## ----output='hide'------------------------------------------------------------
require(Rdistance)
## ----setup--------------------------------------------------------------------
data("sparrowDetectionData")
data("sparrowSiteData")
## ----detectionDataDisplay-----------------------------------------------------
head(sparrowDetectionData)
## ----siteDataDisplay----------------------------------------------------------
head(sparrowSiteData)
## ----rawDistanceHistogram, fig.width=6, fig.height=4, fig.cap="Histogram of sparrow off-transect detection distances."----
hist(sparrowDetectionData$dist
, col="grey"
, main=""
, xlab = "Distance")
rug(sparrowDetectionData$dist,quiet = TRUE)
## ----rawDistanceSummary-------------------------------------------------------
summary(sparrowDetectionData$dist)
## ----dfuncEstimation----------------------------------------------------------
rightTruncDistance <- units::set_units(150, "m")
dfuncSparrow<- dfuncEstim(formula = dist ~ 1 + offset(groupsize)
, detectionData = sparrowDetectionData
, likelihood = "halfnorm"
, w.hi = rightTruncDistance)
dfuncSparrow
## ----fig-dfuncPlot, fig.width=6, fig.height=4, fig.cap="The half-normal distance function fitted to sparrow off-transect detection distances."----
# Figure 2
plot(dfuncSparrow, nbins =40, col="grey")
## ----studyAreaSize, include=FALSE---------------------------------------------
# NOT INCLUDED IN VIGNETTE
saSize <- units::set_units(4105, "km^2")
set.seed(8927342)
## ----abundEstim, include=FALSE------------------------------------------------
# To save vignette build time, we run it with ci=NULL and insert values from
# a separate run with R=500
fit <- abundEstim(dfuncSparrow
, detectionData = sparrowDetectionData
, siteData = sparrowSiteData
, area = saSize
, ci = NULL
)
fit$n.hat.ci <- c( `2.5%` = 268646.3
, `97.5%` = 412088 )
fit$density.ci <- units::set_units(c( 6.544368e-05, 0.0001003868 ), "1/m^2")
fit$effDistance.ci <- units::set_units(c(56.66586, 68.72937 ), "m")
fit$nItersConverged <- 500
fit$alpha <- 0.95
B.d <- c(
7.41186193094976e-05 , 8.69775946939212e-05 , 8.20995278909548e-05 , 8.24824091338752e-05 , 9.8337763002546e-05 , 9.26031826522007e-05 , 6.04364659611598e-05 , 8.71601415352838e-05 , 8.40262756115299e-05 , 7.7231071824023e-05 , 9.11710914168955e-05 , 9.2903836882483e-05 , 6.80190749696935e-05 , 7.24194771707422e-05 , 8.03752068184811e-05 , 7.10616504662341e-05 , 8.87211875789235e-05 , 7.77797903678722e-05 , 9.70539888565341e-05 , 6.91494234014299e-05 , 5.99323466448954e-05 , 9.1536426090801e-05 , 8.78047152332687e-05 , 8.240447497912e-05 , 7.72538426316594e-05 , 9.00281157467167e-05 , 6.79047145642239e-05 , 7.3269216589717e-05 , 9.50687068382639e-05 , 8.69559685519084e-05 , 7.86417398636142e-05 , 8.38757670358632e-05 , 9.32955821610548e-05 , 8.34866913495973e-05 , 8.45745109724636e-05 , 9.40384432934679e-05 , 7.74584770226755e-05 , 7.93460986097706e-05 , 8.49367942080276e-05 , 8.7762782032957e-05 , 0.000107336477382257 , 7.49378886521039e-05 , 8.64341528033551e-05 , 7.97889053472375e-05 , 9.82845151606708e-05 , 6.75369876955574e-05 , 7.94871587715466e-05 , 9.28890954855497e-05 , 8.17756108416586e-05 , 8.13518485015131e-05 , 9.23380101515964e-05 , 6.38674815032795e-05 , 7.4019104504842e-05 , 8.76412066853059e-05 , 7.3134988616935e-05 , 7.80253387361734e-05 , 6.98590861109206e-05 , 8.37681709997682e-05 , 7.78681183406401e-05 , 7.75360888613941e-05 , 8.57628854489922e-05 , 7.50104807254164e-05 , 9.70816294988619e-05 , 8.65005709839268e-05 , 9.10233045157741e-05 , 8.65089098348133e-05 , 7.86196622774781e-05 , 7.30487925477049e-05 , 7.18769800268041e-05 , 7.10210407671115e-05 , 9.54275285749659e-05 , 8.00591983079024e-05 , 7.82397156022772e-05 , 8.54669971830502e-05 , 9.10098078983285e-05 , 7.61447451365422e-05 , 7.41766138615937e-05 , 8.32873458731774e-05 , 9.72387260595565e-05 , 8.80288813357399e-05 , 8.76201986956681e-05 , 8.67431742854634e-05 , 6.20300640936345e-05 , 7.46761669713478e-05 , 8.65846621379941e-05 , 7.39459571082965e-05 , 7.59675056903756e-05 , 8.06379952300787e-05 , 8.44384485429816e-05 , 7.93605156635565e-05 , 8.78797654717914e-05 , 8.13040086387347e-05 , 8.10778220209004e-05 , 8.70121599421275e-05 , 0.000109439576130608 , 9.02348628721857e-05 , 7.85842207560927e-05 , 7.6337523898567e-05 , 7.31013121262569e-05 , 9.6084063609991e-05 , 8.05140795975154e-05 , 7.5520113016103e-05 , 7.62595381278356e-05 , 8.11812033705672e-05 , 9.55471854262335e-05 , 8.24751175371162e-05 , 7.99379602452841e-05 , 7.9791817578694e-05 , 7.46026214166558e-05 , 9.25099706143335e-05 , 7.03852253456436e-05 , 8.97392059110786e-05 , 9.10088741706802e-05 , 7.4827800563782e-05 , 8.67447212282118e-05 , 7.652474453691e-05 , 7.36150525872533e-05 , 7.78963015251251e-05 , 7.80880365777775e-05 , 7.33254920828694e-05 , 7.14432856399823e-05 , 9.07093463579799e-05 , 7.20861284165556e-05 , 7.71881472063593e-05 , 9.91233069543969e-05 , 7.07914575676011e-05 , 9.50389888737708e-05 , 8.9389729492371e-05 , 7.57369453485519e-05 , 9.00023130347773e-05 , 7.77713365398204e-05 , 8.26355817317967e-05 , 7.83727221099925e-05 , 7.4344935479553e-05 , 8.38312644685292e-05 , 7.38907412191759e-05 , 8.28064211688874e-05 , 8.56200556855738e-05 , 8.54118422450951e-05 , 8.0846577222901e-05 , 6.66306956925327e-05 , 8.42745451068001e-05 , 9.22676510649875e-05 , 9.05812587755955e-05 , 8.34443403975103e-05 , 7.2468669576973e-05 , 8.60217034370583e-05 , 8.87803189671566e-05 , 8.40572435161317e-05 , 8.2375515608265e-05 , 8.72089493672299e-05 , 8.92386292037965e-05 , 7.7321706664353e-05 , 9.48539369313877e-05 , 7.82325655422112e-05 , 7.88587311194897e-05 , 6.12971568684862e-05 , 9.86676651983264e-05 , 8.44782180790178e-05 , 7.25005571717218e-05 , 9.31506533512914e-05 , 7.68451826075576e-05 , 9.61352779273209e-05 , 8.22535433620533e-05 , 8.88401988898716e-05 , 5.7368655141976e-05 , 9.43683368766725e-05 , 9.46919138935022e-05 , 7.83062823497553e-05 , 8.66341331744823e-05 , 8.03358088558252e-05 , 8.02954426552289e-05 , 9.29002348362239e-05 , 9.76140481236503e-05 , 6.99897680000816e-05 , 6.84325902659195e-05 , 7.10052585166883e-05 , 9.17103117701564e-05 , 8.47553335038473e-05 , 0.000100195318601671 , 7.39411305811077e-05 , 8.73737054151313e-05 , 6.54262607944402e-05 , 8.26005372279447e-05 , 9.40766966415021e-05 , 8.40570139394394e-05 , 8.32389988846452e-05 , 0.000105555849210801 , 8.43379151549714e-05 , 8.6542776584437e-05 , 8.13502321093254e-05 , 7.49586983063665e-05 , 8.60321222243637e-05 , 5.90080020631661e-05 , 7.39450631003699e-05 , 6.93419810588636e-05 , 8.59061475332039e-05 , 6.12425878456712e-05 , 6.84278169812998e-05 , 7.48341249018789e-05 , 8.54687808287217e-05 , 7.95429438968889e-05 , 7.67924558644659e-05 , 7.73521486583513e-05 , 8.28847804526009e-05 , 0.000103560867467733 , 7.35567566801547e-05 , 7.17223867623591e-05 , 8.38685287383189e-05 , 9.35723341360076e-05 , 0.000103487040907377 , 7.78562335695492e-05 , 8.70121124031759e-05 , 8.29740661742719e-05 , 8.79164504169475e-05 , 8.58233488548016e-05 , 9.58092656886269e-05 , 0.000100469782260996 , 8.83972477686073e-05 , 7.61138460936409e-05 , 7.41559243482071e-05 , 9.50748630110072e-05 , 8.37040929238613e-05 , 8.37502369717202e-05 , 6.65219215914592e-05 , 7.9934785649973e-05 , 9.7495805701392e-05 , 8.00119680015466e-05 , 8.76766403314432e-05 , 7.66475173007073e-05 , 7.39334220092162e-05 , 7.77286392114712e-05 , 8.01467116403014e-05 , 8.16367333916679e-05 , 8.12739502821282e-05 , 7.63775631356292e-05 , 7.71471577903636e-05 , 8.26474083140042e-05 , 8.81594593144686e-05 , 9.07093226056759e-05 , 8.68499670774804e-05 , 7.60290476847027e-05 , 8.54993686045404e-05 , 8.19956934672603e-05 , 7.67235332473808e-05 , 8.49506178730528e-05 , 9.09212568982495e-05 , 7.06884108073003e-05 , 7.53803529942619e-05 , 7.83857379355414e-05 , 9.56518644168316e-05 , 7.90241666126352e-05 , 7.88646530360321e-05 , 8.41433018135625e-05 , 8.00842194741539e-05 , 7.40798429278931e-05 , 9.94632749485314e-05 , 8.21821050635686e-05 , 9.59236458638392e-05 , 8.53279562468714e-05 , 9.41072707955564e-05 , 0.000107585906111944 , 9.58321352931763e-05 , 7.7439721280841e-05 , 7.18625161107672e-05 , 9.56393489464636e-05 , 9.24861423375279e-05 , 8.44078039797996e-05 , 8.5148962189532e-05 , 7.66263043773153e-05 , 8.76612230454225e-05 , 9.14827373707543e-05 , 7.94684501067572e-05 , 7.28743986739702e-05 , 7.72482274192602e-05 , 7.37027389458352e-05 , 9.44379855967609e-05 , 8.21969343771601e-05 , 8.67810817102775e-05 , 9.72492676478023e-05 , 8.93332637979939e-05 , 9.52469081127755e-05 , 6.56700770340861e-05 , 8.45271456349873e-05 , 8.83605343954807e-05 , 9.02043486970886e-05 , 8.73651069539185e-05 , 8.508876846241e-05 , 7.03088135389129e-05 , 7.85725264832678e-05 , 9.4713714398389e-05 , 8.36368956120976e-05 , 8.03359790618387e-05 , 6.78381960132318e-05 , 8.47645824967194e-05 , 9.84713650120266e-05 , 8.71327376693591e-05 , 9.99008375568115e-05 , 9.18158734348995e-05 , 7.44317326166889e-05 , 8.80393977060951e-05 , 7.83806074474559e-05 , 8.82399550332299e-05 , 7.72577952274759e-05 , 8.92415967635025e-05 , 6.28835202470605e-05 , 8.50915904311932e-05 , 9.69985789857186e-05 , 8.55295649855376e-05 , 8.75082603362225e-05 , 9.09316582475909e-05 , 6.99588860380832e-05 , 6.84776195851645e-05 , 7.52833852763376e-05 , 7.225872427367e-05 , 8.67932301139964e-05 , 9.5003097267605e-05 , 8.01043145281e-05 , 9.3510847981782e-05 , 9.56071309916164e-05 , 8.57934788672115e-05 , 7.47832403418488e-05 , 6.74771838987688e-05 , 9.13139661027117e-05 , 7.45271960196292e-05 , 8.83462068848155e-05 , 7.02653252639893e-05 , 8.61167518591516e-05 , 7.71260663165087e-05 , 8.23212807087248e-05 , 9.2101312466067e-05 , 7.69018658603984e-05 , 9.47705827026236e-05 , 7.97018966409744e-05 , 8.07979021064315e-05 , 0.000107806118230997 , 7.27795558608282e-05 , 8.02626336352446e-05 , 6.96139498431645e-05 , 8.13489238821347e-05 , 0.000100074515147317 , 6.37307062497307e-05 , 9.09590806243367e-05 , 8.81045593797417e-05 , 8.60951138243968e-05 , 6.69747263733447e-05 , 9.59602406644013e-05 , 6.089626189937e-05 , 7.99371274447994e-05 , 8.37424323477484e-05 , 7.47609312801187e-05 , 8.99404598079024e-05 , 7.32368161103617e-05 , 9.88843652882384e-05 , 8.23776785471916e-05 , 6.67366287584427e-05 , 0.000107893158484432 , 9.3846129534546e-05 , 8.5569952656581e-05 , 8.77292828376391e-05 , 7.33752225301726e-05 , 7.86228631706859e-05 , 7.77390006408133e-05 , 9.42718031524649e-05 , 6.89460012523336e-05 , 9.18648659179194e-05 , 7.96042544183538e-05 , 7.51937979451023e-05 , 7.4181318838139e-05 , 8.11692946743056e-05 , 8.12358230606766e-05 , 8.40251333599078e-05 , 0.000100377778993875 , 7.79467471730603e-05 , 7.38607825234405e-05 , 8.2059461695045e-05 , 6.88004436658658e-05 , 7.85852939328257e-05 , 8.99553099266676e-05 , 7.415469248303e-05 , 7.09710694617005e-05 , 8.59555409409972e-05 , 6.71360512752293e-05 , 8.32934693406917e-05 , 8.58784043875092e-05 , 9.17829779917562e-05 , 9.52600072349062e-05 , 8.17004142369388e-05 , 8.67766066848941e-05 , 9.71279798704004e-05 , 8.0995587348777e-05 , 7.7433104020976e-05 , 8.61724602351863e-05 , 8.25470306643349e-05 , 8.84510552946986e-05 , 7.25418231680128e-05 , 8.47028778539682e-05 , 6.94509323024331e-05 , 8.5718140561333e-05 , 7.8770814729392e-05 , 9.67530233591282e-05 , 8.97585617552819e-05 , 9.21691678591068e-05 , 6.65727821013571e-05 , 7.60021325659079e-05 , 8.12478621728794e-05 , 8.59958792746685e-05 , 8.36960043581442e-05 , 7.01575042429371e-05 , 8.57942585361146e-05 , 6.54113045312806e-05 , 8.19137305843735e-05 , 7.6855594786111e-05 , 9.29754243337834e-05 , 8.69577157086979e-05 , 7.74688755886869e-05 , 7.7990562557043e-05 , 8.22744847206398e-05 , 7.79709778190657e-05 , 7.61529124722675e-05 , 9.50882820408906e-05 , 0.000102034551740795 , 7.22040282241486e-05 , 8.34854827996266e-05 , 8.4329434061583e-05 , 8.41214660876611e-05 , 8.13096858712433e-05 , 8.45455525380393e-05 , 8.24171965801847e-05 , 8.32524653216603e-05 , 7.35089833442168e-05 , 9.4929948703978e-05 , 7.61339751256264e-05 , 8.92103422396434e-05 , 6.96588631016521e-05 , 8.51463730701273e-05 , 9.26805074753872e-05 , 0.000104939030722566 , 8.00873055152132e-05 , 8.45061695488167e-05 , 8.42962960891549e-05 , 5.46176632336652e-05 , 7.34705577252939e-05 , 7.48161779714619e-05 , 8.44453907619565e-05 , 7.98805790409065e-05 , 8.55012090624576e-05 , 7.66960317244663e-05 , 9.72888791164028e-05 , 7.93768454192467e-05 , 9.19351756179612e-05 , 7.56428783277542e-05 , 8.34896510123919e-05 , 7.38515691032997e-05 , 8.9201060352664e-05 , 8.00259956724171e-05 , 8.52203065104761e-05 , 7.3152670548734e-05 , 8.53891607188066e-05 , 6.83593784638123e-05 , 8.82942822728837e-05 , 8.00505925062488e-05 , 7.83255614211715e-05 , 8.56377699002543e-05 , 6.62659830025762e-05 , 6.70084218709181e-05 , 7.81130079504255e-05 , 9.3839040986604e-05 , 7.46036927417697e-05 , 8.43842805078824e-05 , 8.89102273215777e-05 , 7.05097591709063e-05 , 9.37405117887987e-05 , 9.06518598865084e-05 , 7.49248035781908e-05 , 9.41993064846115e-05 , 7.35818587150744e-05 , 8.44600353996145e-05 , 7.29637812162819e-05 , 0.00010070491332943 , 7.78664551284871e-05 , 9.41628045066642e-05 , 7.71782442862443e-05 , 9.06020842144899e-05 , 8.4283060805051e-05 , 8.34190774938153e-05 , 7.24684523401352e-05 , 8.95130897223415e-05 , 6.74433633021476e-05 , 8.74464124698647e-05 , 8.17601182430774e-05 , 8.82544272239838e-05 , 9.52179677767985e-05 , 7.95008933367293e-05 , 8.87977007919396e-05 , 7.26011471845029e-05 , 8.19604156226147e-05 , 8.59438605003949e-05 , 9.21585005854874e-05 , 8.58517787408752e-05)
B.esw <- c(
65.960334048781 , 64.5121439706043 , 62.5934036516555 , 60.7873720171166 , 57.0595765023233 , 58.7932756576273 , 62.9678703913832 , 56.8876237004058 , 61.653994751669 , 64.9206177057004 , 55.7559776276991 , 60.3969792787867 , 65.13695691289 , 60.9872763407773 , 60.9986334324486 , 63.5207437383364 , 63.0877739011678 , 59.2841802388783 , 58.816061045893 , 59.0508659722084 , 65.3514652091511 , 62.0578691800862 , 59.9499568434843 , 59.8335898239129 , 60.5867021770309 , 60.7834805475399 , 60.5423516981703 , 70.3266394484822 , 62.9661569005563 , 62.2920629471591 , 64.1092971163947 , 61.9302168911723 , 60.7388531740376 , 64.0487979075723 , 59.7763498414029 , 63.8036933605511 , 65.2679441925455 , 62.4899449400625 , 63.2823507187653 , 63.3019535942845 , 60.1690449588103 , 60.4204076097981 , 59.1330040885495 , 65.6245512872223 , 61.4711957096196 , 66.2188583592455 , 60.2822737750428 , 59.8084794185536 , 61.8222903323166 , 64.705215503382 , 64.2265903913138 , 61.5423602597157 , 64.9231787888112 , 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61.7779966925246 , 67.0077603590823 , 67.5862057874461 , 65.1007927607243 , 56.6837619397975 , 63.1422948245646 , 56.3444938089122 , 57.5867524008523 , 60.7050052731612 , 56.5224950706037 , 58.2733741148302 , 62.2876402507214 , 61.4435276121104 , 66.104848795276 , 62.2603522622575 , 63.3628677022393 , 60.7460869640565 , 59.2208489694621 , 66.2116948371067 , 62.4077720171072 , 64.2781395011178 , 64.224770566396 , 59.9551584927244 , 60.2826165818762 , 61.1519071240909 )
fit$B <- data.frame(
density = B.d
, effDistance = B.esw
)
units(fit$B[,1]) <- "1/m^2"
units(fit$B[,2]) <- "m"
d <- fit$density
d.ci <- fit$density.ci
units(d) <- "1/ha"
units(d.ci) <- "1/ha"
## -----------------------------------------------------------------------------
fit
## ----bootstrapPrintout--------------------------------------------------------
# Convert to hectares for readability
units(fit$B$density) <- "1/ha"
head(fit$B)
## ----bootPlot, fig., fig.width=6, fig.height=4, fig.cap="Bootstrap distribution of sparrow density computed in the `abundEstim` routine."----
# Figure 3
hist(fit$B$density
, n = 30
, xlab = "Density"
, main = NULL)
# Show final density estimates, after converting to 1/ha
d <- fit$density
d.ci <- fit$density.ci
units(d) <- "1/ha"
units(d.ci) <- "1/ha"
abline(v = c(d, d.ci), col="blue")
## ----changeUnits--------------------------------------------------------------
dfuncSparrow <- dfuncEstim(formula = dist~1
, detectionData = sparrowDetectionData
, likelihood = "halfnorm"
, w.hi = rightTruncDistance
, outputUnits = "km")
## ----include=FALSE------------------------------------------------------------
fit <- abundEstim(dfuncSparrow
, detectionData = sparrowDetectionData
, siteData = sparrowSiteData
, area = saSize
, ci = NULL
)
fit$B <- matrix(1, nrow = 100, ncol = 2)
fit$n.hat.ci <- c( `2.5%`= 250119.1
, `97.5%` = 390627.4 )
fit$density.ci <- units::set_units(c(6.093035e-05, 9.515893e-05), "1/m^2")
fit$effDistance.ci <- units::set_units(c(56.74188, 68.64928 ), "m")
fit$nItersConverged <- 500
fit$alpha <- 0.95
units(fit$density.ci) <- "1/km^2"
units(fit$effDistance.ci) <- "km"
## -----------------------------------------------------------------------------
fit