glr {visualFields} | R Documentation |
Global and pointwise linear regression analyses
Description
Functions that compute global and pointwise linear regression analyses:
glr
performs global linear regression analysisplr
performs pointwise linear regression (PLR) analysispoplr
performs PoPLR analysis as in O'Leary et al (see reference)
Usage
glr(g, type = "md", testSlope = 0)
plr(vf, type = "td", testSlope = 0)
poplr(vf, type = "td", testSlope = 0, nperm = factorial(7), trunc = 1)
Arguments
g |
global indices |
type |
type of analysis. For |
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) |
Details
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
Value
glr
andplr
return a list with the followingid
patient IDeye
patient eyetype
type of data analysis. . Forglr
, it can be 'ms
', 'ss
', 'md
', 'sd
', 'pmd
', 'psd
', 'vfi
', or 'gh
' for mean sensitivity, standard deviation of sensitivities, mean deviation, standard deviation of total deviation values, pattern mean deviation, pattern standard deviation, VFI, and general height, respectively. Forplr
andpoplr
, it can be 's
', 'td
', or 'pd
' for sensitivities, total deviation values, or pattern deviation values, respectivelytestSlope
slope forglr
or list of slopes forplr
to test as null hypothesesnvisits
number of visitsyears
years from baseline. Used for the pointwise linear regression analysisdata
data analyzed. Forglr
, it is the values of the global indes analyzed. Forplr
, 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 regressionint
intercept estimated at each location for pointwise (simple) linear regressiontval
t-values obtained for the left-tailed-t-tests for the slopes obtained in the pointwise (simple) linear regression at each locationpval
p-values obtained for the left-tailed t-tests for the slopes obtained
poplr
returns a list with the following additional fieldscsl
the modifed Fisher's S-statistic for the left-tailed permutation testcslp
the p-value for the left-tailed permutation testcsr
the modifed Fisher's S-statistic for the right-tailed permutation testcsrp
the p-value for the right-tailed permutation testpstats
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 allnperm
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 respectivelycslp, csrp
the corresponding p-values for the permutation testscslall, csrall
the combined Fisher S-statistics for all permutations
References
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
Examples
vf <- vffilter(vfpwgRetest24d2, id == 1) # select one patient
res <- glr(getgl(vf)) # linear regression with global indices
res <- plr(vf) # pointwise linear regression (PLR) with TD values
res <- poplr(vf) # Permutation of PLR with TD values