Single observer point count data example from Distance

Single observer point count data example from Distance

Format

The format is 144 obs of 6 variables: distance: numeric distance from center observer: Factor w/ 2 levels "1","2": 1 2 1 2 1 2 1 2 1 2 ... detected: numeric 0/1 object: sequential object number Sample.Label: point label Region.Label: single region label

Examples

# \donttest{
data(ptdata.distance)
xx <- ddf(dsmodel = ~cds(key="hn", formula = ~1), data = ptdata.distance,
          method = "ds", meta.data = list(point=TRUE))
#> Warning: no truncation distance specified; using largest observed distance
summary(xx)
#> 
#> Summary for ds object
#> Number of observations :  144 
#> Distance range         :  0  -  34.16 
#> AIC                    :  919.1403 
#> Optimisation           :  mrds (nlminb) 
#> 
#> Detection function:
#>  Half-normal key function 
#> 
#> Detection function parameters 
#> Scale coefficient(s): 
#>             estimate         se
#> (Intercept) 2.283007 0.04523359
#> 
#>                        Estimate           SE         CV
#> Average p             0.1644301   0.01466592 0.08919244
#> N in covered region 875.7520203 102.72037375 0.11729390
#> EDR                  13.8518741   0.61774122 0.04459622
plot(xx,main="Distance point count data")

ddf.gof(xx)

#> 
#> Goodness of fit results for ddf object
#> 
#> Chi-square tests
#>           [0,2.85] (2.85,5.69] (5.69,8.54] (8.54,11.4] (11.4,14.2] (14.2,17.1]
#> Observed     5.000      19.000      16.000      25.000      28.000      23.000
#> Expected     5.955      16.432      23.166      25.232      23.213      18.671
#> Chisquare    0.153       0.401       2.217       0.002       0.987       1.004
#>           (17.1,19.9] (19.9,22.8] (22.8,25.6] (25.6,28.5] (28.5,31.3]
#> Observed       14.000       5.000       5.000       3.000       0.000
#> Expected       13.356       8.578       4.978       2.620       1.254
#> Chisquare       0.031       1.493       0.000       0.055       1.254
#>           (31.3,34.2]   Total
#> Observed        1.000 144.000
#> Expected        0.547 144.000
#> Chisquare       0.375   7.973
#> 
#> P = 0.6315 with 10 degrees of freedom
#> 
#> Distance sampling Cramer-von Mises test (unweighted)
#> Test statistic = 0.0954697 p-value = 0.607543
Regions <- data.frame(Region.Label=1,Area=1)
Samples <- data.frame(Sample.Label=1:30,
                      Region.Label=rep(1,30),
                      Effort=rep(1,30))
print(dht(xx,sample.table=Samples,region.table=Regions))
#> Abundance and density estimates from distance sampling
#> Variance       : P2, N/L 
#> 
#> Summary statistics
#> 
#>   Region Area CoveredArea Effort   n  k  ER     se.ER      cv.ER
#> 1      1    1    109978.3     30 144 30 4.8 0.4245349 0.08844477
#> 
#> Abundance:
#>   Region    Estimate          se        cv         lcl        ucl       df
#> 1  Total 0.007962956 0.001000224 0.1256096 0.006212055 0.01020736 97.52316
#> 
#> Density:
#>   Region    Estimate          se        cv         lcl        ucl       df
#> 1  Total 0.007962956 0.001000224 0.1256096 0.006212055 0.01020736 97.52316
# }