Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/BioGeoBEARS_detection_v1.R
This function applies calc_obs_like
to all
cells of the input matrices obs_target_species
and
obs_all_species
. These matrices obviously must
have the same dimensions.
1 2 3  mapply_calc_obs_like(truly_present = TRUE,
obs_target_species, obs_all_species,
mean_frequency = 0.1, dp = 1, fdp = 0)

truly_present 
Is the OTU of interest
known/conditionally assumed to be truly present
( 
obs_target_species 
A scalar or
column/vector/matrix of detection counts, e.g. as
produced from the output from

obs_all_species 
A scalar or column/vector/matrix
of detection counts, e.g. as produced from the output
from 
mean_frequency 
This is the proportion of samples from the taphonomic control group that will truly be from this OTU, GIVEN that the OTU is present. This could be estimated, but a decent first guess is (total # samples of OTU of interest / total # of samples in the taphonomic control group where the OTU is known to be present). All that is really needed is some reasonable value, such that more sampling without detection lowers the likelihood of the data on the hypothesis of true presence, and vice versa. This value can only be 1 when the number of detections = the number of taphonomic control detections, for every OTU and area. This is the implicit assumption in e.g. standard historical biogeography analyses in LAGRANGE or BioGeoBEARS. 
dp 
The detection probability. This is the persample probability that you will correctly detect the OTU in question, when you are looking at it. Default is 1, which is the implicit assumption in standard analyses. 
fdp 
The false detection probability. This is
probability of falsely concluding a detection of the OTU
of interest occurred, when in fact the specimen was of
something else. The default is 0, which assumes zero
error rate, i.e. the assumption being made in all
historical biogeography analyses that do not take into
account detection probability. These options are being
included for completeness, but it may not be wise to try
to infer 
The inputs are the same as for
calc_obs_like
, except that
obs_target_species
and obs_all_species
can
be matrices.
pp_df
A matrix of the natural loglikelihood of
the data, given the model & assumption of true presence
or absence.
Go BEARS!
Nicholas J. Matzke matzke@berkeley.edu
http://phylo.wikidot.com/matzke2013internationalbiogeographysocietyposter http://en.wikipedia.org/wiki/Bayes'_theorem
Matzke_2012_IBS
Bottjer_Jablonski_1988
Bayes_1763
calc_obs_like
,
calc_post_prob_presence
,
mapply_calc_post_prob_presence
,
Pdata_given_rangerow
,
mapply
,
tiplikes_wDetectionModel
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35  test=1
# Calculate likelihood of data, given presence in an area,
# given a dp (detection probability) and detection model.
# softcoded input files
extdata_dir = np(system.file("extdata", package="BioGeoBEARS"))
detects_fn = np(paste(extdata_dir, "/Psychotria_detections_v1.txt", sep=""))
controls_fn = np(paste(extdata_dir, "/Psychotria_controls_v1.txt", sep=""))
OTUnames=NULL
areanames=NULL
tmpskip=0
detects_df = read_detections(detects_fn, OTUnames=NULL, areanames=NULL, tmpskip=0)
controls_df = read_controls(controls_fn, OTUnames=NULL, areanames=NULL, tmpskip=0)
detects_df
controls_df
detects_df / controls_df
# Calculate data likelihoods, and posterior probability of presence=TRUE
mean_frequency=0.1
dp=1
fdp=0
mapply_calc_obs_like(truly_present=TRUE, obs_target_species=detects_df,
obs_all_species=controls_df, mean_frequency, dp, fdp)
mapply_calc_obs_like(truly_present=FALSE, obs_target_species=detects_df,
obs_all_species=controls_df, mean_frequency, dp, fdp)
mapply_calc_post_prob_presence(prior_prob_presence=0.01,
obs_target_species=detects_df,
obs_all_species=controls_df, mean_frequency, dp, fdp)

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