brainGraph_GLM specifies and fits a General Linear Model (GLM) at each
vertex for a given vertex measure (e.g. degree) or at the graph-level
(e.g., global efficiency). Given a contrast matrix or list of
contrast(s), and contrast type (for t- or F-contrast(s), respectively) it
will calculate the associated statistic(s) for the given contrast(s).
The summary method prints the results, only for which
\(p < \alpha\), where alpha comes from the bg_GLM object.
“Simple” P-values are used by default, but you may change this to the
FDR-adjusted or permutation P-values via the function argument p.sig.
You may also choose to subset by contrast.
The plot method plots the GLM diagnostics (similar to that of
plot.lm). There are a total of 6 possible plots,
specified by the which argument; the behavior is the same as in
plot.lm. Please see the help for that function.
The [ method allows you to select observations (i.e., rows of X
and y) and independent variables (i.e., columns of X) from a
bg_GLM object.
brainGraph_GLM(g.list, covars, measure, contrasts, con.type = c("t",
"f"), outcome = NULL, X = NULL, con.name = NULL,
alternative = c("two.sided", "less", "greater"), alpha = 0.05,
level = c("vertex", "graph"), permute = FALSE,
perm.method = c("freedmanLane", "terBraak", "smith", "draperStoneman",
"manly", "stillWhite"), part.method = c("beckmann", "guttman",
"ridgway"), N = 5000, perms = NULL, long = FALSE, ...)# S3 method for bg_GLM
print(x, ...)
# S3 method for bg_GLM
summary(object, p.sig = c("p", "p.fdr", "p.perm"),
contrast = NULL, alpha = object$alpha, digits = max(3L,
getOption("digits") - 2L), print.head = TRUE, ...)
# S3 method for bg_GLM
plot(x, region = NULL, which = c(1L:3L, 5L),
ids = TRUE, ...)
# S3 method for bg_GLM
[(x, i, j)
An object of class bg_GLM containing some input-specific
variables (level, outcome, measure, con.type,
contrasts, con.name, alt, alpha,
permute, perm.method, part.method, N) in
addition to:
A data table from which the design matrices are created and the outcome variable, for all regions.
A named numeric matrix or a 3D array of the design matrix.
Rownames are subject IDs, column names are predictor variables, and
dimnames along the 3rd dimension are region names (if applicable). This
is a 3D array only if outcome != measure and level ==
'vertex'.
A named numeric matrix of the outcome variable. Rownames are Study
IDs and column names are regions. There will be multiple columns only if
outcome == measure and level == 'vertex'.
A data table with an entry for each vertex (region) containing statistics of interest
A named integer vector in which the names are subject
ID's of those removed due to incomplete data (if any). The integers
correspond to the row number in the input covars table.
If outcome != measure and level == 'vertex', this
will be a character vector of the regions for which the design matrix is
invertible. Otherwise, it is NULL.
Character vector of the regions for which the outcome variable
has 0 variability. For example, if level='vertex' and
measure='degree', some regions may be disconnected or have the
same degree for all subjects.
Character string of the atlas used (guessed based on the vertex count).
A list containing: null.dist (the null distribution of maximum statistics), thresh (the statistic value corresponding to the \(100 \times (1 - \alpha)\)th% percentile of the null distribution)
The plot method returns a list of ggplot objects
(if installed) or writes the plots to a PDF in the current directory named
bg_GLM_diagnostics.pdf
A bg_GLM object with the specified row(s) selected or removed
from both X and y, and column(s) selected/removed from
X
A brainGraphList object
A data.table of covariates
Character string of the graph measure of interest
Numeric matrix (for T statistics) or list of matrices (for F statistics) specifying the contrast(s) of interest; if only one contrast is desired, you can supply a vector (for T statistics)
Character string; either 't' or 'f' (for t or
F-statistics). Default: 't'
Character string specifying the name of the outcome variable,
if it differs from the graph metric (measure)
Numeric matrix, if you wish to supply your own design matrix.
Ignored if outcome != measure.
Character vector of the contrast name(s); if contrasts
has row/list names, those will be used for reporting results
Character string, whether to do a two- or one-sided test.
Default: 'two.sided'
Numeric; the significance level. Default: 0.05
Character string; either vertex (default) or
graph
Logical indicating whether or not to permute group labels.
Default: FALSE
Character string indicating the permutation method.
Default: 'freedmanLane'
Character string; the method of partitioning the design
matrix into covariates of interest and nuisance. Default: 'beckmann'
Integer; number of permutations to create. Default: 5e3
Matrix of permutations, if you would like to provide your own.
Default: NULL
Logical indicating whether or not to return all permutation
results. Default: FALSE
Arguments passed to brainGraph_GLM_design
A bg_GLM object
Character string specifying which P-value to use for displaying
significant results (default: p)
Integer specifying the contrast to plot/summarize; defaults to showing results for all contrasts
Integer specifying the number of digits to display for P-values
Logical indicating whether or not to print only the first
and last 5 rows of the statistics tables (default: TRUE)
Character string specifying which region's results to
plot; only relevant if level='vertex'. Default: NULL
Integer vector indicating which of the 6 plots to print to the
plot device. Default: c(1:3, 5)
Logical indicating whether to plot subject ID's for outliers. Otherwise plots the integer index
Integer/character vector; the observation number(s) or row names to select or remove
Integer/character vector; the design matrix column number(s) or names to select or remove
The GLM's design matrix will often be identical to the model
matrix associated with lm objects (if “dummy” coding, the
default, is used) and is created from the input data.table and
arguments passed to brainGraph_GLM_design. The first column
should have the name of getOption('bg.subject_id') and its values
must match the name graph-level attribute of the input graphs. The
covariates table must be supplied even if you provide your own design matrix
X. If level='vertex' and outcome == measure, there will
be a single design for all regions but a separate model for each region
(since the graph measure varies by region). If level='vertex' and
outcome != measure, there will be a separate design (and, therefore, a
separate model) for each region even though the outcome is the same in all
models.
Either t- or F-contrasts can be calculated (specified by con.type).
Multiple t-contrasts can be specified by passing a multi-row matrix to
contrasts. Multiple F-contrasts can be specified by passing a
list of matrices; all matrices must have the same number of columns.
All F-contrasts are necessarily two-sided; t-contrasts can be any
direction, but only one can be chosen per function call.
If you choose con.type="f", the calculated effect size is represented
by the ESS (“extra sum of squares”), the additional variance
explained for by the model parameters of interest (as determined by the
contrast matrix). The standard error for F-contrasts is the sum of squared
errors of the full model.
You can calculate permutations of the data to build a null distribution of
the maximum statistic which corrects for multiple testing. To account for
complex designs, the design matrix must be partitioned into covariates
of interest and nuisance; the default method is the Beckmann method.
The default permutation strategy is that of Freedman & Lane (1983), and is
the same as that in FSL's randomise. See randomise.
Christopher G. Watson, cgwatson@bu.edu
The measure argument will be the graph- or vertex-level measure of
interest. Often, this will serve as the model's outcome (or dependent,
or response) variable; i.e., the variable typically denoted by y in
GLMs. In other cases, you may wish to choose some other variable as the
outcome; e.g., IQ, age, etc. Then you could test for a direct association
between the network measure and outcome of interest, or test for another
association while adjusting for the network metric. For these applications,
you must provide the variable name via the outcome argument. This is
analogous to -evperdat in FSL's PALM and to --pvr in
FreeSurfer.
Other GLM functions: GLM design,
GLM fits, mtpc
Other Group analysis functions: Bootstrapping,
Mediation, NBS,
brainGraph_permute, mtpc
if (FALSE) {
conmat <- matrix(c(0, 0, 0, 1), nrow=1)
rownames(conmat) <- 'Control > Patient'
res.lm <- brainGraph_GLM(g[[6]], covars=covars.all[tract == 1],
measure='strength', contrasts=conmat, alt='greater', permute=TRUE, long=TRUE)
}
if (FALSE) {
## Save objects and then to multipage PDF
lmPlots <- plot(x)
ggsave('lmPlots.pdf', lmPlots)
## Save all the GLM sub-objects from MTPC analysis
res.mtpc <- mtpc(...)
glmPlots <- lapply(res.mtpc$res.glm, plot, which=1:6)
ml <- marrangeGrob(glmPlots, nrow=1, ncol=1)
ggsave('glmPlots.pdf', ml, width=8.5, height=11)
}
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