predictAxes.RdThis function replicates the analyses presented in August et al XXXX. This allows you to use your own data to extract the values for the 4-axes (recording intensity, spatial extent, recording potential, and rarity recording). This function applies the same centring and scaling values as used in August et al and performs the same pre-analysis log transformations. Note that the axis values, while comparable to those used in August et al, may not be optimal for your data, and you should also extract the raw metrics and apply your own PCA to see if the same axes are important for explaining the variation observed in your data.
predictAxes(data, recorders = NULL, verbose = TRUE, recorder_col = "recorder", date_col = "date", y_col = "lat", x_col = "long", square_km_col = "km_sq", active_days_limit = 10, crs = "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs", new_crs = "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +datum=OSGB36 +units=m +no_defs", sp_col = "species")
| data | The data.frame of recording information. See `head(cit_sci_data)`, for an example format. This should be the data for all observations made to your citizen science project. |
|---|---|
| recorders | Optional. A vector of recorders (as in `recorder_col`), for which you want to calculate values. |
| verbose | Should progress be reported? |
| recorder_col | The name of the column that contains the recorder names |
| date_col | The name of the column that contains the date. This must be formatted as a date |
| y_col | The name of the column that contains the y coordinate (e.g. latitude) of the observation. This should be a numeric. |
| x_col | The name of the column that contains the x coordinate (e.g. longitude) of the observation. This should be a numeric. |
| square_km_col | To calculate list lengths the location of recorders is defined by the 1km-square in which they are recorded. To make results comparable to August et al provide the 1km-square of each record here (i.e. a grid reference). |
| active_days_limit | If there are less than this number of active days NA values will be returned for the metrics. August et al use 10, and changing this value will result in metrics that are not comparable to August et al |
| crs | The proj4 string that describes the projection your data are using. For GPS lat long this is "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs". You can find more at http://spatialreference.org/ |
| new_crs | The proj4 string that the describes the coordinate system your data should be reprojected to. THIS IS IMPORTANT. Your data must be on a projection that has units in meters so that results are comparable to other studies. An appropriate system in the UK is the UK national grid "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +datum=OSGB36 +units=m +no_defs". If your original crs (given in the argument crs), already has units in meters then set new_crs = NULL. WARNING: if you set this to NULL but your coordinate system is not in units of meters you will likely have errors. |
| sp_col | The name of the column that contains the species names |
# NOT RUN { # load example data head(cit_sci_data) # Run for 10 recorders metrics_axes <- predictAxes(data = cit_sci_data, recorders = unique(cit_sci_data$recorder)[1:10]) # The returned object is a list of the metrics... metrics_axes$recorder_metrics # ...and the axes values metrics_axes$axes # Run the metric all recorders. NOTE: this takes a long time metrics_axes <- predictAxes(data = cit_sci_data) # }