flatfile {Distance} | R Documentation |
Distance
allows loading data as a "flat file" and analyse data (and
obtain abundance estimates) straight away, provided that the format of the
flat file is correct. One can provide the file as, for example, an Excel
spreadsheet using readxl::read_xls
in or CSV using
read.csv
.
Each row of the data table corresponds to one observation and must have a the following columns:
distance
observed distance to object
Sample.Label
Identifier for the sample (transect id)
Effort
effort for this transect (e.g. line transect length or number
of times point transect was visited)
Region.Label
label for a given stratum (see below)
Area
area of the strata'
Note that in the simplest case (one area surveyed only once) there is only
one Region.Label
and a single corresponding Area
duplicated for each
observation.
The example given below was provided by Eric Rexstad.
## Not run: library(Distance) # Need to have the readxl package installed from CRAN require(readxl) # Need to get the file path first minke.filepath <- system.file("minke.xlsx", package="Distance") # Load the Excel file, note that col_names=FALSE and we add column names after minke <- read_xlsx(minke.filepath, col_names=FALSE) names(minke) <- c("Region.Label", "Area", "Sample.Label", "Effort", "distance") # One may want to call edit(minke) or head(minke) at this point # to examine the data format ## perform an analysis using the exact distances pooled.exact <- ds(minke, truncation=1.5, key="hr", order=0) summary(pooled.exact) ## Try a binned analysis # first define the bins dist.bins <- c(0,.214, .428,.643,.857,1.071,1.286,1.5) pooled.binned <- ds(minke, truncation=1.5, cutpoints=dist.bins, key="hr", order=0) # binned with stratum as a covariate minke$stratum <- ifelse(minke$Region.Label=="North", "N", "S") strat.covar.binned <- ds(minke, truncation=1.5, key="hr", formula=~as.factor(stratum), cutpoints=dist.bins) # Stratified by North/South full.strat.binned.North <- ds(minke[minke$Region.Label=="North",], truncation=1.5, key="hr", order=0, cutpoints=dist.bins) full.strat.binned.South <- ds(minke[minke$Region.Label=="South",], truncation=1.5, key="hr", order=0, cutpoints=dist.bins) ## model summaries model.sel.bin <- data.frame(name=c("Pooled f(0)", "Stratum covariate", "Full stratification"), aic=c(pooled.binned$ddf$criterion, strat.covar.binned$ddf$criterion, full.strat.binned.North$ddf$criterion+ full.strat.binned.South$ddf$criterion)) # Note model with stratum as covariate is most parsimonious print(model.sel.bin) ## End(Not run)