66246558484484, 4.80424996332984, 4.94121262270349,
4.53454492449136, 4.53419598722523, 4.53454492449136, 4.47589167424468,
4.47589167424468, 4.42360799906947, 4.4074923871952, 4.39773506579433,
2.01907578318878, 4.38322128535129, 1.84538737297096, 2.41830268718988,
2.23480800909955, 3.16465475329972, 2.52618004042805, 4.56596058005526,
2.95157192131744, 3.00349899341948, 3.52972300068793, 3.75717008349914,
3.56654832534446, 4.26829419274457, 4.33246733302122, 4.47209933166968,
4.07829496689505, 4.54366179335202, 4.02412309850527, 4.29676089988903,
2.8695390825768, 3.60901246346208, 3.72904913668145, 2.56498963399193,
3.47139417367879, 2.8695390825768, 3.81640051029768, 2.81894331471379,
3.98051398879079, 3.58227507536714, 2.38916039360867, 3.31021811457649,
3.9468402869224, 3.83007753533476, 3.42794910435245, 4.59492002573633,
3.28024670656985, 2.4097817563527, 3.65030540624117, 3.74079199043232,
4.17176477555296, 4.35996196359352, 4.73359981456944, 4.90398228783007,
3.32409784536479, 4.87118581003356, 4.56666593488235, 4.60802805678997,
3.68916442604293, 5.02118529619339, 4.4514690518147, 4.39455164410878,
2.80677702722226, 3.05963754502245, 3.07193520904601, 4.2641136733005,
3.48305015568073, 4.25979660315916, 2.23480800909955, 4.26558395648925,
3.9282839050028, 1.89269207014021, 2.33130752327983, 3.07193520904601,
3.62770234943172, 4.18074663670204, 4.4514690518147, 4.34675554764834,
2.34036194508005, 4.20281875657975, 4.49267828838454, 4.86491005913173,
3.84127256835413, 4.19209673281346, 4.78844960315538, 4.93234475967529,
3.20188086629451, 4.5529065893626, 3.7797549774533, 3.21833597902597,
2.97302436839012, 3.38475186086735, 4.01502608262676, 4.82494685548552,
4.1309427087262, 2.34892141444876, 3.79122469981557, 2.74996824339996,
3.52882080169136, 2.90475632846179, 4.83425050183613, 4.41167553240461,
3.19775298791517, 5.05733248819791, 2.34500731502677, 3.86347792664764,
4.55194984744145, 4.8536532162969, 4.68580090262939, 3.8560422018531,
4.80424996332984, 2.83579574924565, 2.43541801827223, 3.26917867887851,
4.70658092262176, 2.41830268718988, 4.54205071405147, 3.70347651496055,
4.51179026913548, 3.87139934363606, 4.53702297816656, 4.24040027977525,
4.74578292053171, 4.17176477555296, 3.15984460601259, 3.9768746297032,
4.46949323821673, 3.9573829235615, 4.46949323821673, 2.79454182212808,
4.82981722014996, 4.89700936635987, 4.55003332824908, 4.04744453906246,
2.31225215646125, 3.17078397447093, 4.42804643606615, 3.02033214617563,
3.72568245460231, 4.25829574912718, 4.78226928394965, 4.92905316482749,
3.34086061969711, 4.04183801378363, 4.48319605565641, 4.30085456068988,
3.79122469981557, 4.45589006029623, 1.70822621819291, 4.63142865759546,
2.90699979029706, 3.35965176254463, 2.79019595892293, 3.77878982474076,
2.37522576957373, 4.27154459842833, 4.49758018625893, 2.97721538330697,
4.15244511731778, 3.73325024745895, 5.10139379386631, 4.17176477555296,
4.54618813621284, 3.39248194962301, 2.37881218415977, 4.1464619591812,
4.15316130101696, 4.70707172705424, 4.77267338855031, 2.03737184678255,
3.81939314106, 3.26312377270195, 3.87684000847915, 3.72904913668145,
4.21150941368065, 2.87482262642492, 2.41830268718988, 3.25814616479328,
1.95974385334363, 4.14957253152334, 3.51852796766856, 3.31248236073798,
3.93926475863266, 2.82676588308018, 2.25715273670682, 3.23877823069724,
3.47139417367879, 3.09057442380978, 4.50079498118852, 4.84503359841119,
3.05834404390917, 4.41167553240461, 4.55194984744145, 4.36569411888241,
4.83039001292176, 2.93850101323729, 4.22239549435516, 4.80886282388512,
4.94393108371142, 5.09584661299229, 5.05283820324407, 5.02319768413649,
4.67005370266932, 2.87068065591085, 4.19209673281346, 4.82343926231588,
3.91974901504793, 3.72904913668145, 4.25081432629887, 4.88033737221285,
4.53751058675557, 5.09889228357165, 5.09889228357165, 2.7120126164778,
3.20084942592193, 3.38475186086735, 2.57611729759449, 2.74406304100437,
4.46897200885461, 3.07664762906049, 3.58227507536714, 2.64466064962826,
4.66384481684621, 2.30962110926233, 3.91408252149265, 3.83967705403174,
4.55910078340349, 3.21628402040912, 2.43541801827223, 4.05100906700729,
4.22685401869853, 2.48748627144419, 3.29427230717969, 4.20248230878606,
4.46949323821673, 4.21150941368065, 4.73359981456944, 3.47139417367879,
3.81640051029768, 4.32499408591158, 4.53844903952499, 4.11184051977588,
4.73359981456944, 4.50784277427558, 4.92221662489616, 5.00972596707877,
2.76645294455757, 4.24203451863899, 3.63632959242775, 4.8438946910502,
3.40019013262738, 4.64268935027678, 5.04028605762641, 4.59492002573633,
3.84127256835413, 4.79892563273548, 4.41562762186783, 4.06871965116534,
4.36969042097439, 3.98051398879079, 4.42043658523483, 3.75496657158094,
3.29427230717969, 3.45730608585978, 5.04844935470778, 3.77878982474076,
4.05166542652766, 3.28024670656985, 4.26078052262918, 3.72904913668145,
3.10550475341744, 3.47139417367879, 4.41541846233588, 1.88505721037991,
4.3767970882106, 4.35153448481756, 2.18557386001823, 4.31311576433973,
2.6364550425402, 2.83579574924565, 4.27355104887647, 4.26316357649486,
3.07193520904601), .Dim = c(500L, 2L), .Dimnames = list(c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46",
"47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57",
"58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68",
"69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79",
"80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90",
"91", "92", "93", "94", "95", "96", "97", "98", "99", "100",
"101", "102", "103", "104", "105", "106", "107", "108", "109",
"110", "111", "112", "113", "114", "115", "116", "117", "118",
"119", "120", "121", "122", "123", "124", "125", "126", "127",
"128", "129", "130", "131", "132", "133", "134", "135", "136",
"137", "138", "139", "140", "141", "142", "143", "144", "145",
"146", "147", "148", "149", "150", "151", "152", "153", "154",
"155", "156", "157", "158", "159", "160", "161", "162", "163",
"164", "165", "166", "167", "168", "169", "170", "171", "172",
"173", "174", "175", "176", "177", "178", "179", "180", "181",
"182", "183", "184", "185", "186", "187", "188", "189", "190",
"191", "192", "193", "194", "195", "196", "197", "198", "199",
"200", "201", "202", "203", "204", "205", "206", "207", "208",
"209", "210", "211", "212", "213", "214", "215", "216", "217",
"218", "219", "220", "221", "222", "223", "224", "225", "226",
"227", "228", "229", "230", "231", "232", "233", "234", "235",
"236", "237", "238", "239", "240", "241", "242", "243", "244",
"245", "246", "247", "248", "249", "250", "251", "252", "253",
"254", "255", "256", "257", "258", "259", "260", "261", "262",
"263", "264", "265", "266", "267", "268", "269", "270", "271",
"272", "273", "274", "275", "276", "277", "278", "279", "280",
"281", "282", "283", "284", "285", "286", "287", "288", "289",
"290", "291", "292", "293", "294", "295", "296", "297", "298",
"299", "300", "301", "302", "303", "304", "305", "306", "307",
"308", "309", "310", "311", "312", "313", "314", "315", "316",
"317", "318", "319", "320", "321", "322", "323", "324", "325",
"326", "327", "328", "329", "330", "331", "332", "333", "334",
"335", "336", "337", "338", "339", "340", "341", "342", "343",
"344", "345", "346", "347", "348", "349", "350", "351", "352",
"353", "354", "355", "356", "357", "358", "359", "360", "361",
"362", "363", "364", "365", "366", "367", "368", "369", "370",
"371", "372", "373", "374", "375", "376", "377", "378", "379",
"380", "381", "382", "383", "384", "385", "386", "387", "388",
"389", "390", "391", "392", "393", "394", "395", "396", "397",
"398", "399", "400", "401", "402", "403", "404", "405", "406",
"407", "408", "409", "410", "411", "412", "413", "414", "415",
"416", "417", "418", "419", "420", "421", "422", "423", "424",
"425", "426", "427", "428", "429", "430", "431", "432", "433",
"434", "435", "436", "437", "438", "439", "440", "441", "442",
"443", "444", "445", "446", "447", "448", "449", "450", "451",
"452", "453", "454", "455", "456", "457", "458", "459", "460",
"461", "462", "463", "464", "465", "466", "467", "468", "469",
"470", "471", "472", "473", "474", "475", "476", "477", "478",
"479", "480", "481", "482", "483", "484", "485", "486", "487",
"488", "489", "490", "491", "492", "493", "494", "495", "496",
"497", "498", "499", "500"), NULL))
For a relatively simple implementation, you can try as follows:
Consider a running slice of the data, with a constant width. The width must be larger than the largest gap.
Construct the upper and lower hull of the points, using the Monotone Chain algorithm (there is no need to sort, your points are already by increasing abscissa). https://en.wikibooks.org/wiki/Algorithm_Implementation/Geometry/Convex_hull/Monotone_chain
For the middle abscissa in the slice, estimate the two ordinates on the convex hull (by identifying the hull edges at that particular absicssa). By sweeping, this will create the desired curves. At both ends of the range, use all abscissas in the first and last half-slices.
There will be a small downward bias where the curvature is high and/or the window is large, but nobody's perfect.
The total running time will be proportional to the product of the number of points and the window width. But I guess that an incremental version can do better.
There is a linear programming approach to this, and you might find R packages that will do linear programming for you.
Find a space of smooth curves such as splines, where a smooth curve is a linear combination of basis functions.
Now solve the linear program to minimize the sum, taken over all points, of the value at that point minus the value from a linear combination of basis functions at that point, subject to the constraint that the linear combination of basis functions must be no larger than the value at that point.
When I attempt to create a Beanplot for some data I get the following error
library(beanplot)
beanplot(error ~ rates, data = result[result$stream=="Speelyai", ],
col=c("orange", "black", "white", "red"), border ="pink",
what=c(0,1,1,1), maxstripline=.20)
Error in bw.SJ(x, method = "dpi") : sample is too sparse to find TD
Here is an attempt at using dput to provide a reproducible example
dput(head(result, 20))
structure(list(stream = c("Brooks", "Siouxon", "Speelyai", "Brooks",
"Siouxon", "Speelyai", "Brooks", "Siouxon", "Speelyai", "Brooks",
"Siouxon", "Speelyai", "Brooks", "Siouxon", "Speelyai", "Brooks",
"Siouxon", "Speelyai", "Brooks", "Siouxon"), n = c(3, 3, 3, 4,
4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9), rates = c(3,
3, 3, 7.5, 7.5, 7.5, 10, 10, 10, 13, 13, 13, 15, 15, 15, 17,
17, 17, 19.5, 19.5), means = c(0.962915209069, 0.583192916943,
0.619923506082, 0.972270732503, 0.586503057099, 0.6118633820775,
0.9637475111, 0.580441970844, 0.5959238270885, 0.948657459436,
0.5846854247605, 0.559714620214, 0.954776205607, 0.582733153046333,
0.588721324855, 0.927079783491, 0.589401183924667, 0.584910716913,
0.9468615412695, 0.578929242163667), column = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = c("1", "2", "3", "4", "5", "6", "7", "8",
"9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19",
"20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30",
"31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41",
"42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52",
"53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63",
"64", "65", "66", "67", "68", "69", "70", "71", "72", "73", "74",
"75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85",
"86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "96",
"97", "98", "99", "100", "101", "102", "103", "104", "105", "106",
"107", "108", "109", "110", "111", "112", "113", "114", "115",
"116", "117", "118", "119", "120", "121", "122", "123", "124",
"125", "126", "127", "128", "129", "130", "131", "132", "133",
"134", "135", "136", "137", "138", "139", "140", "141", "142",
"143", "144", "145", "146", "147", "148", "149", "150", "151",
"152", "153", "154", "155", "156", "157", "158", "159", "160",
"161", "162", "163", "164", "165", "166", "167", "168", "169",
"170", "171", "172", "173", "174", "175", "176", "177", "178",
"179", "180", "181", "182", "183", "184", "185", "186", "187",
"188", "189", "190", "191", "192", "193", "194", "195", "196",
"197", "198", "199", "200", "201", "202", "203", "204", "205",
"206", "207", "208", "209", "210", "211", "212", "213", "214",
"215", "216", "217", "218", "219", "220", "221", "222", "223",
"224", "225", "226", "227", "228", "229", "230", "231", "232",
"233", "234", "235", "236", "237", "238", "239", "240", "241",
"242", "243", "244", "245", "246", "247", "248", "249", "250",
"251", "252", "253", "254", "255", "256", "257", "258", "259",
"260", "261", "262", "263", "264", "265", "266", "267", "268",
"269", "270", "271", "272", "273", "274", "275", "276", "277",
"278", "279", "280", "281", "282", "283", "284", "285", "286",
"287", "288", "289", "290", "291", "292", "293", "294", "295",
"296", "297", "298", "299", "300", "301", "302", "303", "304",
"305", "306", "307", "308", "309", "310", "311", "312", "313",
"314", "315", "316", "317", "318", "319", "320", "321", "322",
"323", "324", "325", "326", "327", "328", "329", "330", "331",
"332", "333", "334", "335", "336", "337", "338", "339", "340",
"341", "342", "343", "344", "345", "346", "347", "348", "349",
"350", "351", "352", "353", "354", "355", "356", "357", "358",
"359", "360", "361", "362", "363", "364", "365", "366", "367",
"368", "369", "370", "371", "372", "373", "374", "375", "376",
"377", "378", "379", "380", "381", "382", "383", "384", "385",
"386", "387", "388", "389", "390", "391", "392", "393", "394",
"395", "396", "397", "398", "399", "400", "401", "402", "403",
"404", "405", "406", "407", "408", "409", "410", "411", "412",
"413", "414", "415", "416", "417", "418", "419", "420", "421",
"422", "423", "424", "425", "426", "427", "428", "429", "430",
"431", "432", "433", "434", "435", "436", "437", "438", "439",
"440", "441", "442", "443", "444", "445", "446", "447", "448",
"449", "450", "451", "452", "453", "454", "455", "456", "457",
"458", "459", "460", "461", "462", "463", "464", "465", "466",
"467", "468", "469", "470", "471", "472", "473", "474", "475",
"476", "477", "478", "479", "480", "481", "482", "483", "484",
"485", "486", "487", "488", "489", "490", "491", "492", "493",
"494", "495", "496", "497", "498", "499", "500", "501", "502",
"503", "504", "505", "506", "507", "508", "509", "510", "511",
"512", "513", "514", "515", "516", "517", "518", "519", "520",
"521", "522", "523", "524", "525", "526", "527", "528", "529",
"530", "531", "532", "533", "534", "535", "536", "537", "538",
"539", "540", "541", "542", "543", "544", "545", "546", "547",
"548", "549", "550", "551", "552", "553", "554", "555", "556",
"557", "558", "559", "560", "561", "562", "563", "564", "565",
"566", "567", "568", "569", "570", "571", "572", "573", "574",
"575", "576", "577", "578", "579", "580", "581", "582", "583",
"584", "585", "586", "587", "588", "589", "590", "591", "592",
"593", "594", "595", "596", "597", "598", "599", "600", "601",
"602", "603", "604", "605", "606", "607", "608", "609", "610",
"611", "612", "613", "614", "615", "616", "617", "618", "619",
"620", "621", "622", "623", "624", "625", "626", "627", "628",
"629", "630", "631", "632", "633", "634", "635", "636", "637",
"638", "639", "640", "641", "642", "643", "644", "645", "646",
"647", "648", "649", "650", "651", "652", "653", "654", "655",
"656", "657", "658", "659", "660", "661", "662", "663", "664",
"665", "666", "667", "668", "669", "670", "671", "672", "673",
"674", "675", "676", "677", "678", "679", "680", "681", "682",
"683", "684", "685", "686", "687", "688", "689", "690", "691",
"692", "693", "694", "695", "696", "697", "698", "699", "700",
"701", "702", "703", "704", "705", "706", "707", "708", "709",
"710", "711", "712", "713", "714", "715", "716", "717", "718",
"719", "720", "721", "722", "723", "724", "725", "726", "727",
"728", "729", "730", "731", "732", "733", "734", "735", "736",
"737", "738", "739", "740", "741", "742", "743", "744", "745",
"746", "747", "748", "749", "750", "751", "752", "753", "754",
"755", "756", "757", "758", "759", "760", "761", "762", "763",
"764", "765", "766", "767", "768", "769", "770", "771", "772",
"773", "774", "775", "776", "777", "778", "779", "780", "781",
"782", "783", "784", "785", "786", "787", "788", "789", "790",
"791", "792", "793", "794", "795", "796", "797", "798", "799",
"800", "801", "802", "803", "804", "805", "806", "807", "808",
"809", "810", "811", "812", "813", "814", "815", "816", "817",
"818", "819", "820", "821", "822", "823", "824", "825", "826",
"827", "828", "829", "830", "831", "832", "833", "834", "835",
"836", "837", "838", "839", "840", "841", "842", "843", "844",
"845", "846", "847", "848", "849", "850", "851", "852", "853",
"854", "855", "856", "857", "858", "859", "860", "861", "862",
"863", "864", "865", "866", "867", "868", "869", "870", "871",
"872", "873", "874", "875", "876", "877", "878", "879", "880",
"881", "882", "883", "884", "885", "886", "887", "888", "889",
"890", "891", "892", "893", "894", "895", "896", "897", "898",
"899", "900", "901", "902", "903", "904", "905", "906", "907",
"908", "909", "910", "911", "912", "913", "914", "915", "916",
"917", "918", "919", "920", "921", "922", "923", "924", "925",
"926", "927", "928", "929", "930", "931", "932", "933", "934",
"935", "936", "937", "938", "939", "940", "941", "942", "943",
"944", "945", "946", "947", "948", "949", "950", "951", "952",
"953", "954", "955", "956", "957", "958", "959", "960", "961",
"962", "963", "964", "965", "966", "967", "968", "969", "970",
"971", "972", "973", "974", "975", "976", "977", "978", "979",
"980", "981", "982", "983", "984", "985", "986", "987", "988",
"989", "990", "991", "992", "993", "994", "995", "996", "997",
"998", "999", "1000"), class = "factor"), value = c(0.427070064,
0.905037358, 0.085540211, 1.43338843, 0.50574543, 0.3225239615,
0.889842105, 0.475018001, 0.150794906, 1.324221046, 0.3947968405,
0.370059413, 1.763186222, 0.794329673, 0.366688618666667, 0.288093907,
0.599490175666667, 1.31257167275, 1.147111171, 0.587266637333333
), truevalue = c(0.944062036, 0.585852702, 0.583984402, 0.944062036,
0.585852702, 0.583984402, 0.944062036, 0.585852702, 0.583984402,
0.944062036, 0.585852702, 0.583984402, 0.944062036, 0.585852702,
0.583984402, 0.944062036, 0.585852702, 0.583984402, 0.944062036,
0.585852702), error = c(-0.516991972, 0.319184656, -0.498444191,
0.489326394, -0.0801072719999999, -0.2614604405, -0.054219931,
-0.110834701, -0.433189496, 0.38015901, -0.1910558615, -0.213924989,
0.819124186, 0.208476971, -0.217295783333333, -0.655968129, 0.0136374736666667,
0.72858727075, 0.203049135, 0.00141393533333334)), .Names = c("stream",
"n", "rates", "means", "column", "value", "truevalue", "error"
), row.names = c(NA, 20L), class = "data.frame")
I'm running a hurdle type analysis on species distribution data which involves two fitting steps. The first step is to model (m1) presence/absence data using all data with family=quasibinomial. The second step (m2) is to use positive presence only data with family=Gamma. This works wonderfully until I try to predict using the second model (m2) on the full dataset I receive an error due to new factor levels. I understand why I am receiving this error; there are factor levels that appear in the full dataset that are not present in the reduce (presence only) dataset. My question is how do I work around this error so that I can get predictions using the second model on the full set?
I am using mgcv.
Edit: Updated with additional code and data.
# Step1 - GAM using full dataset for presence/absense
grays<-structure(list(Grid_ID = structure(c(39L, 51L, 52L, 67L), .Label = c("1",
"1,000", "1,001", "1,008", "1,009", "1,010", "1,011", "1,012",
"1,013", "1,014", "1,015", "1,016", "1,022", "1,023", "1,024",
"1,025", "1,026", "1,027", "1,028", "1,029", "1,034", "1,035",
"1,036", "1,037", "1,039", "1,040", "1,045", "1,046", "1,047",
"1,048", "1,053", "1,054", "1,055", "10", "100", "101", "103",
"104", "105", "106", "107", "108", "109", "11", "110", "118",
"119", "12", "122", "125", "126", "127", "128", "129", "13",
"130", "131", "132", "133", "14", "141", "142", "15", "150",
"151", "152", "153", "154", "155", "156", "157", "158", "159",
"160", "161", "162", "163", "167", "168", "169", "173", "174",
"175", "176", "177", "178", "179", "180", "181", "182", "183",
"184", "185", "188", "189", "190", "196", "197", "198", "199",
"2", "20", "200", "201", "202", "203", "204", "205", "206", "207",
"209", "210", "211", "219", "22", "220", "221", "222", "223",
"224", "225", "226", "227", "228", "229", "23", "230", "231",
"233", "234", "235", "236", "237", "24", "246", "247", "248",
"249", "25", "250", "252", "253", "254", "255", "256", "257",
"258", "259", "26", "260", "261", "267", "268", "269", "27",
"270", "271", "272", "273", "274", "275", "276", "277", "278",
"279", "28", "280", "281", "286", "287", "288", "289", "29",
"290", "291", "292", "293", "294", "295", "296", "297", "298",
"299", "3", "300", "301", "302", "303", "305", "306", "307",
"308", "309", "310", "311", "312", "313", "314", "315", "316",
"317", "318", "319", "320", "321", "326", "327", "328", "329",
"330", "331", "332", "333", "334", "335", "336", "337", "339",
"340", "341", "343", "344", "345", "346", "347", "348", "349",
"350", "351", "352", "355", "356", "357", "36", "360", "361",
"362", "363", "364", "365", "366", "367", "368", "369", "37",
"372", "373", "374", "38", "380", "381", "382", "383", "384",
"385", "386", "39", "391", "392", "397", "398", "399", "4", "40",
"400", "401", "402", "408", "409", "41", "410", "412", "413",
"414", "415", "416", "417", "42", "423", "424", "425", "426",
"43", "430", "431", "432", "433", "434", "44", "441", "442",
"443", "444", "447", "448", "449", "45", "450", "451", "458",
"459", "46", "460", "461", "462", "463", "464", "465", "466",
"470", "471", "472", "473", "474", "475", "476", "484", "485",
"486", "487", "488", "489", "490", "491", "492", "496", "497",
"498", "499", "5", "500", "501", "513", "514", "515", "516",
"517", "518", "523", "524", "525", "526", "527", "528", "529",
"54", "541", "542", "543", "544", "545", "55", "550", "551",
"552", "553", "554", "56", "569", "57", "570", "571", "572",
"573", "574", "578", "579", "580", "581", "582", "599", "60",
"600", "601", "602", "603", "604", "605", "606", "607", "608",
"609", "61", "610", "62", "626", "627", "628", "629", "63", "632",
"633", "634", "635", "636", "637", "638", "639", "64", "653",
"654", "655", "656", "657", "658", "659", "660", "663", "664",
"665", "666", "667", "668", "669", "670", "671", "672", "673",
"687", "688", "689", "690", "691", "692", "693", "696", "697",
"698", "699", "7", "700", "701", "702", "703", "704", "705",
"716", "717", "718", "720", "721", "722", "723", "724", "725",
"726", "727", "728", "739", "74", "740", "741", "746", "747",
"748", "749", "75", "750", "751", "752", "753", "754", "764",
"765", "768", "769", "77", "770", "771", "772", "773", "78",
"782", "783", "784", "788", "789", "79", "790", "798", "799",
"8", "80", "800", "801", "804", "805", "81", "812", "813", "814",
"815", "816", "819", "82", "820", "821", "827", "828", "829",
"83", "830", "831", "833", "834", "835", "836", "84", "842",
"843", "844", "845", "846", "849", "85", "850", "851", "852",
"853", "854", "860", "861", "862", "863", "864", "869", "870",
"871", "872", "873", "874", "88", "881", "882", "883", "884",
"885", "886", "89", "890", "891", "892", "893", "894", "9", "902",
"903", "904", "905", "906", "908", "909", "910", "911", "912",
"922", "923", "924", "925", "926", "927", "928", "929", "930",
"940", "941", "942", "943", "944", "945", "946", "947", "948",
"957", "958", "959", "96", "960", "961", "962", "963", "964",
"965", "966", "97", "976", "977", "978", "979", "980", "981",
"982", "983", "984", "992", "993", "994", "995", "996", "997",
"998", "999"), class = "factor"), Grid_Lat = c(56.85582097, 56.90062505,
56.90024495, 56.94461032), Grid_Long = c(153.4783612, 153.4777153,
153.3954873, 153.3124098), Er_Pres = c(0L, 0L, 0L, 0L), Er_Count = c(0L,
0L, 0L, 0L), Er_Count_Density = c(0, 0, 0, 0), Month = structure(c(8L,
8L, 8L, 8L), .Label = c("1", "2", "3", "4", "5", "6", "7", "8",
"9", "10", "11"), class = "factor"), Year = structure(c(1L, 1L,
1L, 1L), .Label = c("1997", "1998", "1999", "2000", "2001", "2002",
"2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010",
"2011", "2012", "2013"), class = "factor"), chl = c(0.53747,
0.53747, 0.53747, 0.581741), SST = c(13.4171, 13.4171, 13.4171,
13.4025002), Bathymetry = c(76.11354065, 92.14147949, 90.60312653,
71.55316162), Grid_Area = c(25, 25, 25, 25), DFS = c(6.807817092,
4.233185446, 9.199096676, 5.153224038), Slope = c(0.13670446,
0.38316911, 0.08646853, 0.20038579), DOY = c(244L, 244L, 244L,
244L)), .Names = c("Grid_ID", "Grid_Lat", "Grid_Long", "Er_Pres",
"Er_Count", "Er_Count_Density", "Month", "Year", "chl", "SST",
"Bathymetry", "Grid_Area", "DFS", "Slope", "DOY"), row.names = c(NA,
4L), class = "data.frame")
m1<-gam(Er_Pres~ s(Grid_Lat,Grid_Long,k=10,bs='tp')+Month+Year+s(SST,k=5,bs='tp'),family=quasibinomial(link='logit'),data=grays,gamma=1.4,offset(Grid_Area))
#step 2 - reduce dataset and run second GAM for positive abundance only.
grays2<-subset(grays,Er_Pres>0)
m2<-gam(Er_Count~ Year +s(Grid_Lat,Grid_Long,k=10,bs='tp') + s(SST,k=5,bs='tp') + s(sqrt(DFS),k=5,bs='tp') + Month +log10(chl),family=Gamma(link='log'),data=grays2,Gamma=1.4,offset(Grid_Area))
Running the second model gives me the follow error:
Error in predict.gam(m2, newdata = full, type = "response") :
1997, 1998, 2006, 2007 not in original fit
This is an old post, so I suspect you have found a solution by now, but if not consider this:
If you only want to account for data within the same year being more similar than data across year, but you are not necessarily interested in the effect of particular years (say the difference between 2007 and 1998) then you could specify year as a random effect.
I believe there are several ways to do this, but in mgcv, you can specify:
s(Year, bs="re")
I'm trying to calculate sums and means on a very large dataset (~22000 records) for several parameters (e.g. Er_Count, Mn_Count) by month, year , Survey ID and Grid ID. I tried this code initially to get overall sums:
dlply(Effort_All,c("Er_Count","Mn_Count","Bp_Count"),sum)
And received the following error:
Error: only defined on a data frame with all numeric variables
Since I cannot even get overall sums, I am unable to get statistics by the specific variables either. Do I need to split the data in some manner?
I have included a sample dataset of 25 records below.
structure(list(Grid_ID = structure(c(527L, 92L, 331L, 395L, 934L,
93L), .Label = c("1", "1,000", "1,001", "1,002", "1,003", "1,004",
"1,005", "1,006", "1,007", "1,008", "1,009", "1,010", "1,011",
"1,012", "1,013", "1,014", "1,015", "1,016", "1,017", "1,018",
"1,019", "1,020", "1,021", "1,022", "1,023", "1,024", "1,025",
"1,026", "1,027", "1,028", "1,029", "1,030", "1,031", "1,032",
"1,033", "1,034", "1,035", "1,036", "1,037", "1,038", "1,039",
"1,040", "1,041", "1,042", "1,043", "1,044", "1,045", "1,046",
"1,047", "1,048", "1,049", "1,050", "1,051", "1,052", "1,053",
"1,054", "1,055", "1,056", "1,057", "1,058", "1,059", "1,060",
"1,061", "10", "100", "101", "102", "103", "104", "105", "106",
"107", "108", "109", "11", "110", "111", "112", "113", "114",
"115", "116", "117", "118", "119", "12", "120", "121", "122",
"123", "124", "125", "126", "127", "128", "129", "13", "130",
"131", "132", "133", "134", "135", "136", "137", "138", "139",
"14", "140", "141", "142", "143", "144", "145", "146", "147",
"148", "149", "15", "150", "151", "152", "153", "154", "155",
"156", "157", "158", "159", "16", "160", "161", "162", "163",
"164", "165", "166", "167", "168", "169", "17", "170", "171",
"172", "173", "174", "175", "176", "177", "178", "179", "18",
"180", "181", "182", "183", "184", "185", "186", "187", "188",
"189", "19", "190", "191", "192", "193", "194", "195", "196",
"197", "198", "199", "2", "20", "200", "201", "202", "203", "204",
"205", "206", "207", "208", "209", "21", "210", "211", "212",
"213", "214", "215", "216", "217", "218", "219", "22", "220",
"221", "222", "223", "224", "225", "226", "227", "228", "229",
"23", "230", "231", "232", "233", "234", "235", "236", "237",
"238", "239", "24", "240", "241", "242", "243", "244", "245",
"246", "247", "248", "249", "25", "250", "251", "252", "253",
"254", "255", "256", "257", "258", "259", "26", "260", "261",
"262", "263", "264", "265", "266", "267", "268", "269", "27",
"270", "271", "272", "273", "274", "275", "276", "277", "278",
"279", "28", "280", "281", "282", "283", "284", "285", "286",
"287", "288", "289", "29", "290", "291", "292", "293", "294",
"295", "296", "297", "298", "299", "3", "30", "300", "301", "302",
"303", "304", "305", "306", "307", "308", "309", "31", "310",
"311", "312", "313", "314", "315", "316", "317", "318", "319",
"32", "320", "321", "322", "323", "324", "325", "326", "327",
"328", "329", "33", "330", "331", "332", "333", "334", "335",
"336", "337", "338", "339", "34", "340", "341", "342", "343",
"344", "345", "346", "347", "348", "349", "35", "350", "351",
"352", "353", "354", "355", "356", "357", "358", "359", "36",
"360", "361", "362", "363", "364", "365", "366", "367", "368",
"369", "37", "370", "371", "372", "373", "374", "375", "376",
"377", "378", "379", "38", "380", "381", "382", "383", "384",
"385", "386", "387", "388", "389", "39", "390", "391", "392",
"393", "394", "395", "396", "397", "398", "399", "4", "40", "400",
"401", "402", "403", "404", "405", "406", "407", "408", "409",
"41", "410", "411", "412", "413", "414", "415", "416", "417",
"418", "419", "42", "420", "421", "422", "423", "424", "425",
"426", "427", "428", "429", "43", "430", "431", "432", "433",
"434", "435", "436", "437", "438", "439", "44", "440", "441",
"442", "443", "444", "445", "446", "447", "448", "449", "45",
"450", "451", "452", "453", "454", "455", "456", "457", "458",
"459", "46", "460", "461", "462", "463", "464", "465", "466",
"467", "468", "469", "47", "470", "471", "472", "473", "474",
"475", "476", "477", "478", "479", "48", "480", "481", "482",
"483", "484", "485", "486", "487", "488", "489", "49", "490",
"491", "492", "493", "494", "495", "496", "497", "498", "499",
"5", "50", "500", "501", "502", "503", "504", "505", "506", "507",
"508", "509", "51", "510", "511", "512", "513", "514", "515",
"516", "517", "518", "519", "52", "520", "521", "522", "523",
"524", "525", "526", "527", "528", "529", "53", "530", "531",
"532", "533", "534", "535", "536", "537", "538", "539", "54",
"540", "541", "542", "543", "544", "545", "546", "547", "548",
"549", "55", "550", "551", "552", "553", "554", "555", "556",
"557", "558", "559", "56", "560", "561", "562", "563", "564",
"565", "566", "567", "568", "569", "57", "570", "571", "572",
"573", "574", "575", "576", "577", "578", "579", "58", "580",
"581", "582", "583", "584", "585", "586", "587", "588", "589",
"59", "590", "591", "592", "593", "594", "595", "596", "597",
"598", "599", "6", "60", "600", "601", "602", "603", "604", "605",
"606", "607", "608", "609", "61", "610", "611", "612", "613",
"614", "615", "616", "617", "618", "619", "62", "620", "621",
"622", "623", "624", "625", "626", "627", "628", "629", "63",
"630", "631", "632", "633", "634", "635", "636", "637", "638",
"639", "64", "640", "641", "642", "643", "644", "645", "646",
"647", "648", "649", "65", "650", "651", "652", "653", "654",
"655", "656", "657", "658", "659", "66", "660", "661", "662",
"663", "664", "665", "666", "667", "668", "669", "67", "670",
"671", "672", "673", "674", "675", "676", "677", "678", "679",
"68", "680", "681", "682", "683", "684", "685", "686", "687",
"688", "689", "69", "690", "691", "692", "693", "694", "695",
"696", "697", "698", "699", "7", "70", "700", "701", "702", "703",
"704", "705", "706", "707", "708", "709", "71", "710", "711",
"712", "713", "714", "715", "716", "717", "718", "719", "72",
"720", "721", "722", "723", "724", "725", "726", "727", "728",
"729", "73", "730", "731", "732", "733", "734", "735", "736",
"737", "738", "739", "74", "740", "741", "742", "743", "744",
"745", "746", "747", "748", "749", "75", "750", "751", "752",
"753", "754", "755", "756", "757", "758", "759", "76", "760",
"761", "762", "763", "764", "765", "766", "767", "768", "769",
"77", "770", "771", "772", "773", "774", "775", "776", "777",
"778", "779", "78", "780", "781", "782", "783", "784", "785",
"786", "787", "788", "789", "79", "790", "791", "792", "793",
"794", "795", "796", "797", "798", "799", "8", "80", "800", "801",
"802", "803", "804", "805", "806", "807", "808", "809", "81",
"810", "811", "812", "813", "814", "815", "816", "817", "818",
"819", "82", "820", "821", "822", "823", "824", "825", "826",
"827", "828", "829", "83", "830", "831", "832", "833", "834",
"835", "836", "837", "838", "839", "84", "840", "841", "842",
"843", "844", "845", "846", "847", "848", "849", "85", "850",
"851", "852", "853", "854", "855", "856", "857", "858", "859",
"86", "860", "861", "862", "863", "864", "865", "866", "867",
"868", "869", "87", "870", "871", "872", "873", "874", "875",
"876", "877", "878", "879", "88", "880", "881", "882", "883",
"884", "885", "886", "887", "888", "889", "89", "890", "891",
"892", "893", "894", "895", "896", "897", "898", "899", "9",
"90", "900", "901", "902", "903", "904", "905", "906", "907",
"908", "909", "91", "910", "911", "912", "913", "914", "915",
"916", "917", "918", "919", "92", "920", "921", "922", "923",
"924", "925", "926", "927", "928", "929", "93", "930", "931",
"932", "933", "934", "935", "936", "937", "938", "939", "94",
"940", "941", "942", "943", "944", "945", "946", "947", "948",
"949", "95", "950", "951", "952", "953", "954", "955", "956",
"957", "958", "959", "96", "960", "961", "962", "963", "964",
"965", "966", "967", "968", "969", "97", "970", "971", "972",
"973", "974", "975", "976", "977", "978", "979", "98", "980",
"981", "982", "983", "984", "985", "986", "987", "988", "989",
"99", "990", "991", "992", "993", "994", "995", "996", "997",
"998", "999"), class = "factor"), ER_Groups = c(2, 2, 2, 3, 5,
6), Er_Count = c(60, 75, 14, 12, 8, 26), Mn_Count = c(30, 9, 6, 33,
7, 12), Bp_Groups = c(1, 2, 1, 1, 0, 1), Bp_Count = c(3, 3, 2,
5, 0, 6), Mn_Groups = c(1, 1, 3, 1, 0, 0), Month = c(10L, 6L,
12L, 4L, 2L, 4L), Year = c(2000L, 2001L, 2009L, 2004L, 2002L,
2001L), SurveyID = structure(c(16L, 24L, 93L, 56L, 34L, 22L), .Label = c("199708HS",
"199808HS", "199908HS", "199909SSLQ", "199910SSL", "199911SSL",
"200001SSLQ", "200002SSL", "200003SSLQ", "200004SSLQ", "200005SSL",
"200006SSL", "200007SSL", "200008HS", "200008SSL", "200009SSL",
"200010SSL", "200011SSL", "200101SSL", "200102SSL", "200103SSL",
"200104SSL", "200105SSL", "200106SSL", "200107SSL", "200108HS",
"200108SSL", "200109SSL", "200110SSL", "200111SSL", "200112SSL",
"200201SSL", "200202SSL", "200203SSL", "200204SSL", "200205SSL",
"200206SSL", "200207SSL", "200208HS", "200208SSL", "200210SSL",
"200211SSL", "200212SSL", "200301SSL", "200302SSL", "200303SSL",
"200304SSL", "200305SSL", "200306SSL", "200307SSL", "200309SSL",
"200310SSL", "200311SSL", "200312SSL", "200403SSL", "200404SSL",
"200405SSL", "200406SSL", "200407SSL", "200408HS", "200408SSL",
"200409SSL", "200505SSL", "200506SSL", "200507SSL", "200510SSL",
"200512SSL", "200603SSL", "200609SSL", "200612SSL", "200709GAP07",
"200710GAP07", "200712GAP07", "200802GAP07", "200803GAP07", "200804GAP07",
"200805GAP07", "200806GAP07", "200807GAP07", "200808GAP07", "200809GAP08",
"200810GAP08", "200812GAP08", "200901GAP08", "200903GAP08", "200904GAP08",
"200905GAP08", "200906GAP08", "200907GAP08", "200908GAP08", "200909GAP08",
"200910GAP09", "200912GAP09", "201001GAP09", "201002GAP09", "201003GAP09",
"201004GAP09", "201005GAP09", "201006GAP09", "201007GAP09", "201008GAP09",
"201009GAP09", "201010GAP09", "201011GAP09", "201101GAP09", "201102GAP09",
"201103GAP09", "201104GAP09", "201106GAP09", "201108GAP09", "201109GAP09",
"201111GAP09", "201201GAP09", "201203GAP09", "201205GAP09", "201207GAP09",
"201208GAP09", "201211GAP09", "201301GAP09", "201303GAP09", "201305GAP09",
"201307GAP09", "201309GAP09", "201311GAP09"), class = "factor"),
Er_Group_Density = c(4, 9, 12, 4, 1, 0), Mn_Group_Density = c(3,
1, 1, 1, 0, 2), Bp_Group_Density = c(1, 2, 1, 0, 1, 0), Er_Count_Density = c(50,
14, 12, 9, 6, 4), Mn_Count_Density = c(9, 5, 2, 3, 2, 0), Bp_Count_Density = c(2,
3, 0, 4, 1, 0)), .Names = c("Grid_ID", "ER_Groups", "Er_Count",
"Mn_Count", "Bp_Groups", "Bp_Count", "Mn_Groups", "Month", "Year",
"SurveyID", "Er_Group_Density", "Mn_Group_Density", "Bp_Group_Density",
"Er_Count_Density", "Mn_Count_Density", "Bp_Count_Density"), row.names = c(2770L,
4421L, 17348L, 11263L, 6736L, 3974L), class = "data.frame")
There are a number of ways to get statistics by group. I'll assume you have a bias for plyr, since your example uses it.
Remember that dlply() splits the data into smaller dataframes by the grouping variables, then it applies the requested function to each of the smaller dataframes. Therefore the function you pass should operate on a whole dataframe. sum() does not do this. You can write your own function, though.
Based on your description, what you want is something like this
myfun <- function(x) colSums(x[, c("Er_Count", "Mn_Count", "Bp_Count")])
dlply(Effort_All, c("Month", "Year", "Grid_ID", "SurveyID"), myfun)
Remember that the second argument to dlply() is the set of variables used for grouping. Not sure why you want the output as a list. Would it be easier to read if you used ddply (with the same arguments)?
Other approaches include using sqldf() or something like lapply().
=============== EDIT: Other approaches =============
sqldf is always very easy to read and understand:
output <- sqldf('select Month,Year,Grid_ID,SurveyID,
sum(Er_Count) as ercount,
sum(Mn_Count) as mncount,
sum(Bp_Count) as bpcount
from Effort_All
group by Month, Year, Grid_ID, SurveyID')
lapply works pretty much the same way as dlply. Just different arguments.
Also, you could use colwise from plyr
dlply(Effort_All, .(Month, Year, Grid_ID, SurveyID), colwise(sum, .(Er_Count, Mn_Count, Bp_Count)))
Or summarise_each from dplyr
library(dplyr)
Effort_All%>%
group_by(Month, Year, Grid_ID, SurveyID) %>%
summarise_each(funs(sum), Er_Count, Mn_Count, Bp_Count)
#Source: local data frame [6 x 7]
#Groups: Month, Year, Grid_ID
# Month Year Grid_ID SurveyID Er_Count Mn_Count Bp_Count
# 1 2 2002 884 200203SSL 8 7 0
# 2 4 2001 126 200104SSL 26 12 6
# 3 4 2004 399 200404SSL 12 33 5
# 4 6 2001 125 200106SSL 75 9 3
# 5 10 2000 517 200009SSL 60 30 3
# 6 12 2009 340 200912GAP09 14 6 2