dens | R Documentation |
calculates mean "density" in abundance, size and occupancy and 95% confidence intervals.
dens(
object,
group,
years,
values = "count",
density = TRUE,
species = NA,
plots = NA,
area = "count",
plotarea = NA,
subplots = NA,
subplotarea = NA,
subplotnumber = NA,
output = "dataframe",
...
)
object |
Either an object of class |
group |
A required character string indicating which group of plants should be selected. Options are: "trees", "saplings", "seedlings", "shrubs" "shseedlings"(indicated shrub seedlings), "vines", or "herbs'. |
years |
Defaults to |
values |
Defaults to "count". Passed on to
|
density |
Logical Value, Only used when |
species |
Character vector, defaults to |
plots |
Character vecltor, defaults to |
area |
A character vector. Used when
|
plotarea |
Single number. Defaults to |
subplots |
Single number. Defaults to |
subplotarea |
Vector of numbers. Indicates how many subplots were sampled for each plot. If left as |
subplotnumber |
Vector of number Inidcates how large an area was sampled for each plot. If left as |
output |
Either "dataframe" or "list". Determines the output type When |
... |
Any additional arguments which are valid for either |
This function calculates the mean and 95% confidence values for a given measurement for one or more object of class NPSForVeg
. The raw data for this calculation is acquired by calling SiteXSpec
which will in turn call getPlants
. The method of calculating the results depends on the values
argument. If values="count"
the data is assuemd to follow a negative binomial distribution. The mean and the intervals are calculated using the glm.nb
function in the MASS package. Note that when a species if found in few of the plots, this function will often return a warning
.
If values="size"
no distribution is assumed for the underlying data. Instead, confidence intervals and determined using 1000 bootstrap replicates. Note that if a species is relatively rare this can lead to the 95
If values="presab"
data is treat as being binomial and confidence intervals are detemined using glm
.
Complications arise when the sampling effort is uneven in the data you are analyzing. This can happen in a variety of ways. If a subplot is not sampled during an event, perhaps due to a safety concern, the the analysis need to take into account that some of the data is missing. Similarly, if the design of the plot changes, adding or remving subplots or changing the area sampled similar problems occur. Finally, if you are combining data from areas with differnt plot designs, this also must be taken into account.
When working with a single park, or a list of parks when output="list"
, the function will use an offset of area sampled when
values = "count"
, will use input data on a per ha or per acre basis (determined by area
) when values="size"
and
will use an offset based on subplots sampled when values="presab"
. Areas and subplots sampled are specified using the
subplotarea
and subplotnumber
argurments, but will be automatically determined with a call to getSubplotCount
if left as NA
When the input is a list, output="list"
data from muliple parks are combined. Differences in sampled area are accounted for
in the same manner as above. However, if the output is desired on a per plot basis rather than a per area basis (always the case when
values="preasb"
), then it is necessary to assume a particular plot size or number of subplots. This is particularly true
when combining parks with different areas sampled. This is done through the plotarea
and subplots
arguments.
Returns a data.frame
,or a list
or them, which indicate the mean and 95% confidence intervals of the requested measurement.
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