pedometrics (version 0.3-0)

gcpDiff: Diffence on xyz coordinates between ground control points

Description

This function estimates the difference, absolute difference, and squared difference on x, y and z coordinates of two sets of ground control points (GCP). It also estimates the module (difference vector), its square and azimuth. The result is a data frame ready to be used to define a object of class spsurvey.object.

Usage

gcpDiff(measured, predicted, type = "xy", aggregate = FALSE, rounding = 0)

Arguments

measured

Object of class SpatialPointsDataFrame with the reference GCP. A column named siteID giving case names is mandatory. See Details, item Type of data.

predicted

An object of class SpatialPointsDataFrame with the point data being validated. A column named siteID giving case names is mandatory. See Details, item Type of data

type

Type of data under analysis. Defaults to type = "xy". Details, item Type of data.

aggregate

Logical for aggregating the data when it comes from cluster sampling. Used only when type = "z". Defaults to aggregate = FALSE. See Details, item Data aggregation.

rounding

Rounding level of the data in the output data frame.

Value

• An object of class data.frame ready to be used to feed the argument data.cont when creating a spsurvey.analysis object.

Details

Type_of_data{

Two types of validation data that can be submitted to function gcpDiff(): those coming from horizontal (positional) validation exercises (type = "xy"), and those coming from vertical validation exercises (type = "z").

Horizontal (positional) validation exercises compare the position of measured point data with the position of predicted point data. Horizontal displacement (error) is measured in both x and y coordinates, and is used to calculate the error vector (module) and its azimuth. Both objects measured and predicted used with function gcpDiff() must be of class SpatialPointsDataFrame. They must have at least one column named siteID giving the identification of evary case. Matching of case IDs is mandatory. Other columns are discarded.

Vertical validation exercises are interested in comparing the measured value of a variable at a given location with that predicted by some model. In this case, error statistics are calculated only for the the vertical displacement (error) in the z coordinate. Both objects measured and predicted used with function gcpDiff() must be of class SpatialPointsDataFrame. They also must have a column named siteID giving the identification of evary case. Again, matching of case IDs is mandatory. However, both objects must have a column named z which contains the values of the z coordinate. Other columns are discarded. } Data_aggregation{

Validation is sometimes performed using cluster or transect sampling. Before estimation of error statistics, the data needs to be aggregated by cluster or transect. The function gcpDiff() aggregates validation data of type = "z" calculating the mean value per cluster. Thus, aggregation can only be properly done if the siteID column of both objects measured and predicted provides the identification of clusters. Setting aggregate = TRUE will return aggregated estimates of error statistics. If the data has been aggregated beforehand, the parameter aggregate can be set to FALSE. } Case_matching{

There are circumstances in which the number of cases in the object measured is larger than that in the object predicted. The function gcpDiff() compares the number of cases in both objects and automatically drops those cases of object measured that do not match the cases of object predicted. However, case matching can only be done if case IDs are exactly the same for both objects. Otherwise, estimated error statistics will have no meaning at all. }

References

Kincaid, T. M. and Olsen, A. R. (2013). spsurvey: Spatial Survey Design and Analysis. R package version 2.6. URL: http://www.epa.gov/nheerl/arm/.

See Also

coordenadas, gcpVector, spsurvey.analysis.

Examples

## Create an spsurvey.analysis object
my.spsurvey <- 
  spsurvey.analysis(design = coordenadas(my.data),
                    data.cont = delta(ref.data, my.data),
                    popcorrect = TRUE, pcfsize = length(my.data$id),
                    support = rep(1, length(my.data$id)),
                    wgt = rep(1, length(my.data$id)), vartype = "SRS")