unmarkedFramePCO.RdOrganizes repeated count data along with the covariates and possibly the dates on which each survey was conducted. This S4 class is required by the data argument of pcountOpen
unmarkedFramePCO(y, siteCovs=NULL, obsCovs=NULL, yearlySiteCovs, mapInfo, numPrimary, primaryPeriod)
| y | An MxJT matrix of the repeated count data, where M is the number of sites, J is the maximum number of secondary sampling periods per site and T is the maximum number of primary sampling periods per site. |
|---|---|
| siteCovs | A |
| obsCovs | Either a named list of |
| yearlySiteCovs | Either a named list of MxT |
| mapInfo | Currently ignored |
| numPrimary | Maximum number of observed primary periods for each site |
| primaryPeriod | matrix of integers indicating the primary period of each survey. |
unmarkedFramePCO is the S4 class that holds data to be passed
to the pcountOpen model-fitting function.
The unmarkedFramePCO class is similar to the unmarkedFramePCount class except that it contains the dates for each survey, which needs to be supplied .
an object of class unmarkedFramePCO
# Repeated count data with 5 primary periods and # no secondary sampling periods (ie J==1) y1 <- matrix(c( 0, 2, 3, 2, 0, 2, 2, 3, 1, 1, 1, 1, 0, 0, 3, 0, 0, 0, 0, 0), nrow=4, ncol=5, byrow=TRUE) # Site-specific covariates sc1 <- data.frame(x1 = 1:4, x2 = c('A','A','B','B')) # Observation-specific covariates oc1 <- list( x3 = matrix(1:5, nrow=4, ncol=5, byrow=TRUE), x4 = matrix(letters[1:5], nrow=4, ncol=5, byrow=TRUE)) # Primary periods of surveys primaryPeriod1 <- matrix(as.integer(c( 1, 2, 5, 7, 8, 1, 2, 3, 4, 5, 1, 2, 4, 5, 6, 1, 3, 5, 6, 7)), nrow=4, ncol=5, byrow=TRUE) # Create the unmarkedFrame umf1 <- unmarkedFramePCO(y=y1, siteCovs=sc1, obsCovs=oc1, numPrimary=5, primaryPeriod=primaryPeriod1)#> Warning: siteCovs contains characters. Converting them to factors.#> Warning: obsCovs contains characters. Converting them to factors.# Take a look umf1#> Data frame representation of unmarkedFrame object. #> y.1 y.2 y.3 y.4 y.5 x1 x2 x3.1 x3.2 x3.3 x3.4 x3.5 x4.1 x4.2 x4.3 x4.4 x4.5 #> 1 0 2 3 2 0 1 A 1 2 3 4 5 a b c d e #> 2 2 2 3 1 1 2 A 1 2 3 4 5 a b c d e #> 3 1 1 0 0 3 3 B 1 2 3 4 5 a b c d e #> 4 0 0 0 0 0 4 B 1 2 3 4 5 a b c d e#> unmarkedFrame Object #> #> 4 sites #> Maximum number of observations per site: 5 #> Mean number of observations per site: 5 #> Number of primary survey periods: 5 #> Number of secondary survey periods: 1 #> Sites with at least one detection: 3 #> #> Tabulation of y observations: #> 0 1 2 3 #> 9 4 4 3 #> #> Site-level covariates: #> x1 x2 #> Min. :1.00 A:2 #> 1st Qu.:1.75 B:2 #> Median :2.50 #> Mean :2.50 #> 3rd Qu.:3.25 #> Max. :4.00 #> #> Observation-level covariates: #> x3 x4 #> Min. :1 a:4 #> 1st Qu.:2 b:4 #> Median :3 c:4 #> Mean :3 d:4 #> 3rd Qu.:4 e:4 #> Max. :5# Repeated count data with 4 primary periods and # no 2 secondary sampling periods (ie J=2) y2 <- matrix(c( 0,0, 2,2, 3,2, 2,2, 2,2, 2,1, 3,2, 1,1, 1,0, 1,1, 0,0, 0,0, 0,0, 0,0, 0,0, 0,0), nrow=4, ncol=8, byrow=TRUE) # Site-specific covariates sc2 <- data.frame(x1 = 1:4, x2 = c('A','A','B','B')) # Observation-specific covariates oc2 <- list( x3 = matrix(1:8, nrow=4, ncol=8, byrow=TRUE), x4 = matrix(letters[1:8], nrow=4, ncol=8, byrow=TRUE)) # Yearly-site covariates ysc <- list( x5 = matrix(c( 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4), nrow=4, ncol=4, byrow=TRUE)) # Primary periods of surveys primaryPeriod2 <- matrix(as.integer(c( 1,2,5,7, 1,2,3,4, 1,2,4,5, 1,3,5,6)), nrow=4, ncol=4, byrow=TRUE) # Create the unmarkedFrame umf2 <- unmarkedFramePCO(y=y2, siteCovs=sc2, obsCovs=oc2, yearlySiteCovs=ysc, numPrimary=4, primaryPeriod=primaryPeriod2)#> Warning: siteCovs contains characters. Converting them to factors.#> Warning: obsCovs contains characters. Converting them to factors.# Take a look umf2#> Data frame representation of unmarkedFrame object. #> y.1 y.2 y.3 y.4 y.5 y.6 y.7 y.8 x1 x2 x5.1 x5.2 x5.3 x5.4 x3.1 x3.2 x3.3 x3.4 #> 1 0 0 2 2 3 2 2 2 1 A 1 2 3 4 1 2 3 4 #> 2 2 2 2 1 3 2 1 1 2 A 1 2 3 4 1 2 3 4 #> 3 1 0 1 1 0 0 0 0 3 B 1 2 3 4 1 2 3 4 #> 4 0 0 0 0 0 0 0 0 4 B 1 2 3 4 1 2 3 4 #> x3.5 x3.6 x3.7 x3.8 x4.1 x4.2 x4.3 x4.4 x4.5 x4.6 x4.7 x4.8 #> 1 5 6 7 8 a b c d e f g h #> 2 5 6 7 8 a b c d e f g h #> 3 5 6 7 8 a b c d e f g h #> 4 5 6 7 8 a b c d e f g h#> unmarkedFrame Object #> #> 4 sites #> Maximum number of observations per site: 8 #> Mean number of observations per site: 8 #> Number of primary survey periods: 4 #> Number of secondary survey periods: 2 #> Sites with at least one detection: 3 #> #> Tabulation of y observations: #> 0 1 2 3 #> 15 6 9 2 #> #> Site-level covariates: #> x1 x2 #> Min. :1.00 A:2 #> 1st Qu.:1.75 B:2 #> Median :2.50 #> Mean :2.50 #> 3rd Qu.:3.25 #> Max. :4.00 #> #> Observation-level covariates: #> x3 x4 #> Min. :1.00 a :4 #> 1st Qu.:2.75 b :4 #> Median :4.50 c :4 #> Mean :4.50 d :4 #> 3rd Qu.:6.25 e :4 #> Max. :8.00 f :4 #> (Other):8 #> #> Yearly-site-level covariates: #> x5 #> Min. :1.00 #> 1st Qu.:1.75 #> Median :2.50 #> Mean :2.50 #> 3rd Qu.:3.25 #> Max. :4.00