inst/doc/Rdistance_BeginnerLineTransectCovar.R

## ------------------------------------------------------------------------
require(Rdistance)

## ------------------------------------------------------------------------
data(sparrowDetectionData)
head(sparrowDetectionData)
data(sparrowSiteData)
head(sparrowSiteData)



## ------------------------------------------------------------------------
sparrowDetectionData$dist<- perpDists(sightDist="sightdist",
                    sightAngle="sightangle", 
                    data = sparrowDetectionData)
sparrowDetectionData <- sparrowDetectionData[,
                    -which(names(sparrowDetectionData) 
                    %in% c("sightdist", "sightangle"))]

## ---- fig.width=6, fig.height=4------------------------------------------
hist(sparrowDetectionData$dist, col="grey", main="", 
     xlab="distance (m)")
rug(sparrowDetectionData$dist,quiet = TRUE)
summary(sparrowDetectionData$dist)

## ---- fig.width=6, fig.height=4------------------------------------------

dfuncSparrow<- dfuncEstim(formula = dist~1, 
                          detectionData = sparrowDetectionData,
                          siteData = sparrowSiteData, 
                          likelihood = "halfnorm", w.hi = 150)

plot(dfuncSparrow)
dfuncSparrow


## ---- fig.width=6, fig.height=4,results='hide'---------------------------
fit <- abundEstim(dfuncSparrow, 
                  detectionData=sparrowDetectionData,
                  siteData=sparrowSiteData,
                  area=10000, 
                  ci=NULL)
# To save vignette build time, we insert values from 
# a separate run with R=500
fit$ci <- c( lowCI=0.6473984, hiCI=1.0167007)

## ------------------------------------------------------------------------
fit

## ------------------------------------------------------------------------
fit$n.hat
fit$ci

## ---- fig.width = 6, fig.height = 4, warning = FALSE---------------------
hist(sparrowDetectionData$dist[sparrowSiteData$shrubclass ==
     "High"], col="grey", main="High Sage",  
     xlab="distance (m)", breaks = 15, xlim = c(0,150))
rug(sparrowDetectionData$dist[sparrowSiteData$shrubclass == 
    "High"],quiet = TRUE)
hist(sparrowDetectionData$dist[sparrowSiteData$shrubclass == 
    "Low"], col="grey", main="Low Sage",  xlab="distance (m)", 
     breaks = 14, xlim = c(0,150))
rug(sparrowDetectionData$dist[sparrowSiteData$shrubclass == 
    "Low"],quiet = TRUE)


## ---- eval=FALSE---------------------------------------------------------
#  
#  dfuncShrubClass<- dfuncEstim(formula = dist~shrubclass,
#                            detectionData = sparrowDetectionData,
#                            siteData = sparrowSiteData,
#                            likelihood = "halfnorm", w.hi = 150)

## ---- include=FALSE------------------------------------------------------
dfuncShrubClass <-
structure(list(parameters = c(`(Intercept)` = 4.01969799841488, 
shrubclassHigh = -0.181306851357376), varcovar = structure(c(0.0050270030909592, 
-0.00502975457487661, -0.00502975457487661, 0.00748138480810069
), .Dim = c(2L, 2L)), loglik = 1628.37871595743, convergence = 0L, 
    like.form = "halfnorm", w.lo = 0, w.hi = 150, dist = c(`1` = 16.8, 
    `2` = 12.2, `3` = 24.1, `4` = 3.5, `5` = 69.7, `6` = 10, 
    `7` = 14.9, `8` = 1.7, `9` = 4, `10` = 10, `11` = 9.8, `12` = 67.6, 
    `13` = 73.9, `14` = 74.7, `15` = 21.2, `16` = 12.9, `17` = 23.9, 
    `18` = 22, `19` = 25, `20` = 20.5, `21` = 65, `22` = 21.1, 
    `23` = 24.9, `24` = 31.9, `25` = 3.5, `26` = 70, `27` = 77.1, 
    `28` = 8.6, `29` = 12, `30` = 29.4, `31` = 46.7, `32` = 29.1, 
    `33` = 42.6, `34` = 29, `35` = 29.7, `36` = 22.5, `37` = 42.6, 
    `38` = 35.7, `39` = 27.7, `40` = 45.1, `41` = 5.9, `42` = 103, 
    `43` = 31.5, `44` = 13.5, `45` = 93, `46` = 7.3, `47` = 34.8, 
    `48` = 60.5, `49` = 16.1, `50` = 27.9, `51` = 44.4, `52` = 78, 
    `53` = 5.5, `54` = 75.8, `55` = 48, `56` = 19, `57` = 83.7, 
    `58` = 25.9, `59` = 15.5, `60` = 52.5, `61` = 26.4, `62` = 24, 
    `63` = 66.4, `64` = 8.9, `65` = 32.5, `66` = 26.4, `67` = 64.3, 
    `68` = 115.5, `69` = 5, `70` = 21.2, `71` = 61.9, `72` = 32.5, 
    `73` = 77.3, `74` = 34.5, `75` = 33, `76` = 9.6, `77` = 53.4, 
    `78` = 12.2, `79` = 27.6, `80` = 21.9, `81` = 34.7, `82` = 49.3, 
    `83` = 21.2, `84` = 14.7, `85` = 14, `86` = 16.1, `87` = 36.8, 
    `88` = 0, `89` = 38.4, `90` = 12.2, `91` = 126.6, `92` = 35, 
    `93` = 42.1, `94` = 36.3, `95` = 50, `96` = 46, `97` = 22.9, 
    `98` = 22.9, `99` = 12.7, `100` = 0, `101` = 7.5, `102` = 56.6, 
    `103` = 77.3, `104` = 16.5, `105` = 54.9, `106` = 0, `107` = 28, 
    `108` = 1.4, `109` = 26.8, `110` = 23.5, `111` = 26.8, `112` = 39, 
    `113` = 44, `114` = 57.3, `115` = 20.6, `116` = 32.5, `117` = 10.3, 
    `118` = 115.9, `119` = 50.8, `120` = 92, `121` = 0, `122` = 81, 
    `123` = 52.5, `124` = 19.5, `125` = 62.1, `126` = 100, `127` = 103.9, 
    `128` = 117.1, `129` = 6.4, `130` = 65, `131` = 33.5, `132` = 41, 
    `133` = 31, `135` = 73.9, `136` = 45, `137` = 16.1, `138` = 0, 
    `139` = 17, `140` = 11.6, `141` = 13.4, `142` = 2.6, `143` = 17.5, 
    `144` = 11, `145` = 1.2, `146` = 0, `147` = 0, `148` = 14, 
    `149` = 44.4, `150` = 17.6, `151` = 89.6, `152` = 81.1, `153` = 48.2, 
    `154` = 99.5, `155` = 36.6, `156` = 121.8, `157` = 46.5, 
    `158` = 6.7, `159` = 15.8, `160` = 5.9, `161` = 0, `162` = 67.7, 
    `163` = 7.8, `164` = 47.5, `165` = 52.5, `166` = 26, `167` = 14.6, 
    `168` = 59, `169` = 44.8, `170` = 68.1, `171` = 38, `172` = 2, 
    `173` = 4.6, `174` = 6, `175` = 7.8, `176` = 44.2, `177` = 50.8, 
    `178` = 19.9, `179` = 4.5, `180` = 11.4, `181` = 141.9, `182` = 26.4, 
    `183` = 15.8, `184` = 93, `185` = 40.6, `186` = 101.5, `187` = 24.7, 
    `188` = 23.9, `189` = 14.3, `190` = 34.5, `191` = 91.8, `192` = 109.6, 
    `193` = 53.6, `194` = 32.8, `195` = 6.1, `196` = 1.7, `197` = 40, 
    `198` = 20.5, `199` = 9.1, `200` = 35, `201` = 34.5, `202` = 24.6, 
    `203` = 93.7, `204` = 31.5, `205` = 45.8, `206` = 44.5, `207` = 0, 
    `208` = 7.5, `209` = 63.6, `210` = 10.1, `211` = 0, `212` = 0, 
    `213` = 46, `214` = 72, `215` = 8.5, `216` = 15.5, `217` = 2.2, 
    `218` = 94, `219` = 54, `220` = 14, `221` = 19.1, `222` = 31.2, 
    `223` = 78, `224` = 23, `225` = 110, `226` = 7.1, `227` = 26, 
    `228` = 19, `229` = 0, `230` = 5.2, `231` = 11.8, `232` = 63.9, 
    `233` = 54.4, `234` = 72.1, `235` = 46, `236` = 60.1, `237` = 60.8, 
    `238` = 19.6, `239` = 24.6, `240` = 26, `241` = 38.4, `242` = 144.8, 
    `243` = 25.9, `244` = 17.8, `245` = 32.2, `246` = 31, `247` = 59.1, 
    `248` = 74.6, `249` = 2.1, `250` = 12.5, `251` = 74.3, `252` = 7.2, 
    `254` = 63, `255` = 90.7, `256` = 5.5, `257` = 19.9, `258` = 22.9, 
    `259` = 28, `260` = 87.6, `261` = 114.6, `262` = 61, `263` = 55.4, 
    `264` = 57.5, `265` = 86, `266` = 71.9, `267` = 68.8, `268` = 63, 
    `269` = 39.6, `270` = 8.9, `271` = 71, `272` = 23.8, `273` = 41.5, 
    `274` = 87.6, `275` = 39, `276` = 31, `277` = 76, `278` = 45.2, 
    `279` = 12.2, `280` = 26.2, `281` = 8.5, `282` = 73.9, `283` = 87, 
    `284` = 77.8, `285` = 2.4, `286` = 30.5, `287` = 27.9, `288` = 134, 
    `289` = 30.5, `290` = 17.5, `291` = 4.5, `292` = 55.1, `293` = 19.4, 
    `294` = 8.5, `295` = 21.2, `296` = 79.5, `297` = 21, `298` = 102, 
    `299` = 32.5, `300` = 51.6, `301` = 118, `302` = 115.2, `303` = 44.4, 
    `304` = 117.5, `305` = 39, `306` = 29, `307` = 61, `308` = 42.4, 
    `310` = 21.7, `311` = 12, `312` = 33.4, `313` = 6.9, `314` = 0, 
    `315` = 46, `316` = 0, `317` = 66.5, `318` = 22.5, `319` = 15, 
    `320` = 60.8, `321` = 105, `322` = 0, `323` = 20, `324` = 14.7, 
    `325` = 23.6, `326` = 30.4, `327` = 52, `328` = 60, `329` = 46.8, 
    `330` = 78, `331` = 0, `332` = 24.3, `333` = 14.5, `334` = 9.8, 
    `335` = 46, `336` = 10.9, `337` = 55.3, `338` = 40.7, `339` = 58, 
    `340` = 57.2, `341` = 60.1, `342` = 45, `343` = 0.9, `344` = 28, 
    `345` = 14, `346` = 41.8, `347` = 0, `348` = 5.6, `349` = 14.2, 
    `350` = 35.5, `351` = 14, `352` = 51, `353` = 78.7, `354` = 27.3, 
    `355` = 1, `356` = 3.1), covars = structure(c(1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
    1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), .Dim = c(353L, 
    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", 
    "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", "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", "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"), c("(Intercept)", "shrubclassHigh"
    ))), model.frame = structure(list(dist = c(16.8, 12.2, 24.1, 
    3.5, 69.7, 10, 14.9, 1.7, 4, 10, 9.8, 67.6, 73.9, 74.7, 21.2, 
    12.9, 23.9, 22, 25, 20.5, 65, 21.1, 24.9, 31.9, 3.5, 70, 
    77.1, 8.6, 12, 29.4, 46.7, 29.1, 42.6, 29, 29.7, 22.5, 42.6, 
    35.7, 27.7, 45.1, 5.9, 103, 31.5, 13.5, 93, 7.3, 34.8, 60.5, 
    16.1, 27.9, 44.4, 78, 5.5, 75.8, 48, 19, 83.7, 25.9, 15.5, 
    52.5, 26.4, 24, 66.4, 8.9, 32.5, 26.4, 64.3, 115.5, 5, 21.2, 
    61.9, 32.5, 77.3, 34.5, 33, 9.6, 53.4, 12.2, 27.6, 21.9, 
    34.7, 49.3, 21.2, 14.7, 14, 16.1, 36.8, 0, 38.4, 12.2, 126.6, 
    35, 42.1, 36.3, 50, 46, 22.9, 22.9, 12.7, 0, 7.5, 56.6, 77.3, 
    16.5, 54.9, 0, 28, 1.4, 26.8, 23.5, 26.8, 39, 44, 57.3, 20.6, 
    32.5, 10.3, 115.9, 50.8, 92, 0, 81, 52.5, 19.5, 62.1, 100, 
    103.9, 117.1, 6.4, 65, 33.5, 41, 31, 201, 73.9, 45, 16.1, 
    0, 17, 11.6, 13.4, 2.6, 17.5, 11, 1.2, 0, 0, 14, 44.4, 17.6, 
    89.6, 81.1, 48.2, 99.5, 36.6, 121.8, 46.5, 6.7, 15.8, 5.9, 
    0, 67.7, 7.8, 47.5, 52.5, 26, 14.6, 59, 44.8, 68.1, 38, 2, 
    4.6, 6, 7.8, 44.2, 50.8, 19.9, 4.5, 11.4, 141.9, 26.4, 15.8, 
    93, 40.6, 101.5, 24.7, 23.9, 14.3, 34.5, 91.8, 109.6, 53.6, 
    32.8, 6.1, 1.7, 40, 20.5, 9.1, 35, 34.5, 24.6, 93.7, 31.5, 
    45.8, 44.5, 0, 7.5, 63.6, 10.1, 0, 0, 46, 72, 8.5, 15.5, 
    2.2, 94, 54, 14, 19.1, 31.2, 78, 23, 110, 7.1, 26, 19, 0, 
    5.2, 11.8, 63.9, 54.4, 72.1, 46, 60.1, 60.8, 19.6, 24.6, 
    26, 38.4, 144.8, 25.9, 17.8, 32.2, 31, 59.1, 74.6, 2.1, 12.5, 
    74.3, 7.2, 196.6, 63, 90.7, 5.5, 19.9, 22.9, 28, 87.6, 114.6, 
    61, 55.4, 57.5, 86, 71.9, 68.8, 63, 39.6, 8.9, 71, 23.8, 
    41.5, 87.6, 39, 31, 76, 45.2, 12.2, 26.2, 8.5, 73.9, 87, 
    77.8, 2.4, 30.5, 27.9, 134, 30.5, 17.5, 4.5, 55.1, 19.4, 
    8.5, 21.2, 79.5, 21, 102, 32.5, 51.6, 118, 115.2, 44.4, 117.5, 
    39, 29, 61, 42.4, 207, 21.7, 12, 33.4, 6.9, 0, 46, 0, 66.5, 
    22.5, 15, 60.8, 105, 0, 20, 14.7, 23.6, 30.4, 52, 60, 46.8, 
    78, 0, 24.3, 14.5, 9.8, 46, 10.9, 55.3, 40.7, 58, 57.2, 60.1, 
    45, 0.9, 28, 14, 41.8, 0, 5.6, 14.2, 35.5, 14, 51, 78.7, 
    27.3, 1, 3.1), shrubclass = structure(c(2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Low", "High"), class = "factor")), terms = dist ~ 
        shrubclass, row.names = c(NA, 356L), class = "data.frame"), 
    expansions = 0, series = "cosine", call = quote(dfuncEstim(formula = dist ~ 
        shrubclass, detectionData = sparrowDetectionData, siteData = sparrowSiteData, 
        likelihood = "halfnorm", w.hi = 150)), call.x.scl = 0, 
    call.g.x.scl = 1, call.observer = "both", fit = list(par = c(`(Intercept)` = 4.01969799841488, 
    shrubclassHigh = -0.181306851357376), objective = -5686.93412448465, 
        convergence = 0L, iterations = 10L, counts = c(`function` = 14L, 
        gradient = 27L), message = "relative convergence (4)", 
        hessian = structure(c(607.723213611669, 408.573906078495, 
        408.573906078495, 408.350399242343), .Dim = c(2L, 2L))), 
    factor.names = "shrubclass", pointSurvey = FALSE, formula = dist ~ 
        shrubclass, x.scl = 0, g.x.scl = 1), class = "dfunc")

## ---- fig.width=6, fig.height=4------------------------------------------
plot(dfuncShrubClass, newdata = data.frame(shrubclass=
                levels(sparrowSiteData$shrubclass)), nbins = 15)

## ---- fig.width=6, fig.height=4, results='hide'--------------------------
covarFit <- abundEstim(dfuncShrubClass, 
                       detectionData=sparrowDetectionData,
                       siteData=sparrowSiteData,
                       area=10000, 
                       ci=NULL)
# To save vignette build time, insert values from 
# a separate run with R=500
covarFit$ci <- c("2.939477%" = 0.6552051, "97.88301%"= 1.0411813)

## ------------------------------------------------------------------------
covarFit

## ---- eval=FALSE---------------------------------------------------------
#  
#  auto <- autoDistSamp(formula        = dist~1,
#                       detectionData  = sparrowDetectionData,
#                       siteData       = sparrowSiteData,
#                       area           = 10000,
#                       likelihoods    = c("halfnorm","hazrate","uniform"),
#                       expansions     = 0:1,
#                       series         = "cosine",
#                       ci             = 0.95,
#                       R              = 500,
#                       plot           = FALSE,
#                       plot.bs        = FALSE,
#                       w.hi           = 150)
#  

## ---- include=FALSE------------------------------------------------------
auto<-list(n.hat=1.003543 ,
  ci=list(0.7823177, 1.366216))

## ---- eval=FALSE,highlight=FALSE-----------------------------------------
#  ## Likelihood	Series	Expans	Converged?	Scale?	AICc
#  ## halfnorm	cosine	0	Yes		Ok	3263.443
#  ## halfnorm	cosine	1	Yes		Ok	3261.5853
#  ## hazrate		cosine	0	Yes		Ok	3267.6249
#  ## hazrate		cosine	1	Yes		Ok	3263.3098
#  ## uniform		cosine	0	Yes		Ok	3260.7321
#  ## uniform		cosine	1	Yes		Ok	3262.4814
#  ## Computing bootstrap confidence interval on N...
#  ## |===================================================================| 100%
#  ## 101 of 500 iterations did not converge.
#  ##
#  ## ------------ Abundance Estimate Based on Top-Ranked Detection Function ------------
#  ## Call: dfuncEstim(formula = formula, detectionData = detectionData,
#  ##    siteData = siteData, likelihood = fit.table$like[1], pointSurvey =
#  ##    pointSurvey, w.lo = w.lo, w.hi = w.hi, expansions =
#  ##    fit.table$expansions[1], series = fit.table$series[1])
#  ## Coefficients:
#  ##            Estimate    SE            z         p(>|z|)
#  ## Threshold  27.7397495  19.537302641  1.419835  1.556557e-01
#  ## Knee        0.0340005   0.005304744  6.409451  1.460443e-10
#  ##
#  ## Convergence: Success
#  ## Function: UNIFORM
#  ## Strip: 0 to 150
#  ## Effective strip width (ESW): 51.34584
#  ## Probability of detection: 0.3423056
#  ## Scaling: g(0) = 1
#  ## Log likelihood: 1628.349
#  ## AICc: 3260.732
#  ##
#  ## Abundance estimate:  1.003543 ;  95% CI=( 0.7823177 to 1.366216 )
#  ## CI based on 399 of 500 successful bootstrap iterations

## ---- eval=FALSE---------------------------------------------------------
#  
#  autoCov <- autoDistSamp(formula     = dist~shrubclass,
#                       detectionData  = sparrowDetectionData,
#                       siteData       = sparrowSiteData,
#                       likelihoods    = c("halfnorm", "hazrate", "negexp"),
#                       expansions     = 0,
#                       area           = 10000,
#                       ci             = 0.95,
#                       R              = 500,
#                       plot           = FALSE,
#                       plot.bs        = FALSE,
#                       w.hi           = 150)
#  

## ----highlight=FALSE,eval=FALSE------------------------------------------
#  ## Likelihood Series  Expans  Converged?  Scale?  AICc
#  ## halfnorm   cosine  0       Yes         Ok      3260.7917
#  ## hazrate    cosine  0       Yes         Ok      3264.531
#  ## negexp     cosine  0       Yes         Ok      3260.8539
#  ## Computing bootstrap confidence interval on N...
#  ## |===================================================================| 100%
#  ##
#  ## ------------ Abundance Estimate Based on Top-Ranked Detection Function ------------
#  ## Call: dfuncEstim(formula = formula, detectionData = detectionData,
#  ##    siteData = siteData, likelihood = fit.table$like[1], pointSurvey =
#  ##    pointSurvey, w.lo = w.lo, w.hi = w.hi, expansions =
#  ##    fit.table$expansions[1], series = fit.table$series[1])
#  ## Coefficients:
#  ##                 Estimate    SE          z          p(>|z|)
#  ## (Intercept)      4.0196980  0.07090136  56.694228  0.00000000
#  ## shrubclassHigh  -0.1813069  0.08649500  -2.096154  0.03606852
#  ##
#  ## Convergence: Success
#  ## Function: HALFNORM
#  ## Strip: 0 to 150
#  ## Average effective strip width (ESW): 62.28349
#  ## Average probability of detection: 0.4152232
#  ## Scaling: g(0) = 1
#  ## Log likelihood: 1628.379
#  ## AICc: 3260.792
#  ##
#  ## Abundance estimate:  0.8350688 ;  95% CI=( 0.6484067 to 1.036907 )

## ---- include=FALSE------------------------------------------------------
autoCov<-list(n.hat=0.8350688 ,
  ci=list(0.6484067,1.036907))

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Rdistance documentation built on May 2, 2019, 3:49 a.m.