Description Usage Arguments Value See Also Examples
Performs a number of standard alpha diversity estimates,
and returns the results as a data.frame
.
Strictly speaking, this function is not only estimating richness,
despite its name.
It can operate on the cumulative population of all
samples in the dataset, or by repeating the richness estimates for each
sample individually.
NOTE: You must use untrimmed datasets
for meaningful results, as these estimates (and even the “observed” richness)
are highly dependent on the number of singletons. You can always trim the data
later on if needed, just not before using this function.
1 | estimate_richness(physeq, split = TRUE, measures = NULL)
|
physeq |
(Required). |
split |
(Optional). Logical. Should a separate set of richness estimates be performed for each sample? Or alternatively, pool all samples and estimate richness of the entire set. |
measures |
(Optional). Default is |
A data.frame
of the richness estimates, and their standard error.
Check out the custom plotting function, plot_richness
,
for easily showing the results of different estimates,
with method-specific error-bars.
Also check out the internal functions borrowed from the vegan
package:
1 2 3 4 5 6 7 8 9 | ## There are many more interesting examples at the phyloseq online tutorials.
## http://joey711.github.com/phyloseq/plot_richness-examples
data("esophagus")
# Default is all available measures
estimate_richness(esophagus)
# Specify just one:
estimate_richness(esophagus, measures="Observed")
# Specify a few:
estimate_richness(esophagus, measures=c("Observed", "InvSimpson", "Shannon", "Chao1"))
|
Observed Chao1 se.chao1 ACE se.ACE Shannon Simpson InvSimpson
B 28 39.14286 8.224252 49.09697 4.123723 2.249367 0.8313718 5.930206
C 33 37.50000 4.103809 40.94645 3.100302 2.762389 0.9033449 10.346062
D 38 71.00000 19.321782 88.97684 6.506711 2.032493 0.6657493 2.991766
Fisher
B 8.803673
C 10.098927
D 13.278349
Observed
B 28
C 33
D 38
Observed Chao1 se.chao1 Shannon InvSimpson
B 28 39.14286 8.224252 2.249367 5.930206
C 33 37.50000 4.103809 2.762389 10.346062
D 38 71.00000 19.321782 2.032493 2.991766
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