This is the main object of the visualFields package. It is essentially a dataframe, but with a fixed number of columns (with pre-determined names) for information about the subject and test data and a variable number of columns for the perimetry results. These can be the sensitivities, or total-deviation values, or pattern-deviation values obtained from static automated perimetry (SAP), frequency-doubling perimetry (FDP), or any other perimetry device. (The number of columns for tested locations is variable as is different for different testing patterns, 24-2, 30-2, 10-2, etc.) Mean deviation, pattern standard deviation, vfi, etc are stored too in a visualField-type object
Details
The fixed columns of the visualField object with information about subject and test are:
id
subject identification number
tperimetry
test perimetry. The type of perimetry analysys performed. Possible values include
"sap" and "fdp". The value of this column, tperimetry, is used
in conjunction with the value in talgorithm, and tpattern to find the
corresponding normative values (see help on nv) to use for data analysis (e.g.
calculation of total-deviation and pattern-deviation values and probability maps). At
the moment, only normative values for SAP, 24-2, SITA standard, is distributed with
visualFields. Nevertheless, visualFields contains a number of functions
that can be used for the generation of normative values (see getnv,
ageLinearModel, sdnv, tdval, pdval, locperc,
vfstats, vfindex, gloperc, vfiperc, setnv).
talgorithm
test algorithm. The algorithm used for the perimetric test. Posible values are
sitas and zest. At the moment, only normative values for SAP, 24-2, SITA
standard, is distributed with visualFields
tpattern
test pattern. The pattern of locations used for the perimetric test. Posible values
are p24d2 or p10d2. At the moment, only normative values for SAP, 24-2,
SITA standard, is distributed with visualFields
tdate
test date
ttime
test time
stype
type of subject. Values can be ctr for controls, pwg for patients with
glaucoma, sus for suspect subjects. This is just for information to display in
the printouts
sage
subject age. Important for the calculation of total-deviation values and probabiliby
maps.
seye
eye tested
sbsx
estimated x-position of the blind spot in degrees of angle of vision
sbsy
estimated y-position of the blind spot in degrees of angle of vision
sfp
false positives
sfn
false negatives
sfl
fixation losses
sduration
total duration of the test
spause
total time of pause
The reminder of the columns can be different things. For threshold sensitivity values, and total-deviation and pattern-deviation values, and their corresponding probability maps, they are:
L1 .. L54 .. L68 .. L76
location number. There are up to 54 locations for the 24-2, up to 68 for
the 10-2, and 76 for the 30-2. Information about the position of the
locations, the size of the stimulus, and the x and y coordinates in
degrees of visual angles are specified in saplocmap (for SAP)
fdplocmap (for FDP)
For statistical values of the visual-fields results (mean deviation, pattern standard deviation, and others) and their corresponding probability mapped value, they are:
msens
mean sensitivity value; or the probability mapped value
ssens
standard deviation of the sensitivity values; or the probability mapped value
mtdev
mean deviation (mean value of the total-deviation values; or the probability mapped value)
stdev
standard deviation of the total-deviation values; or the probability mapped value
mpdev
mean value of the pattern-deviation values; or the probability mapped value)
stdev
standard pattern deviation (standard deviation pattern-deviation values; or the
probability mapped value
For visual field index (VFI) value and the corresponding probability mapped value, they are:
mvfi
visual field indes (VFI); or the probability mapped value
svfi
standard deviation of the VFI at each location; or the probability mapped value
Author(s)
Ivan Marin-Franch
See Also
vfsettings
Examples
# DO NOT EXECUTE
# one can load sensitivities using loadvfcsv or loadvfxml the data so
# vf <- loadvfcsv( filename = "foo.csv", , patternMap = saplocmap$p24d2 )
# calculate total deviation values using code{link{visualFields}} normative values for
# SAP SITAS 24-2 (and Goldman size III stimulus)
# td <- tdval( vf )
# calculate pattern deviation values using total deviation values SAP SITAS 24-2
# pd <- tdval( td )
# OR
# pd <- tdval( tdval( vf ) )
# calculate total deviation proabiliby maps
# tdp <- tdpmap( td )
# calculate pattern deviation proabiliby maps
# pdp <- pdpmap( pd )
providing 
.