Type: Package
Title: Visual Field (VF) Progression Analysis and Plotting Methods
Version: 0.7.1
Date: 2019-05-21
Author: Tobias Elze, Dian Li (documentation), Eun Young Choi (QC)
Maintainer: Dian Li <lidian@zju.edu.cn>
Description: Realization of published methods to analyze visual field (VF) progression. Introduction to the plotting methods (designed by author TE) for VF output visualization. A sample dataset for two eyes, each with 10 follow-ups is included. The VF analysis methods could be found in – Musch et al. (1999) <doi:10.1016/S0161-6420(99)90147-1>, Nouri-Mahdavi et at. (2012) <doi:10.1167/iovs.11-9021>, Schell et at. (2014) <doi:10.1016/j.ophtha.2014.02.021>, Aptel et al. (2015) <doi:10.1111/aos.12788>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (≥ 2.10)
Imports: stats, grDevices, graphics
NeedsCompilation: no
Packaged: 2019-05-23 20:39:50 UTC; LiD2
Repository: CRAN
Date/Publication: 2019-05-24 15:00:02 UTC

General plotting function for multiple 24-2 or 30-2 visual field measurements together:

Description

plotComponentMatrix plots the following 24-2 or 30-2 visual field measurement: sensitivity, TD, TD prob, PD, and PD prob:

Usage

plotComponentMatrix(componentmatrix, ncomp = ncol(componentmatrix),
  plot.ncols = 5, plot.nrows = NULL,
  plot.annot.topleft.function = toString,
  plot.annot.bottomleft.function = function(i) NULL,
  globaltitle = sprintf("k = %i", ncol(componentmatrix)),
  globalannotright = NULL,
  zmin = -ceiling(max(abs(c(min(componentmatrix),
  max(componentmatrix))))), zmax = -zmin,
  color.pal = colorRampPalette(c("red", "white", "blue"), space =
  "Lab")(256), td.probabilities = FALSE,
  show.colorbar = !td.probabilities, titleheight = 0.2, ...)

Arguments

componentmatrix

a matrix or data frame, column represents different eyes and rows are the VF measurements of the same type (sensitivity, TD, TD prob, PD, or PD prob).

ncomp

a numeric variable defines the number of components to be plotted (default: all).

plot.ncols

a numeric variable defines the number of columns to be plotted (default: 5).

plot.nrows

a numeric variable defines the number of rows to be plotted (default: NULL (automatically calculated)).

plot.annot.topleft.function

a function(i) that is given to any subplot i to create its top left annotation.

plot.annot.bottomleft.function

a function(i) that is given to any subplot i to create its bottom left annotation (default: returns NULL).

globaltitle

a string for global title (default: k = ncomp; set to NULL to suppress global title).

globalannotright

a string annotation to the right of the global title (default: NULL).

zmin

minimum value of the color scale (default: auto defined).

zmax

maximum value of the color scale (default: auto defined).

color.pal

an object that defines color scale theme (default: colorRampPalette(c("red", "white", "blue"), space = "Lab")(256)).

td.probabilities

a logic variable indicates whether to plot TD probability symbols instead of TD colors (default: FALSE).

show.colorbar

a logic variable indicates whether to show a global colorbar (default: !td.probabilities).

titleheight

a numeric variable defines the height of the title relative to height of row one.

...

other variables to be added.

Value

heatmap for sensitivity, TD and PD input. Value plot for TD prob and PD prob input.

Examples

data(vfseries)
componentmatrix = t(vfseries[1:10, grepl('^s[0-9]+', colnames(vfseries))])
globaltitle = paste("Sensitivities, k = ", ncol(componentmatrix), sep = '')
plotComponentMatrix(componentmatrix, globaltitle = globaltitle)
componentmatrix = t(vfseries[1:10, grepl('^td[0-9]+', colnames(vfseries))])
globaltitle = paste("TDs, k = ", ncol(componentmatrix), sep = '')
plotComponentMatrix(componentmatrix, globaltitle = globaltitle)
componentmatrix = t(vfseries[1:10, grepl('^pd[0-9]+', colnames(vfseries))])
globaltitle = paste("PDs, k = ", ncol(componentmatrix), sep = '')
plotComponentMatrix(componentmatrix, globaltitle = globaltitle)
componentmatrix = t(vfseries[1:10, grepl('^tdp[0-9]+', colnames(vfseries))])
globaltitle = paste("TD Probs, k = ", ncol(componentmatrix), sep = '')
plotComponentMatrix(componentmatrix, globaltitle = globaltitle, td.probabilities = TRUE)
componentmatrix = t(vfseries[1:10, grepl('^pdp[0-9]+', colnames(vfseries))])
globaltitle = paste("PD Probs, k = ", ncol(componentmatrix), sep = '')
plotComponentMatrix(componentmatrix, globaltitle = globaltitle, td.probabilities = TRUE)

Value plotting function for 24-2 or 30-2 visual field measurement:

Description

plotTDvalues plots the following 24-2 or 30-2 visual field measurement: sensitivity, TD, and PD:

Usage

plotTDvalues(tds, cex.tds = 1, textcolor = function(x) "black",
  show.lines = T, ...)

Arguments

tds

a vector contains sensitivity/TD/PD measurement. For 24-2 VF tds should have 52 or 54 elements. For 30-2 VF, tds should have 74 or 76 elements.

cex.tds

a numeric variable for label size (default: 1).

textcolor

a function defines the label color.

show.lines

a logical variable indicates whether to show the horizontal and vertical lines.

...

other variables to be added.

Value

value plot for sensitivity, TD and PD input.

Examples

data(vfseries)
tds = t(vfseries[1, grepl('^s[0-9]+', colnames(vfseries))])
plotTDvalues(tds)
title(main = "Sensitivity", line = 3)
tds = t(vfseries[1, grepl('^td[0-9]+', colnames(vfseries))])
plotTDvalues(tds)
title(main = "Total Dviation", line = 3)
tds = t(vfseries[1, grepl('^pd[0-9]+', colnames(vfseries))])
plotTDvalues(tds)
title(main = "Pattern Dviation", line = 3)

Value plotting function for 24-2 or 30-2 visual field measurement:

Description

plotTdProbabilities plots the following 24-2 or 30-2 visual field measurement: TD probs, and PD probs:

Usage

plotTdProbabilities(tdprob, cex = 2, rectangle.color = "black",
  rectangle.width = 0.16, margins = c(2, 1, 2, 2) + 0.1, ...)

Arguments

tdprob

a vector contains TD probs/PD probs measurement. For 24-2 VF tdprob should have 52 or 54 elements. For 30-2 VF, tdprob should have 74 or 76 elements.

cex

a numeric variable for label size (default: 2).

rectangle.color

a string variable defines label color (default: 'black').

rectangle.width

a numeric variable defines label width (default: '0.16').

margins

a vector define the plot margins (default: c(2, 1, 2, 2)+0.1).

...

other variables to be added.

Value

value plot for TD prob and PD prob input.

Examples

data(vfseries)
tdprob = t(vfseries[1, grepl('^tdp[0-9]+', colnames(vfseries))])
plotTdProbabilities(tdprob)
title(main = "Total Deviation Probability", line = 3)
tdprob = t(vfseries[1, grepl('^pdp[0-9]+', colnames(vfseries))])
plotTdProbabilities(tdprob)
title(main = "Pattern Deviation Probability", line = 3)

Single plotting function for one 24-2 or 30-2 visual field measurement:

Description

plotfield.normalized plots the following 24-2 or 30-2 visual field measurement: sensitivity, TD, TD prob, PD, and PD prob:

Usage

plotfield.normalized(eigenfields, component = 1,
  zmin = -max(abs(c(min(eigenfields), max(eigenfields)))),
  zmax = max(abs(c(min(eigenfields), max(eigenfields)))),
  color.pal = colorRampPalette(c("red", "white", "blue"), space =
  "Lab")(256), show.colorbar = TRUE, topleftannotation = NULL,
  bottomleftannotation = NULL, labelcex = 2, ...)

Arguments

eigenfields

a vector contains Sensitivity/TD/PD measurement. For 24-2 VF eigenfields should have 52 or 54 elements. For 30-2 VF, eigenfields should have 74 or 76 elements.

component

Number of components to be plotted (default: 1).

zmin

minimum value of the color scale (default: auto defined).

zmax

maximum value of the color scale (default: auto defined).

color.pal

an object that defines color scale theme (default: colorRampPalette(c("red", "white", "blue"), space = "Lab")(256)).

show.colorbar

a logic value to show colorbar (default: TRUE).

topleftannotation

a string annotation shown on the top left side of the plot (default: NULL).

bottomleftannotation

a string annotation shown on the bottom left side of the plot (default: NULL).

labelcex

a numeric variable for label size (default: 2).

...

other variables to be added.

Value

heatmap for sensitivity, TD and PD input

Examples

data(vfseries)
eigenfields = t(vfseries[1, grepl('^s[0-9]+', colnames(vfseries))])
plotfield.normalized(eigenfields)
title(main = "Sensitivity", line = 3)
eigenfields = t(vfseries[1, grepl('^td[0-9]+', colnames(vfseries))])
plotfield.normalized(eigenfields)
title(main = "Total Deviation", line = 3)
eigenfields = t(vfseries[1, grepl('^pd[0-9]+', colnames(vfseries))])
plotfield.normalized(eigenfields)
title(main = "Pattern Deviation", line = 3)

general progression function

Description

progression returns the progression criterion with four methods. plr.nouri.2012, vfi, schell2014, cigts

Usage

progression(vfseries, method = c("plr.nouri.2012", "vfi", "schell2014",
  "cigts"))

Arguments

vfseries

is a data frame. MUST contain the following columns: yearsfollowed', and 'eyeid'. Rows represent the single measurements. Other requirements, such as number of minimum measurements (rows), and necessary VF measurements could be found in each progression method's documentation

method

selected from one or more from: plr.nouri.2012, vfi, schell2014, cigts. Default it ...

Value

"stable", "worsening", or "improving" of measurements in measmatrix

See Also

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495761/

Examples

data(vfseries)
progression(vfseries)
progression(vfseries[vfseries$eyeid == 1,])
progression(vfseries[vfseries$eyeid == 2,])
progression(vfseries, method=c("cigts"))

CIGTS VF progression

Description

progression.cigts returns the progression of visual field test based on 52 or 54 total deviation probabilities (tdp). CIGTS VF progression (Musch et al., 1999).

Usage

progression.cigts(measmatrix)

Arguments

measmatrix

is a data frame. MUST contain the following columns: 52/54 TD probs (column names MUST be 'tdp1' ~ 'tdp52' or 'tdp1' ~ 'tdp54'), 'yearsfollowed', and 'eyeid'. Rows represent the single measurements. The minimum measurements (rows) is 5.

Value

"stable", "worsening", or "improving" of measurements in measmatrix. Note: If a VF series is temporarily improving and temporarily worsening, it is assumed to be "stable" overall

References

http://www.aaojournal.org/article/S0161-6420(99)90147-1/abstract

Examples

data(vf.cigts)
colnames(vf.cigts)
progression.cigts(vf.cigts)
progression.cigts(vf.cigts[vf.cigts$eyeid == 1,])
progression.cigts(vf.cigts[vf.cigts$eyeid == 2,])

Nouri-Mahdavi 2012 VF progression

Description

progression.plr.nouri.2012 returns the progression criterion, using Pointwise Linear Regression (PLR) progression detection method according to Nouri-Mahdavi et al. (2012).

Usage

progression.plr.nouri.2012(measmatrix)

Arguments

measmatrix

is a data frame. MUST contain the following columns: 52/54 TD (column names MUST be 'td1' ~ 'td52' or 'td1' ~ 'td54'), 'yearsfollowed', and 'eyeid'. Rows represent the single measurements. The minimum measurements (rows) is 3.

Value

"stable", "worsening", or "improving" of measurements in measmatrix

See Also

https://www.ncbi.nlm.nih.gov/pubmed/22427560/

Examples

data(vf.plr.nouri.2012)
colnames(vf.plr.nouri.2012)
progression.plr.nouri.2012(vf.plr.nouri.2012)
progression.plr.nouri.2012(vf.plr.nouri.2012[vf.plr.nouri.2012$eyeid == 1,])
progression.plr.nouri.2012(vf.plr.nouri.2012[vf.plr.nouri.2012$eyeid == 2,])

Schell 2014 VF progression

Description

progression.schell2014 returns the progression criterion after Schell et al. 2014, which is essentially like CIGTS but with MD, and only one follow-up is enough to confirm progression.

Usage

progression.schell2014(measmatrix)

Arguments

measmatrix

is a data frame. MUST contain the following columns: 'md' (mean deviation) and 'eyeid'. Rows represent the single measurements. The minimum measurements (rows) is 4.

Value

"stable", "worsening", or "improving" of measurements in measmatrix. Note: If a VF series is temporarily improving and temporarily worsening, it is assumed to be "stable" overall

See Also

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495761/

Examples

data(vf.schell2014)
colnames(vf.schell2014)
progression.schell2014(vf.schell2014)
progression.schell2014(vf.schell2014[vf.schell2014$eyeid == 1,])
progression.schell2014(vf.schell2014[vf.schell2014$eyeid == 2,])

progression according to VFI (significant slope, p<=0.05)

Description

progression.vfi returns the progression criterion used in Aptel et al. (2015).

Usage

progression.vfi(measmatrix)

Arguments

measmatrix

is a data frame. MUST contain the following columns: 'vfi' (visual field index), 'yearsfollowed', and 'eyeid'. Rows represent the single measurements. The minimum measurements (rows) is 3.

Value

"stable", "worsening", or "improving" of measurements in timepoints

See Also

https://www.ncbi.nlm.nih.gov/pubmed/26095771/

Examples

data(vf.vfi)
colnames(vf.vfi)
progression.vfi(vf.vfi)
progression.vfi(vf.vfi[vf.vfi$eyeid == 1,])
progression.vfi(vf.vfi[vf.vfi$eyeid == 2,])

Combined Visual Field Series for General Progression Method

Description

Data

Usage

data(vf.cigts)

Format

A data frame sample for CIGTS progression method, which includes visual field related measurement for two eyes, each with 10 follow-ups. Rows represent the single measurements.

Source

eyeid

eyeid, labeled as 1,2... for different eyes.

yearsfollowed

follow-up years. The minimum measurements /rows for one eye is 5.

tdp1-tdp54

52 total deviation probability, or 'tdp' measurements. The minimum measurements, or rows for one eye is 5.

...

Examples

data(vf.cigts)
colnames(vf.cigts)
progression.cigts(vf.cigts)
progression.cigts(vf.cigts[vf.cigts$eyeid == 1,])
progression.cigts(vf.cigts[vf.cigts$eyeid == 2,])

Combined Visual Field Series for General Progression Method

Description

Data

Usage

data(vf.plr.nouri.2012)

Format

A data frame sample for Pointwise Linear Regression (PLR) method according to Nouri-Mahdavi 2012 progression, which includes visual field related measurement for two eyes, each with 10 follow-ups. Rows represent the single measurements.

Source

eyeid

eyeid, labeled as 1,2... for different eyes

yearsfollowed

follow-up years. The minimum measurements, or rows, for one eye is 3

td1-td54

52 total deviation, or 'td' measurements. The minimum measurements, or rows, for one eye is 3

...

Examples

data(vf.plr.nouri.2012)
colnames(vf.plr.nouri.2012)
progression.plr.nouri.2012(vf.plr.nouri.2012)
progression.plr.nouri.2012(vf.plr.nouri.2012[vf.plr.nouri.2012$eyeid == 1,])
progression.plr.nouri.2012(vf.plr.nouri.2012[vf.plr.nouri.2012$eyeid == 2,])

Combined Visual Field Series for General Progression Method

Description

Data

Usage

data(vf.schell2014)

Format

A data frame sample for progression method by Schell et al. 2014, which includes visual field related measurement for two eyes, each with 10 follow-ups. Rows represent the single measurements.

Source

eyeid

eyeid, labeled as 1,2... for different eyes.

md

mean deviation measurements. The minimum measurements, or rows, for one eye is 4.

...

Examples

data(vf.schell2014)
colnames(vf.schell2014)
progression.schell2014(vf.schell2014)
progression.schell2014(vf.schell2014[vf.schell2014$eyeid == 1,])
progression.schell2014(vf.schell2014[vf.schell2014$eyeid == 2,])

Combined Visual Field Series for General Progression Method

Description

Data

Usage

data(vf.vfi)

Format

A data frame for CIGTS progression example, which includes visual field related measurement for two eyes each with 10 follow-ups.

Source

eyeid

eyeid, labeled as 1,2... for different eye groups.

yearsfollowed

follow-up years. The minimum measurements, or rows, for one eye is 3.

vfi

visual field index. The minimum measurements, or rows, for one eye is 3.

...

Examples

data(vf.vfi)
colnames(vf.vfi)
progression.vfi(vf.vfi)
progression.vfi(vf.vfi[vf.vfi$eyeid == 1,])
progression.vfi(vf.vfi[vf.vfi$eyeid == 2,])

Combined Visual Field Series for General Progression Method

Description

Data

Usage

data(vfseries)

Format

A data frame sample including the following visual field related measurement for two eyes, each with 10 follow-ups.

Source

eyeid

eyeid, labeled as 1,2... for different eyes.

nvisit

number of visits.

yearsfollowed

follow-up years.

distprev

to be updated.

age

in years.

righteye

1 as right eye, 0 as left eye.

malfixrate

VF test malfixation rate.

ght

glaucoma hemifield test result.

vfi

visual field index.

md

mean deviation.

mdprob

mean deviation probability.

psd

pattern standard deviation.

psdprob

pattern standard deviation probability.

s1-s54

52 sensitivity measurements.

td1-td54

52 total deviation measurements.

tdp1-tdp54

52 total deviation probability measurements.

pdp1-pdp54

52 pattern deviation probability measurements.

...

Examples

data(vfseries)
progression(vfseries)
progression(vfseries[vfseries$eyeid == 1,])
progression(vfseries[vfseries$eyeid == 2,])
progression(vfseries, method=c("cigts"))
progression.cigts(vfseries)
progression(vfseries, method=c('plr.nouri.2012', 'schell2014', 'vfi'))