| Title: | Statistical Methods for Visual Fields |
|---|---|
| Description: | A collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research. The package is described in Marin-Franch and Swanson (2013) <doi:10.1167/13.4.10> and is part of the Open Perimetry Initiative (OPI) [Turpin, Artes, and McKendrick (2012) <doi:10.1167/12.11.22>]. |
| Authors: | Ivan Marin-Franch [cre, aut, rev, cph], William H Swanson [fnd, dtc], Michael Wall [fnd, dtc], Andrew Turpin [ctb], Paul H Artes [ctb, dtc], Cord Huchzermeyer [ctb], Giovanni Montesano [ctb], Mitchell W Dul [dtc] |
| Maintainer: | Ivan Marin-Franch <[email protected]> |
| License: | Apache License 2.0 |
| Version: | 1.0.7 |
| Built: | 2025-02-16 06:18:06 UTC |
| Source: | https://github.com/cran/visualFields |
visualFields is a collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research.
The development version of visualFields 1.x, can be found in
https://github.com/imarinfr/vf1. For developers who want to collaborate
extending, updating, and patching visualFields, all necessary imports are to
be added to the source file visualFields.R. visualField developers can
use the source codes here as examples on how to craft new source code and keep
documentation that is consistent with the rest of the package, roxygen2, and CRAN.
The previous version of visualFields, 0.6, is still available for use in https://github.com/imarinfr/vf0, but is no longer maintained.
This work was supported by the NIH grant number R01EY007716 and the Veterans Administration grant number I01 RX-001821-01A1.
Maintainer: Ivan Marin-Franch [email protected] [reviewer, copyright holder]
Other contributors:
William H Swanson [funder, data contributor]
Michael Wall [funder, data contributor]
Andrew Turpin [contributor]
Paul H Artes [contributor, data contributor]
Cord Huchzermeyer [contributor]
Giovanni Montesano [contributor]
Mitchell W Dul [data contributor]
Marín-Franch I & Swanson WH. The visualFields package: A tool for analysis and visualization of visual fields. Journal of Vision, 2013, 13(4):10, 1-12
Turpin A, Artes PH, & McKendrick AM. The Open Perimetry Interface: An enabling tool for clinical visual psychophysics. Journal of Vision, 2012, 12(11):22, 21–25
OPI: the Open Perimetry Initiative
https://opi.lei.org.au/ and https://www.optocom.es/opi/
Generates a function that renders the average path of a nerve fiber bundle that exits through the optic nerve head (ONH) with a particular angle
cart2jpolar(coord) jpolar2cart(rpsi) bundlePath(psi0, r0 = 4) loc2psi(coord, r0 = 4) psi2oct(psi0, diam = 12) vf2gc(coord, angle = 0)cart2jpolar(coord) jpolar2cart(rpsi) bundlePath(psi0, r0 = 4) loc2psi(coord, r0 = 4) psi2oct(psi0, diam = 12) vf2gc(coord, angle = 0)
coord |
coordinates of locations in the visual field |
rpsi |
visual field locations in polar coordinates of the distorted space of the Jansonius map |
psi0 |
angle of incidence at the ONH |
r0 |
radius of the ONH. Its default value is |
diam |
diamater in degrees of visual angle of the OCT circular scan centered at the center of the ONH |
angle |
fovea-disc angle in degrees |
cart2jpolar converts the cartesian coordinates to the polar
coordinates in the distorted space used in the Jansonius map
jpolar2cart converts back from the Jansonius polar
coordinates to cartesian coordinates
bundlePath returns a function describing the expected fiber
path given an angle of incidence on the ONH
loc2psi returns the angle of incidence of the average bundle path
that passes through specific locations of the visual field
psi2oct returns the angle of OCT circular scans corresponding
to average bundle paths with specific angle of incidence at the ONH
vf2gc calculates ganglion-cell soma locations
cart2jpolar: returns the Jansonius modified polar coordinates
jpolar2cart: returns Cartesian coordinates
bundlePath: returns a function describing a retinal ganglion cell bundle path
loc2psi: returns the angle of incidence on the ONH
psi2oct: returns the corresponding angle in the OCT circular scan
vf2gc: returns the ganglion cell soma corresponding to the photoreceptors
of a visual field location
N. M. Jansonius, J. Nevalainen, B. Selig, L. M. Zangwill, P. A. Sample, W. M. Budde, J. B. Jonas, W. A. Lagreze, P. J. Airaksinen, R. Vonthein, L. A. Levin, J. Paetzold, and U. Schiefer. A mathematical description of nerve fiber bundle trajectories and their variability in the human retina. Vision Research, 49(17):2157-2163, 2009
N. M. Jansonius, J. Nevalainen, B. Selig, L. M. Zangwill, P. A. Sample, W. M. Budde, J. B. Jonas, W. A. Lagreze, P. J. Airaksinen, R. Vonthein, L. A. Levin, J. Paetzold, and U. Schiefer. Erratum to "A mathematical description of nerve fiber bundle trajectories and their variability in the human retina". Vision Research, 50:1501, 2010
N. M. Jansonius, J. Schiefer, J. Nevalainen, J. Paetzold, and U. Schiefer. A mathematical model for describing the retinal nerve fiber bundle trajectories in the human eye: Average course, variability, and influence of refraction, optic disc size and optic disc position. Experimental Eye Research, 105:70-78, 2012
N. Drasdo, C. L. Millican, C. R. Katholi, and C. A. Curcio. The length of Henle fibers in the human retina and a model of ganglion receptive field density in the visual field. Vision Research, 47:2901–2911, 2007
D. C. Hood, A. S. Raza, D. M. C. G. V., J. G. Odel, V. C. Greenstein, J. M. Liebmann, and R. Ritch. Initial arcuate defects within the central 10 degrees in glaucoma. Investigative Ophthalmology and Visual Science, 52(2):940-946, 2011
A. S. Raza, J. Cho, D. M. C. G. V., H. Wang, X. Zhang, R. H. Kardon, J. M. Liebmann, R. Ritch, and D. C. Hood. Retinal ganglion cell layer thickness and local visual field sensitivity in glaucoma. Archives of Ophthalmology, 129(12):1529-1536, 2011
G. Montesano, G. Ometto, R. E. Hogg, L. M. Rossetti, D. F. Garway-Heath, and D. P. Crabb. Revisiting the Drasdo Model: Implications for Structure-Function Analysis of the Macular Region. Translational Vision Science and Technology, 9(10):15, 2020
# get ganglion-cell soma locations from visual field locations vf2gc(locmaps$p10d2$coord) # convert to polar of the distorted space used by Jansonius map and back coord <- data.frame(x = c(3, 0, -3), y = c(0, 0, 0)) (rpsi <- cart2jpolar(coord)) jpolar2cart(rpsi) # get an average bundle path from a specific angle of incidence in the ONH # The object returned is a function that returns polar angles of the # distorted space of the Jansonius map for distances from the ONH center pathFun <- bundlePath(-125) jpolar2cart(data.frame(10:20, pathFun(10:20))) # get angle of incidence in the ONH from locations of the visual field loc2psi(coord) # get the OCT circular scan angles from the angle of incidence in the ONH # for the 10-2 map of locations, ... psi2oct(loc2psi(locmaps$p10d2$coord)) # the previous operation was actually fundamentally wrong! We need to # obtain first the psi2oct(loc2psi(vf2gc(locmaps$p10d2$coord)))# get ganglion-cell soma locations from visual field locations vf2gc(locmaps$p10d2$coord) # convert to polar of the distorted space used by Jansonius map and back coord <- data.frame(x = c(3, 0, -3), y = c(0, 0, 0)) (rpsi <- cart2jpolar(coord)) jpolar2cart(rpsi) # get an average bundle path from a specific angle of incidence in the ONH # The object returned is a function that returns polar angles of the # distorted space of the Jansonius map for distances from the ONH center pathFun <- bundlePath(-125) jpolar2cart(data.frame(10:20, pathFun(10:20))) # get angle of incidence in the ONH from locations of the visual field loc2psi(coord) # get the OCT circular scan angles from the angle of incidence in the ONH # for the 10-2 map of locations, ... psi2oct(loc2psi(locmaps$p10d2$coord)) # the previous operation was actually fundamentally wrong! We need to # obtain first the psi2oct(loc2psi(vf2gc(locmaps$p10d2$coord)))
It contains a first list with two LUTs for the X and Y displacement of ganglion cell bodies for arbitrary locations in the retina (in mm assuming 24 mm axial length). The other two elements of the list contain precomputed vectors of degrees and mm on the retina for the same schematic eye, used for conversions. These are used by the function vf2gc().
drasdolutdrasdolut
A large list containing
a list of four elements: xlut and ylut are 2d matrices containing X and Y ganglion cell positions for any given location. Xv and Yv are vectors defining the corresponding locations for the matrices along the X and Y axis.
A vector of degrees from the fovea, using a schematic eye. Corresponds to distances on the retina stored in MM
A vector of MM distance from the fovea, using a schematic eye. Corresponds to distances in degrees stored in Degs
G. Montesano, G. Ometto, R. E. Hogg, L. M. Rossetti, D. F. Garway-Heath, and D. P. Crabb. Revisiting the Drasdo Model: Implications for Structure-Function Analysis of the Macular Region. Translational Vision Science and Technology, 9(10):15, 2020
N. Drasdo, C. L. Millican, C. R. Katholi, and C. A. Curcio. The length of Henle fibers in the human retina and a model of ganglion receptive field density in the visual field. Vision Research, 47:2901–2911, 2007
Computes ages at specific dates
getage(dob, date)getage(dob, date)
dob |
date(s) of birth |
date |
date(s) for which to calculate age |
getage returns the age from the date of birth and a certain date
getage("1977-01-31", "2014-01-30")getage("1977-01-31", "2014-01-30")
Functions that compute global and pointwise linear regression analyses:
glr performs global linear regression analysis
plr performs pointwise linear regression (PLR) analysis
poplr performs PoPLR analysis as in O'Leary et al (see reference)
glr(g, testSlope = 0) plr(vf, testSlope = 0) poplr(vf, testSlope = 0, nperm = factorial(7), trunc = 1)glr(g, testSlope = 0) plr(vf, testSlope = 0) poplr(vf, testSlope = 0, nperm = factorial(7), trunc = 1)
g |
a data.frame with date on the first column and the value of the global index on the second column |
testSlope |
slope, or slopes, to test as null hypothesis. Default is 0.
if a single value, then the same null hypothesis is used for all locations.
If a vector of values, then (for |
vf |
visual fields sensitivity data |
nperm |
number of permutations. If the number of visits is 7 or less, then
|
trunc |
truncation value for the Truncated Product Method (see reference) |
poplr there is a small difference between this implementation of
PoPLR and that proposed by O'Leary et al. The combined S statistic in the
paper used a natural logarithm. Here we not only use a logarithm of base 10
but we also divide by the number of locations. This way the S statistic has
a more direct interpretation as the average number of leading zeros in the
p-values for pointwise (simple) linear regression. That is, if S = 2, then
the p-values have on average 2 leading zeros, if S = 3, then 3 leading zeros,
and so on
glr and plr return a list with the following
id patient ID
eye patient eye
testSlope slope for glr or list of slopes for plr
to test as null hypotheses
nvisits number of visits
years years from baseline. Used for the pointwise linear
regression analysis
data data analyzed. For glr, it is the values of the
global indes analyzed. For plr, each column is a location of the
visual field used for the analysis. Each row is a visit (as many as years)
pred predicted values. Each column is a location of the visual
field used for the analysis. Each row is a visit (as many as years)
sl slopes estimated at each location for pointwise (simple)
linear regression
int intercept estimated at each location for pointwise (simple)
linear regression
tval t-values obtained for the left-tailed-t-tests for the slopes
obtained in the pointwise (simple) linear regression at each location
pval p-values obtained for the left-tailed t-tests for the slopes
obtained
poplr returns a list with the following additional fields
csl the modified Fisher's S-statistic for the left-tailed permutation test
cslp the p-value for the left-tailed permutation test
csr the modifed Fisher's S-statistic for the right-tailed permutation test
csrp the p-value for the right-tailed permutation test
pstats a list with the poinwise slopes ('sl'), intercepts
('int'), standard errors ('se'), and p-values ('pval') obtained
for the series at each location analyzed and for all nperm permutations
(in 'permutations')
cstats a list with all combined stats:
csl, csr the combined Fisher S-statistics for the left- and right-tailed
permutation tests respectively
cslp, csrp the corresponding p-values for the permutation tests
cslall, csrall the combined Fisher S-statistics for all permutations
N. O'Leary, B. C. Chauhan, and P. H. Artes. Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR). Investigative Ophthalmology and Visual Science, 53, 2012
vf <- vffilter(vfpwgRetest24d2, id == 1) # select one patient res <- glr(getgl(vf)[,c("date", "tmd")]) # linear regression with mean deviation (MD) res <- plr(gettd(vf)) # pointwise linear regression (PLR) with TD values res <- poplr(gettd(vf)) # Permutation of PLR with TD valuesvf <- vffilter(vfpwgRetest24d2, id == 1) # select one patient res <- glr(getgl(vf)[,c("date", "tmd")]) # linear regression with mean deviation (MD) res <- plr(gettd(vf)) # pointwise linear regression (PLR) with TD values res <- poplr(gettd(vf)) # Permutation of PLR with TD values
It contains a list of normative values, including pointwise and
smoothed SUNY-IU normative reference values for 24-2 static automated perimetry
(sunyiu_24d2_pw and sunyiu_24d2) obtained with the dataset
vfctrSunyiu24d2
gparsgpars
See section Structure of graphical parameters in vfplot
Functions to load from commercial perimeters
loadhfaxml(file, type = "pwg", repeated = mean) loadhfadicom(file, type = "pwg", repeated = mean) loadoctopus(file, type = "pwg", repeated = mean, dateFormat = "%d.%m.%Y") loadhfaxmlbatch(file, repeated = mean) loadhfadicombatch(file, repeated = mean)loadhfaxml(file, type = "pwg", repeated = mean) loadhfadicom(file, type = "pwg", repeated = mean) loadoctopus(file, type = "pwg", repeated = mean, dateFormat = "%d.%m.%Y") loadhfaxmlbatch(file, repeated = mean) loadhfadicombatch(file, repeated = mean)
file |
name of the csv file exported by the eyesuite software |
type |
type of patient. It can be ' |
repeated |
function to apply if there are repeated values in a particular location |
dateFormat |
format to be used for date. Its default value is %d.%m.%Y |
The XML loader for the Humphrery Field Analyser (HFA) by Carl Zeiss Meditec
is essentially a XML parser that reads in the XML generated with the scientific
export license. The DICOMM loader is also a parser to read HFA data generated in a
DICOMM file. The loader for the Octopus perimeter by Haag-Streit is a csv reader
from files generated with the Eyesuite software. The parser also extracts information
on visual field pattern deviation values and normative values. The list that is returned
with the loadoctopus loader contains data frames which are structured with keys so that
redundancy is minimized (similar to a relational database). Detailed examples for
loadoctopus: https://huchzi.github.io/vf1/loadOctopus.html
Visual field data
List of common and some custom location maps, including the 24-2, 10-2, 30-2, 60-4, etc used the the HFA and Octopus, the 24-2 used by the Matrix (FDT), and others used in Swanson's and Wall's labs
locmapslocmaps
See section Structure of location maps in setlocmap
Functions to handle location maps, which are lists with x and y
coordinates and other importan information about the visual field test
locations. Check section Structure of location maps below for details
locread(file, name = "", desc = "", bs = numeric(), ...) locwrite(locmap, file, ...)locread(file, name = "", desc = "", bs = numeric(), ...) locwrite(locmap, file, ...)
file |
the name of the file which the data are to be read from |
name |
to give the location map |
desc |
brief description for the location map |
bs |
locations that should be excluded from statistical analysis because of their proximity to the blind spot |
... |
arguments to be passed to or from methods |
locmap |
location map from which to get coordinates to export as csv file |
locread reads a csv file with location map data
locwrite writes a csv file with location map data
locread a list with information about a location map
locwrite No return value
Each element in the list locmaps is a location map that
contains the following fields
name descriptive name
desc brief description
coord coordinates of the visual field locations
bs if not empty, the locations that ought to be removed
for statistical analysis due to their proximity to the blind spot
# write and read location map tf <- tempfile("locmap") locwrite(getlocmap(), file = tf) # save current locmap in a temp file print(locread(tf, name = "name", desc = "desc", bs = c(1, 2))) # read the temp file# write and read location map tf <- tempfile("locmap") locwrite(getlocmap(), file = tf) # save current locmap in a temp file print(locread(tf, name = "name", desc = "desc", bs = c(1, 2))) # read the temp file
It contains a list of normative values, including pointwise and
smoothed SUNY-IU normative reference values for 24-2 static automated perimetry
(sunyiu_24d2_pw and sunyiu_24d2) obtained with the dataset
vfctrSunyiu24d2
normvalsnormvals
See section Structure of normative values in setnv
H. J. Wyatt, M. W. Dul, and W. H. Swanson. Variability of visual field measurements is correlated with the gradient of visual sensitivity. Vision Research, 47, 2007.
A. Shafi, W. H. Swanson, and M. W. Dul. Structure and Function in Patients with Glaucomatous Defects Near Fixation. Optometry and Vision Science, 88, 2011.
Functions to generate and handle normative values. Check section
Structure of normative values below for details about how to generate
functioning normative values
nvgenerate( vf, method = "pointwise", probs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 0.98, 0.99, 0.995, 1), name = "", perimetry = "static automated perimetry", strategy = "", size = "", agem = agelm(vf), tdfun = tddef(agem), ghfun = ghdef(0.85), pdfun = pddef(ghfun) ) agelm(vf) tddef(agem) ghdef(perc = 0.85) pddef(ghfun = ghdef(0.85)) lutdef(vf, probs, type = "quantile", ...) gdef(agem, sdtd, sdpd) lutgdef(g, probs, type = "quantile", ...)nvgenerate( vf, method = "pointwise", probs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 0.98, 0.99, 0.995, 1), name = "", perimetry = "static automated perimetry", strategy = "", size = "", agem = agelm(vf), tdfun = tddef(agem), ghfun = ghdef(0.85), pdfun = pddef(ghfun) ) agelm(vf) tddef(agem) ghdef(perc = 0.85) pddef(ghfun = ghdef(0.85)) lutdef(vf, probs, type = "quantile", ...) gdef(agem, sdtd, sdpd) lutgdef(g, probs, type = "quantile", ...)
vf |
visual field data with sensitivity values |
method |
method to generate normative values, pointwise (' |
probs |
numeric vector of probabilities with values in |
name |
name for the normative values, e.g., "SUNY-IU pointwise NVs". Default is blank |
perimetry |
perimetry used to obtain normative data, e.g., "static automated perimetry" (default) |
strategy |
psychophysical strategy used to obtain threshold values, e.g., "SITA standard". Default is blank |
size |
stimulus size, if the same size was used for all visual field locations or empty (default) |
agem |
age model to construct the function to obtain TD values |
tdfun |
Function for the calculation of total deviation maps |
ghfun |
function used for determination of the GH and PD values |
pdfun |
Function for the calculation of pattern deviation maps |
perc |
the percentile to obtain the ranked TD value as
reference for the general height (GH) of the visual field.
Default is the 85th percentile, thus |
type |
type of estimation for the weighted quantile values. See
|
... |
arguments to be passed to or from methods |
sdtd |
standard deviations obtained for TD values |
sdpd |
standard deviations obtained for PD values |
g |
a table with global indices |
nvgenerate returns a list with normative values
agelm returns a list with coefficients and a function defining
a linear age model
tddef returns a function for the computation of TD values
ghdef returns a function for the computation of the general height
pddef returns a function for the computation of PD values
lutdef returns a look up table and a function for the
computation of the probability values for TD and PD
gdef returns a function to compute global indices
lutgdef returns a look up table and a function for the
computation of the probability values for global indices
This is one of the most complex structures in visualFields. It is necessary
to be able to run statistical analyses of visual fields obtained from
perimetry and it requires data from healthy eyes for its generation. The
normative values are only as good as the data they are generated from. Two
common ways to generate full normative values from a dataset of healthy eyes,
are provided in the package, depending on the method selected. The first
one, method="pointwise", generates normative values directly from
pointwise statistics. The second one, method="smooth", uses a 2D
quadratic functions to smooth out those pointwise statistics. Variations
or improvements can be regenerated by copying the code in those functions and
editing it.
info information regarding normative values. Info is not necessary
to carry out statistics, but is useful for the generation of reports. The
fields need not be the same as the ones listed here, although these are used
in the reports in vfsfa for single field analysis and
vfspa for series progression analysis.
name name of the normative values
perimetry perimetry device for which normative values are intended
strategy psychophysical strategy
size stimulus size, e.g. Goldmann size III, size V
agem The normative values' age model. The default methods' generate
age linear models with coefficients for each location in locmap in
coeff and the function definining the model in model
sd standard deviations of the sensitivities, s, total
deviation (TD) values, td, and pattern deviation (PD) values, pd
luts Lookup tables to obtain probability levels for TD and PD values.
probs probability levels
td, pd lookup tables for TD and PD values at each location
in locmaps
global lookup table for the following global visual field indices
ms mean sensitivity (MS) calculated as the unweithed average
over locations' values
ss standard deviation of sensitivity calculated as the
unweithed standard deviation over locations' values
md mean deviation (MD) calculated as the weithed average
over locations' values. Weights are the inverse of the standard
deviation in sd for TD at each location.
sd standard deviation of total deviation calculated as the
weithed standard deviation over locations' values. Weights are the inverse of the standard
deviation in sd for TD at each location.
pmd pattern mean deviation calculated as the weithed average
over locations' values. Weights are the inverse of the standard
deviation in sd for PD at each location.
psd pattern standard deviation calculated as the weithed
standard deviation over locations' values. Weights are the inverse
of the standard deviation in sd for PD at each location.
gh general height. This is defined traditionally for the 24-2
and the 30-2 as the approximatelly the 85th percentile of TD values
vfi the oddly defined visual field index
tdfun a function defining how to obtain the TD values. Typically, it
is a function of age and sensitivity values and it is defined as sensitivity
values minus the age-corrected mean normal obtained as defined in agem.
Thus, TD values are negative is visual field sensitivity values are below
mean normal and positive if they are above mean normal
ghfun a function defining how to obtain the general height
pdfun a function defining how to obtain the PD values. Tipically, they
are obtaines as the TD values minus the general height
glfun a function defining how to obtain different global indices
tdpfun, pdpfun, glpfun mapping functions to get
the probability levels corresponding to TD, PD and global indices values and
based on the lookup tables defined in luts
# generate normative values from SUNY-IU dataset of healthy eyes # pointwise sunyiu_24d2_pw <- nvgenerate(vfctrSunyiu24d2, method = "pointwise", name = "SUNY-IU pointwise NVs", perimetry = "static automated perimetry", strategy = "SITA standard", size = "Size III") # smooth sunyiu_24d2 <- nvgenerate(vfctrSunyiu24d2, method = "smooth", name = "SUNY-IU smoothed NVs", perimetry = "static automated perimetry", strategy = "SITA standard", size = "Size III")# generate normative values from SUNY-IU dataset of healthy eyes # pointwise sunyiu_24d2_pw <- nvgenerate(vfctrSunyiu24d2, method = "pointwise", name = "SUNY-IU pointwise NVs", perimetry = "static automated perimetry", strategy = "SITA standard", size = "Size III") # smooth sunyiu_24d2 <- nvgenerate(vfctrSunyiu24d2, method = "smooth", name = "SUNY-IU smoothed NVs", perimetry = "static automated perimetry", strategy = "SITA standard", size = "Size III")
Functions to set and get settings in the visualField environment
setdefaults() getnv() setnv(nv) getgpar() setgpar(gpar) getlocmap() setlocmap(locmap) getlocini() setlocini(locini = 11) getvfcols() .vfenvsetdefaults() getnv() setnv(nv) getgpar() setgpar(gpar) getlocmap() setlocmap(locmap) getlocini() setlocini(locini = 11) getvfcols() .vfenv
nv |
normative values to to set in the visualFields environment |
gpar |
structure with all graphical parameters |
locmap |
location map to to set in the visualFields environment |
locini |
column from where to start reading the visual field data |
An object of class environment of length 4.
setdefaults sets the default location map, normative value and
graphical parameters visualFields environment
setnv sets normative values in the visualFields environment
getnv gets current normative values from the visualFields
environment
setlocmap sets a location map in the visualFields environment
getlocmap gets the current location map from the visualFields
environment
setgpar sets graphical parameters in the visualFields environment
getgpar gets current graphical parameters from the visualFields
environment
setlocini sets the column where visual field data start in the
visualFields environment
getlocini gets the column where visual field data starts from
the visualFields environment
getlocini gets the column where visual field data starts from
the visualFields environment
getvfcols gets all the columns with visual field data
setdefaults: No return value
getnv: Returns the normative value currently in used by visualFields
setnv: No return value
getgpar: Returns the graphical parameters currently in used by visualFields
setgpar: No return value
getlocmap: Returns the location map currently in used by visualFields
setlocmap: No return value
getlocini: Returns the column where visual field data starts
setlocini: No return value
getvfcols: Returns the columns with visual field data
# get and set normative values getnv()$info$name # print name of set normative values setnv(normvals$iowa_PC26_pw_cps) # set pointwise normative values getnv()$info$name # print name of set normative values setdefaults() # return back to defaults # get and set a location map getlocmap()$name # name of set normative values setlocmap(locmaps$p30d2) # set the 30-2 location map getlocmap()$name # name of set normative values setdefaults() # return back to defaults # get and set a graphical parameters getgpar()$tess$xlim # limits of x axis setgpar(gpars$pPeri) # set graphical parameters for the Peripheral test getgpar()$tess$xlim # limits of x axis setdefaults() # return back to defaults # get and set initial column for visual field data getlocini() getvfcols() # get columns with visual fields data setlocini(15) getvfcols() # get columns with visual fields data setdefaults() # return back to defaults# get and set normative values getnv()$info$name # print name of set normative values setnv(normvals$iowa_PC26_pw_cps) # set pointwise normative values getnv()$info$name # print name of set normative values setdefaults() # return back to defaults # get and set a location map getlocmap()$name # name of set normative values setlocmap(locmaps$p30d2) # set the 30-2 location map getlocmap()$name # name of set normative values setdefaults() # return back to defaults # get and set a graphical parameters getgpar()$tess$xlim # limits of x axis setgpar(gpars$pPeri) # set graphical parameters for the Peripheral test getgpar()$tess$xlim # limits of x axis setdefaults() # return back to defaults # get and set initial column for visual field data getlocini() getvfcols() # get columns with visual fields data setlocini(15) getvfcols() # get columns with visual fields data setdefaults() # return back to defaults
vfaggregate computes summary statistics of visual field data
vfmean computes the mean statistics of visual field data. It is
a wrapper for vfaggregate but only to compute means
vfretestdist computes the conditional distribution from test-retest data
vfaggregate(vf, by = "date", fun = mean, ...) vfmean(vf, by = "date", ...) vfretestdist(vf, nbase = 1, nfollow = 1, alpha = 0.1, ...)vfaggregate(vf, by = "date", fun = mean, ...) vfmean(vf, by = "date", ...) vfretestdist(vf, nbase = 1, nfollow = 1, alpha = 0.1, ...)
vf |
a table with visual fields data. Data is rounded, which leaves sensitivity data unchanged, but it is necessary for the nature of the algorithm if the data passed are TD or PD values or summary stats such as averages. Beware of the locations in the blind spot, which very likely need to be removed |
by |
aggregate by |
fun |
a function to compute the summary statistics which can be applied to
all data subsets. The default is ' |
... |
arguments to be passed to or from methods. A useful one to try
is type of quantile calculation ' |
nbase |
number of visual fields to be used as baseline |
nfollow |
number of visual fields to be used as follow up |
alpha |
significance level to derive the conditional retest intervals.
Default value is |
vfaggregate this is a restricted version of aggregate
that only allows to use part of the key hierarchically, and operates on all
data frames of the VisualField object. The restriction is that only
aggregates that are allowed are 'newkey = c("id", "eye")' and
'newkey = c("id", "eye", "date")'. It returns the aggregated value for all
numeric columns grouped and ordered by the new key (id and eye, or id, eye,
and date). If the aggregate grouping is by eye and the function, then
the date returned is the average.
vfaggregate and vfmean return a vf data frame with aggregate values
vfretestdist returns a list with the following elements:
x with all the test values (x-axis)
y the distribution of retest dB values conditional to each
test value in x. It is a list with as many entries as x
n number of retest values conditional to each value in x.
It is a list with as many entries as x
ymed median for each value in x. It is a list with as
many entries as x
ylow quantile value for significance 1 - alpha / 2
for each value in x. It is a list with as many entries as x
yup quantile value for significance alpha / 2
for each value in x. It is a list with as many entries as x
Together ylow and yup represent the lower and upper limit of the
(1 - alpha)% confidence intervals at each value x.
# aggregate by date vfaggregate(vfpwgRetest24d2, by = "date") # compute the mean vfaggregate(vfpwgRetest24d2, by = "date", fun = sd) # compute standard deviation # aggregate by eye vfaggregate(vfpwgRetest24d2, by = "eye") # compute the mean vfaggregate(vfpwgRetest24d2, by = "eye", fun = sd) # compute standard deviation # mean by date vfmean(vfpwgRetest24d2, by = "date") # mean by eye vfmean(vfpwgRetest24d2, by = "eye") # get the retest sensitivity data after removing the blind spot retest <- vfretestdist(vfpwgRetest24d2, nbase = 1, nfollow = 1) plot(0, 0, typ = "n", xlim = c(0, 40), ylim = c(0,40), xlab = "test in dB", ylab = "retest in dB", asp = 1) for(i in 1:length(retest$x)) { points(rep(retest$x[i], length(retest$y[[i]])), retest$y[[i]], pch = 20, col = "lightgray", cex = 0.75) } lines(c(0,40), c(0,40), col = "black") lines(retest$x, retest$ymed, col = "red") lines(retest$x, retest$ylow, col = "red", lty = 2) lines(retest$x, retest$yup, col = "red", lty = 2)# aggregate by date vfaggregate(vfpwgRetest24d2, by = "date") # compute the mean vfaggregate(vfpwgRetest24d2, by = "date", fun = sd) # compute standard deviation # aggregate by eye vfaggregate(vfpwgRetest24d2, by = "eye") # compute the mean vfaggregate(vfpwgRetest24d2, by = "eye", fun = sd) # compute standard deviation # mean by date vfmean(vfpwgRetest24d2, by = "date") # mean by eye vfmean(vfpwgRetest24d2, by = "eye") # get the retest sensitivity data after removing the blind spot retest <- vfretestdist(vfpwgRetest24d2, nbase = 1, nfollow = 1) plot(0, 0, typ = "n", xlim = c(0, 40), ylim = c(0,40), xlab = "test in dB", ylab = "retest in dB", asp = 1) for(i in 1:length(retest$x)) { points(rep(retest$x[i], length(retest$y[[i]])), retest$y[[i]], pch = 20, col = "lightgray", cex = 0.75) } lines(c(0,40), c(0,40), col = "black") lines(retest$x, retest$ymed, col = "red") lines(retest$x, retest$ylow, col = "red", lty = 2) lines(retest$x, retest$yup, col = "red", lty = 2)
Locations of the visual field tested have eccentricities up to 26 degrees and were obtained with a custom static automated perimetry. Data are from 98 eyes of 98 ocular healthy subjects. Each subject underwent two visual field tests, one of the central visual field (64 locations within 26 degrees of fixation) and one of the peripheral visual field (64 locations with eccentricity from 26 to up to 81 degrees)
vfctrIowaPC26vfctrIowaPC26
See section Structure of visual fields data in vfdesc
Data are for locations within the central 26 degrees. The data for locations
with eccentricity from 26 to up to 81 degrees are in vfctrIowaPeri.
This dataset of healthy eyes was used to generate the normative values
iowa_PC26_pw, and iowa_PC26_pw_cps included in normvals.
I. Marin-Franch, P. H. Artes, L. X. Chong, A. Turpin, and M. Wall. Data obtained with an open-source static automated perimetry test of the full visual field in healthy adults. Data in Brief, 21:75–82, 2018.
vfpwgSunyiu24d2, vfctrIowaPeri,
vfctrSunyiu10d2, vfctrSunyiu24d2,
vfpwgRetest24d2
Locations of the visual field tested have eccentricities from 26 to up to 81 degrees and were obtained with a custom static automated perimetry. Data are from 98 eyes of 98 ocular healthy subjects. Each subject underwent two visual field tests, one of the central visual field (64 locations within 26 degrees of fixation) and one of the peripheral visual field (64 locations with eccentricity from 26 to up to 81 degrees)
vfctrIowaPerivfctrIowaPeri
See section Structure of visual fields data in vfdesc
Data are for locations with eccentricity from 26 to up to 81 degrees.
The dataset for locations within the central 26 degrees are in vfctrIowaPC26.
This dataset of healthy eyes was used to generate the normative values
iowa_Peri_pw, and iowa_Peri_pw_cps included in normvals.
I. Marin-Franch, P. H. Artes, L. X. Chong, A. Turpin, and M. Wall. Data obtained with an open-source static automated perimetry test of the full visual field in healthy adults. Data in Brief, 21:75–82, 2018.
vfpwgSunyiu24d2, vfctrIowaPC26,
vfctrSunyiu10d2, vfctrSunyiu24d2,
vfpwgRetest24d2
SUNY-IU dataset of healthy eyes for 10-2 static automated perimetry. Courtesy of William H Swanson.
vfctrSunyiu10d2vfctrSunyiu10d2
See section Structure of visual fields data in vfdesc
H. J. Wyatt, M. W. Dul, and W. H. Swanson. Variability of visual field measurements is correlated with the gradient of visual sensitivity. Vision Research, 47, 2007.
A. Shafi, W. H. Swanson, and M. W. Dul. Structure and Function in Patients with Glaucomatous Defects Near Fixation. Optometry and Vision Science, 88, 2011.
vfpwgSunyiu24d2, vfctrIowaPC26,
vfctrIowaPeri, vfctrSunyiu24d2,
vfpwgRetest24d2
This dataset of healthy eyes was used to generate the normative values
sunyiu_24d2, sunyiu_24d2_pw, sunyiu_24d2, and
sunyiu_24d2_pw_cps included in normvals.
Courtesy of William H Swanson and Mitch W Dul
vfctrSunyiu24d2vfctrSunyiu24d2
See section Structure of visual fields data in vfdesc
H. J. Wyatt, M. W. Dul, and W. H. Swanson. Variability of visual field measurements is correlated with the gradient of visual sensitivity. Vision Research, 47, 2007.
A. Shafi, W. H. Swanson, and M. W. Dul. Structure and Function in Patients with Glaucomatous Defects Near Fixation. Optometry and Vision Science, 88, 2011.
vfpwgSunyiu24d2, vfctrIowaPC26,
vfctrIowaPeri, vfctrSunyiu10d2,
vfpwgRetest24d2
The main object of the visualFields package is a table with
a specific format and fields that are mandatory for their management and
processing (mainly statistical analysis). Each record (row) in the table
contains data for a single visual field test. The mandatory fields specify
subject (by its ID code), eye, and test date and time. There are required
fields statistical and reliability analyses (e.g., age for the determination
of total-deviation and pattern-deviation values, and for global indices and
fpr, fnr, fl for the proportion of false positives, false negative, and
fixation losses). The rest of mandatory fields are sensitivity or deviation
data for each visual field test location. (The number of fields for
tested locations varies with the location map, 54 for the 24-2, 76 for the
30-2, 68 for the 10-2, etc.). Check section Structure of visual fields data
below for details about the required structure of the table contatining the
visual fields datasets.
The following functions carry out analysis on visual fields data:
vfdesc descriptive summary of a visual field dataset
vfsort sort visual field data
vfisvalid check if a table with visual field data is properly
formatted and valid for analysis
vfread read a csv file with visual field data
vfwrite write a csv file with visual field data
vfjoin joins two visual field datasets
vffilter filters elements from a visual field dataset with
matching conditions. This function is just a wrapper for dplyr's
function filter
vfselect select visual field data by index or the first or last
n visits per subject and eye
gettd computes total-deviation (TD) values and probability values
gettdp computes total-deviation (TD) probability values
getpd computes pattern-deviation (PD) values
getpdp computes pattern-deviation (PD) probability values
getgh computes the general height (GH) from the TD tables
getgl computes visual fields global indices
getglp computes computes visual fields global indices probability values
vfdesc(vf) vfsort(vf, decreasing = FALSE) vfisvalid(vf) vfread(file, dateformat = "%Y-%m-%d", eyecodes = c("OD", "OS", "OU"), ...) vfwrite( vf, file, dateformat = "%Y-%m-%d", eyecodes = c("OD", "OS", "OU"), ... ) vfjoin(vf1, vf2) vffilter(vf, ...) vfselect(vf, sel = "last", n = 1) gettd(vf) gettdp(td) getpd(td) getpdp(pd) getgh(td) getgl(vf) getglp(g)vfdesc(vf) vfsort(vf, decreasing = FALSE) vfisvalid(vf) vfread(file, dateformat = "%Y-%m-%d", eyecodes = c("OD", "OS", "OU"), ...) vfwrite( vf, file, dateformat = "%Y-%m-%d", eyecodes = c("OD", "OS", "OU"), ... ) vfjoin(vf1, vf2) vffilter(vf, ...) vfselect(vf, sel = "last", n = 1) gettd(vf) gettdp(td) getpd(td) getpdp(pd) getgh(td) getgl(vf) getglp(g)
vf |
visual field data |
decreasing |
sort decreasing or increasing?
Default is increasing, that is |
file |
the name of the csv file where to write the data |
dateformat |
format to be used for date. Its default value
is |
eyecodes |
codification for right and left eye, respectively.
By default in visualField uses ' |
... |
arguments to be passed to or from methods |
vf1, vf2
|
the two visual field data objects to join or merge |
sel |
it can be two things, an array of indices to select from visual field data
or a string with the values ' |
n |
number of visits to select. Default value is 1, but it is ignored if
|
td |
total-deviation (TD) values |
pd |
pattern-deviation (PD) values |
g |
global indices |
vfselect when selecting the last or first few visual fields per
subject and eye, if that subject and eye has fewer than n visits,
then all visits are returned
vfdesc returns descriptive statistics of a visual field dataset
vfsort returns a sorted visual field dataset
vfisvalid returns TRUE or FALSE
vfread returns a visual field dataset
vfwrite No return value
vfjoin returns a visual field dataset
vffilter returns a visual field dataset
vfselect returns a visual field dataset
gettd returns a visual field dataset with total deviation values
gettdp returns a visual field dataset with total deviation probability values
getpd returns a visual field dataset with pattern deviation values
getpdp returns a visual field dataset with pattern deviation probability values
getgh returns the general height of visual fields tests
getgl returns visual fields global indices
getglp returns probability values of visual fields global indices
Visual fields data is the central object used in visualFields. It is a table of visual field data collected with the same perimeter, background and stimulus paradigm (e.g., static automated perimetry or frequency-doubling perimetry), stimulus size (e.g., Goldmann size III), grid of visual field test locations (e.g., 24-2), and psychophysical testing strategy (e.g., SITA standard). Normative values can be obtained from appropriate datasets with data for healthy eyes and these normative values can then be used to generate statistical analyses and visualizations of data for patients with retinal or visual anomalies.
Each record correspond to a specific test for an eye of a subject taken on a specific date at a specific time. Visual field data must have the following columns
id an id uniquely identifying a subject. This field is mandatory
eye should be "OD" for right eye or "OS" for left eye. This field is
mandatory
date test date. This field is mandatory
time test time. This field is mandatory
age age of the patient on the test date. This field is required to
obtain total-deviation, pattern-deviation values, and other age-dependent
local and global indices
type type of subject, Could be a healthy subject (ctr for control)
or a patient with glaucoma (pwg) or a patient with idiopatic intraocular
hypertension (iih) or other. This field is no required for management or
statistical analysis.
fpr false positive rate. This field is no required for management or
statistical analysis.
fnr false negative rate. This field is no required for management or
statistical analysis.
fl fixation losses. This field is no required for management or
statistical analysis.
l1..ln sensitivity, total-deviation, or pattern-deviation values for
each location. For analysis with visualFields there should be as many columns
as coordinates in the location map set in the visualFields environment. These
fields are mandatory.
# get dataset description from visual field table vfdesc(vfctrSunyiu24d2) # sort dataset vfsort(vfctrSunyiu24d2[c(5, 4, 10, 50, 30),]) # check if a visualField is valid vf <- vfctrSunyiu24d2 vfisvalid(vf) # valid visual field data vf$id[5] <- NA vfisvalid(vf) # invalid visual field data # write and read visual field data vf <- vfctrSunyiu24d2 tf <- tempfile("vf") vfwrite(vf, file = tf) # save current locmap in a temp file head(vfread(tf)) # read the temp file # join visual fields datasets vfjoin(vfctrSunyiu24d2, vfpwgRetest24d2) # visual field subselection vffilter(vf, id == 1) # fields corresponding to a single subject vffilter(vf, id == 1 & eye == "OD") # fields for a single subject's right eye unique(vffilter(vf, eye == "OS")$eye) # only left eyes vffilter(vfjoin(vfctrSunyiu24d2, vfpwgRetest24d2), type == "ctr") # get only controls vffilter(vfjoin(vfctrSunyiu24d2, vfpwgRetest24d2), type == "pwg") # get only patients # select visual fields by index vfselect(vfctrSunyiu24d2, sel = c(1:4, 150)) # select last few visual fields per subject and eye vfselect(vfpwgRetest24d2, sel = "last") # select first few visual fields per subject and eye vfselect(vfpwgRetest24d2, sel = "first") vfselect(vfpwgRetest24d2, sel = "first", n = 5) # get the last 5 visits # compute visual field statistics vf <- vfpwgSunyiu24d2 td <- gettd(vf) # get TD values tdp <- gettdp(td) # get TD probability values pd <- getpd(td) # get PD values pdp <- getpdp(pd) # get PD probability values gh <- getgh(td) # get the general height g <- getgl(vf) # get global indices gp <- getglp(g) # get global indices probability values# get dataset description from visual field table vfdesc(vfctrSunyiu24d2) # sort dataset vfsort(vfctrSunyiu24d2[c(5, 4, 10, 50, 30),]) # check if a visualField is valid vf <- vfctrSunyiu24d2 vfisvalid(vf) # valid visual field data vf$id[5] <- NA vfisvalid(vf) # invalid visual field data # write and read visual field data vf <- vfctrSunyiu24d2 tf <- tempfile("vf") vfwrite(vf, file = tf) # save current locmap in a temp file head(vfread(tf)) # read the temp file # join visual fields datasets vfjoin(vfctrSunyiu24d2, vfpwgRetest24d2) # visual field subselection vffilter(vf, id == 1) # fields corresponding to a single subject vffilter(vf, id == 1 & eye == "OD") # fields for a single subject's right eye unique(vffilter(vf, eye == "OS")$eye) # only left eyes vffilter(vfjoin(vfctrSunyiu24d2, vfpwgRetest24d2), type == "ctr") # get only controls vffilter(vfjoin(vfctrSunyiu24d2, vfpwgRetest24d2), type == "pwg") # get only patients # select visual fields by index vfselect(vfctrSunyiu24d2, sel = c(1:4, 150)) # select last few visual fields per subject and eye vfselect(vfpwgRetest24d2, sel = "last") # select first few visual fields per subject and eye vfselect(vfpwgRetest24d2, sel = "first") vfselect(vfpwgRetest24d2, sel = "first", n = 5) # get the last 5 visits # compute visual field statistics vf <- vfpwgSunyiu24d2 td <- gettd(vf) # get TD values tdp <- gettdp(td) # get TD probability values pd <- getpd(td) # get PD values pdp <- getpdp(pd) # get PD probability values gh <- getgh(td) # get the general height g <- getgl(vf) # get global indices gp <- getglp(g) # get global indices probability values
Graphical tools for visualization and statistical analysis of visual fields.
vfgpar( coord, tess = vftess(coord), probs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 0.98, 0.99, 0.995, 1), cols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", colorRampPalette(c("#00FF00", "#008000"))(4)), floor = 0, ltprobs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 1), ltcols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", "#008000"), gtprobs = c(0, 0.05, 0.95, 0.98, 0.99, 0.995, 1), gtcols = c("#000000", "#FF0000", "#F7F0EB", colorRampPalette(c("#00FF00", "#008000"))(4)), neprobs = c(0, 0.0025, 0.005, 0.01, 0.25, 0.975, 0.99, 0.995, 0.9975, 1), necols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", colorRampPalette(c("#FFFF00", "#FF0000"))(4)), bprobs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 0.98, 0.99, 0.995, 1), bcols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", colorRampPalette(c("#00FF00", "#008000"))(4)) ) vftess(coord, floor = 0, delta = 3) vfcolscheme( probs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 0.98, 0.99, 0.995, 1), cols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", colorRampPalette(c("#00FF00", "#008000"))(4)), floor = 0 ) vfprogcolscheme( probs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 1), cols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", "#008000") ) vfplot(vf, td = NULL, tdp = NULL, pd = NULL, pdp = NULL, type = "td", ...) vfplotplr( vf, alternative = "LT", xoffs = 0, yoffs = 0, addSpark = FALSE, thr = 2, width = 4, height = 2, ... ) vflegoplot( vf, type = "td", grp = 3, addSpark = FALSE, thr = 2, width = 4, height = 2, ... ) vfsparklines(vf, thr = 2, width = 4, height = 2, add = FALSE, ...)vfgpar( coord, tess = vftess(coord), probs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 0.98, 0.99, 0.995, 1), cols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", colorRampPalette(c("#00FF00", "#008000"))(4)), floor = 0, ltprobs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 1), ltcols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", "#008000"), gtprobs = c(0, 0.05, 0.95, 0.98, 0.99, 0.995, 1), gtcols = c("#000000", "#FF0000", "#F7F0EB", colorRampPalette(c("#00FF00", "#008000"))(4)), neprobs = c(0, 0.0025, 0.005, 0.01, 0.25, 0.975, 0.99, 0.995, 0.9975, 1), necols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", colorRampPalette(c("#FFFF00", "#FF0000"))(4)), bprobs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 0.98, 0.99, 0.995, 1), bcols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", colorRampPalette(c("#00FF00", "#008000"))(4)) ) vftess(coord, floor = 0, delta = 3) vfcolscheme( probs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 0.98, 0.99, 0.995, 1), cols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", colorRampPalette(c("#00FF00", "#008000"))(4)), floor = 0 ) vfprogcolscheme( probs = c(0, 0.005, 0.01, 0.02, 0.05, 0.95, 1), cols = c("#000000", colorRampPalette(c("#FF0000", "#FFFF00"))(4), "#F7F0EB", "#008000") ) vfplot(vf, td = NULL, tdp = NULL, pd = NULL, pdp = NULL, type = "td", ...) vfplotplr( vf, alternative = "LT", xoffs = 0, yoffs = 0, addSpark = FALSE, thr = 2, width = 4, height = 2, ... ) vflegoplot( vf, type = "td", grp = 3, addSpark = FALSE, thr = 2, width = 4, height = 2, ... ) vfsparklines(vf, thr = 2, width = 4, height = 2, add = FALSE, ...)
coord |
print x and y coordinates. Check section
|
tess |
tesselation for the visual field maps. Check section
|
probs |
probability scale to use for TD and PD values. It is a numeric vector of probabilities with values in [0,1]. The values 0 and 1 must be included. Although not technically necessary, it would be best if it is the same as for the normative values used |
cols |
corresponding colors for each of the probability levels |
floor |
Flooring value, typically in dB. Default is 0 |
ltprobs, ltcols
|
color map for progression with the alternative hypothesis lower than (LT) |
gtprobs, gtcols
|
color map for progression with the alternative hypothesis lower than (GT) |
neprobs, necols
|
color map for progression with the alternative hypothesis not equal (NE) |
bprobs, bcols
|
color map for progression with blth alternative hypotheses LT and GT (B for both) |
delta |
Distance over which the boundary should be shifted. See for |
vf |
the visual fields data to plot |
td |
the total deviation values. If |
tdp |
the total deviation probability values. If |
pd |
the pattern deviation values. If |
pdp |
the pattern deviation probability values. If |
type |
the type of data to plot: sensitivities (' |
... |
other graphical arguments. See |
alternative |
alternative hypothesis used in progression analyses.
Allowed values are ' |
xoffs, yoffs
|
offset x and y where to print the slope values. That is, the distance from the center of each Voronoy polygons in degrees of visual angle |
addSpark |
whether to overlay a sparkline graph in each visual field location.
The parameters |
thr |
threshold used for the median absolute deviation of residuals
from simple linear regression. If greater than the threshold, the
sparkline for that location is plotted in red and with a thicker line.
Default is ' |
width |
the width of each pointwise sparkline plot. Default is
' |
height |
the height of each pointwise sparkline plot. Default is
' |
grp |
number of baseline (first) and last visual fields to group.
Default is ' |
add |
whether to generate a new plot (' |
The following functions generate plots using visual fields data
vfgpar generates simple graphical parameters
vftess generates a structure to handle the visual field tessellation.
Check section Tesselation in visualFields below for further details
vfcolscheme generates the structures to handle the color scheme
Check section Color schemes in visualFields below for further details
vfprogcolscheme generates the structures to handle the color scheme
for progression analysis. Check section Color schemes in visualFields
below for further details
vfplot plots a single test for visual field data
vfplotsens plots a single test for visual field sensitivity data
with a grayscale where darker means greater sensitivity loss
vfplotdev plots a single test for visual field total or pattern
deviation data with probability scales represented in color
vfplotplr plots the results of pointwise linear regression for
a series of visual fields for an eye from a subject
vflegoplot the legoplot shows the differences between the average
values of visual field tests taken as baseline and those at the end of
follow up
vflegoplotsens the legoplot for visual field sensitivity data with
a grayscale where darker means greater sensitivity loss
vflegoplotdev the legoplot for visual field total or pattern
deviation data with probability scales represented in color
vfsparklines the sparklines graph shows spark lines for the series
of visual field sensitivities, or total or pattern deviation data for each
location
vfgpar returns a list with graphical parameters to be used for vfplots
vftess returns a list with the xlim, ylim, tessellation tiles and an outer hull
to be used for vfplots
vfcolscheme returns a list with a lookup table and a function that define the color scheme
to be used for vfplots
vfprogcolscheme returns the default vfcolscheme to be used for vfplots
vfplot No return value
vfplotplr No return value
vflegoplot No return value
vfsparklines No return value
Graphical parameters for visualFields must be a list containing
coord print x and y coordinates. They could be different from the
the real visual field location testing coordinates in complex visual field
grids to help readability and improve visualization of statistical results
tess tesselation for the visual field maps. Check section
Tesselation in visualFields
colmap color map representing the probability scale. Check section
Color schemes in visualFields
A default graphical parameters can be generated with generategpar
A tesselation in visualFields must be defined with a list containing
xlim,
ylim 2-dimensional vectors containing the minimum
and maximum x and y values
floor the value to be assinged to any sensitivity value lower than
floor
tiles a list of as many tiles defining the tesselation as visual field
test locations. Each element of the list is a table with x and y coordinates defining
a polygon containing the corresponding test location. Each polygon is thus the tile
for each visual field test location
hull a table with x and y coordinates defining the outer hull of the
tessellation
A default tessellation can be generated with vftess
A color scheme in visualFields must be defined with a list containing
map a table mapping probabilities levels with colors defined
in hexadecimal base
fun a function that takes sensitivity values and deviation
probability levels and returns the corresponding color code.
A default color scheme can be generated with vfcolscheme
# generate a structure with default graphical parameters for the 30-2 map vfgpar(locmaps$p30d2$coord) # generate a structure with default tesselation for the 30-2 map vftess(locmaps$p30d2$coord) # default color scheme vfcolscheme() # default color scheme for progression vfprogcolscheme() # plot visual field values for the last field in the series for the first # subject in the dataset vfpwgSunyiu24d2 # grayscale with sensitivity values vfplot(vfselect(vffilter(vfpwgRetest24d2, id == 1), n = 1), type = "s") # TD values vfplot(vfselect(vffilter(vfpwgRetest24d2, id == 1), n = 1), type = "td") # PD values vfplot(vfselect(vffilter(vfpwgRetest24d2, id == 1), n = 1), type = "pd") # hybrid sensitivities and TD values vfplot(vfselect(vffilter(vfpwgRetest24d2, id == 1), n = 1), type = "tds") # hybrid sensitivities and PD values vfplot(vfselect(vffilter(vfpwgRetest24d2, id == 1), n = 1), type = "pds") # plot results from pointwise linear regression for the series of # visual fields for the right eye in the dataset vfpwgSunyiu24d2 # with sensitivity values vfplotplr(vffilter(vfpwgSunyiu24d2, eye == "OD")) # TD values vfplotplr(gettd(vffilter(vfpwgSunyiu24d2, eye == "OD"))) # PD values vfplotplr(getpd(gettd(vffilter(vfpwgSunyiu24d2, eye == "OD")))) # legoplot for the series of visual fields for the right eye # of the subject in the dataset vfpwgSunyiu24d2 # with sensitivity values vflegoplot(vffilter(vfpwgSunyiu24d2, eye == "OD"), type = "s") # TD values vflegoplot(vffilter(vfpwgSunyiu24d2, eye == "OD"), type = "td") # PD values vflegoplot(vffilter(vfpwgSunyiu24d2, eye == "OD"), type = "pd") # sparklines for the series of visual fields for the right eye of # the subject in the dataset vfpwgSunyiu24d2 # with sensitivity values vfsparklines(vffilter(vfpwgSunyiu24d2, eye == "OD")) # TD values vfsparklines(gettd(vffilter(vfpwgSunyiu24d2, eye == "OD"))) # PD values vfsparklines(getpd(gettd(vffilter(vfpwgSunyiu24d2, eye == "OD"))))# generate a structure with default graphical parameters for the 30-2 map vfgpar(locmaps$p30d2$coord) # generate a structure with default tesselation for the 30-2 map vftess(locmaps$p30d2$coord) # default color scheme vfcolscheme() # default color scheme for progression vfprogcolscheme() # plot visual field values for the last field in the series for the first # subject in the dataset vfpwgSunyiu24d2 # grayscale with sensitivity values vfplot(vfselect(vffilter(vfpwgRetest24d2, id == 1), n = 1), type = "s") # TD values vfplot(vfselect(vffilter(vfpwgRetest24d2, id == 1), n = 1), type = "td") # PD values vfplot(vfselect(vffilter(vfpwgRetest24d2, id == 1), n = 1), type = "pd") # hybrid sensitivities and TD values vfplot(vfselect(vffilter(vfpwgRetest24d2, id == 1), n = 1), type = "tds") # hybrid sensitivities and PD values vfplot(vfselect(vffilter(vfpwgRetest24d2, id == 1), n = 1), type = "pds") # plot results from pointwise linear regression for the series of # visual fields for the right eye in the dataset vfpwgSunyiu24d2 # with sensitivity values vfplotplr(vffilter(vfpwgSunyiu24d2, eye == "OD")) # TD values vfplotplr(gettd(vffilter(vfpwgSunyiu24d2, eye == "OD"))) # PD values vfplotplr(getpd(gettd(vffilter(vfpwgSunyiu24d2, eye == "OD")))) # legoplot for the series of visual fields for the right eye # of the subject in the dataset vfpwgSunyiu24d2 # with sensitivity values vflegoplot(vffilter(vfpwgSunyiu24d2, eye == "OD"), type = "s") # TD values vflegoplot(vffilter(vfpwgSunyiu24d2, eye == "OD"), type = "td") # PD values vflegoplot(vffilter(vfpwgSunyiu24d2, eye == "OD"), type = "pd") # sparklines for the series of visual fields for the right eye of # the subject in the dataset vfpwgSunyiu24d2 # with sensitivity values vfsparklines(vffilter(vfpwgSunyiu24d2, eye == "OD")) # TD values vfsparklines(gettd(vffilter(vfpwgSunyiu24d2, eye == "OD"))) # PD values vfsparklines(getpd(gettd(vffilter(vfpwgSunyiu24d2, eye == "OD"))))
Thirty patients recruited from the glaucoma clinics at the Queen Elizabeth Health Sciences Centre in Halifax, Nova Scotia. Each patient underwent 12 visual fields in 12 consecutive weekly sessions.
vfpwgRetest24d2vfpwgRetest24d2
See section Structure of visual fields data in vfdesc
P. H. Artes, N. O'Leary, M. T. Nicolela, B. C. Chauhan, and D. P. Crabb. Visual field progression in glaucoma: What is the specificity of the guided progression analysis? American Academy of Ophthalmology, 121(10):2023-2027, 2014.
vfpwgSunyiu24d2, vfctrIowaPC26,
vfctrIowaPeri, vfctrSunyiu10d2,
vfctrSunyiu24d2
This is real data for the right and left eyes, but the age has been changed to protect anonymity of the subject. Courtesy of William H Swanson and Mitch W Dul
vfpwgSunyiu24d2vfpwgSunyiu24d2
See section Structure of visual fields data in vfdesc
vfctrIowaPC26, vfctrIowaPeri,
vfctrSunyiu10d2, vfctrSunyiu24d2,
vfpwgRetest24d2
Generates of one-page reports of single field analyses
vfsfa(vf, td = NULL, tdp = NULL, pd = NULL, pdp = NULL, file, ...) vfsfashiny(vf, ...)vfsfa(vf, td = NULL, tdp = NULL, pd = NULL, pdp = NULL, file, ...) vfsfashiny(vf, ...)
vf |
visual field data |
td |
the total deviation values. If |
tdp |
the total deviation probability values. If |
pd |
the pattern deviation values. If |
pdp |
the pattern deviation probability values. If |
file |
The pdf file name where to save the one-page reports of single field analysis |
... |
other graphical arguments |
vfsfa saves a pdf with one-page reports of single field analyses
vfsfashiny generates interactive one-page reports of single field
analyses based on Shiny
No return value
Generation of one-page reports of series progression analyses
vfspa saves a pdf with one-page reports of series progression analyses
vfspashiny generates interactive one-page reports of series progression
analyses based on Shiny
vfspa( vf, file, type = "td", nperm = factorial(7), trunc = 1, testSlope = 0, ... ) vfspashiny( vf, type = "td", nperm = factorial(7), trunc = 1, testSlope = 0, ... )vfspa( vf, file, type = "td", nperm = factorial(7), trunc = 1, testSlope = 0, ... ) vfspashiny( vf, type = "td", nperm = factorial(7), trunc = 1, testSlope = 0, ... )
vf |
visual field data |
file |
The pdf file name where to save the one-page reports of single field analysis |
type |
Type of data to use. It can be ' |
nperm |
Number of permutations. Default is 7! |
trunc |
value for the Truncated Product Method (see reference). Default is 1 |
testSlope |
slope, or slopes, to test as null hypothesis. Default is 0.
if a single value, then the same null hypothesis is used for all locations.
If a vector of values, then (for |
... |
other graphical arguments |
No return value
N. O'Leary, B. C. Chauhan, and P. H. Artes. Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR). Investigative Ophthalmology and Visual Science, 53, 2012
N. O'Leary, B. C. Chauhan, and P. H. Artes. Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR). Investigative Ophthalmology and Visual Science, 53, 2012