Here I will discuss some of the research-related things that you need to ask yourself before you can actually get started. I will also refer to some R information.
R is a programming language with extensive functionality related to statistical analyses.
QDECR is written to capitalize on some of this functionality, including:
AsIs(‘as is’) treatment of data within formulas, i.e. being able to manipulate the data while specifying the formula
All these features can be found in
qdecr function follows a number of steps:
QDECR all vertex measures that Freesurfer calculates have default names. This is
qdecr_ combined with the name of the vertex measure file. A comprehensive list:
Note that qdecr_w-g.pct does not work yet.
Nearly all statistical model functions in
formula objects. The
formula object allows users to generate design matrices for subsequent analysis through straightforward syntax:
Y ~ a + b
Lets deconstruct this:
Y: The outcome (AKA dependent variable AKA label)
~: Denotes the left-hand side versus the right-hand side of the formula
a + b: The additive effect of determinants
b(AKA independent variables AKA features)
This format allows users to use simple pseudo-math to generate complicated design matrices.
R handles design making for incomplete data, conversion of categorical variables to e.g. dummy variables, etc.
QDECR uses the
formula object to allow users to easily create design matrices. It further extends this functionality by explicitly including the vertex measure as a variable in the formula:
qdecr_thickness ~ a + b
R also allows users to apply more complicated formulas:
Y ~ a:b
Y ~ a * b[equivalent to
Y ~ a + b + a:b]
Y ~ a + poly(b, 2, raw = TRUE)[equivalent to
Y ~ a + b + I(b^2)]
Y ~ a + poly(b, 2)
Y ~ bs(a, 3)
AsIs treatment of objects, meaning that variables can be manipulated within the
formula object itself using
I(). This allows users to do all kinds of things in the formula itself, including:
Y ~ I(scale(a))
Y ~ I(a + 2*b)
QDECR has all these features.
Datasets may contain missing information. The missing information can be imputed under certain conditions. Commonly used R package for imputation are
mi. We designed R in such a way that imputed datasets can be used as the input dataset, without any specifications by the user. QDECR currently supports imputed objects from
Users may want to reduce computation time by utilizing multiple processes. QDECR has the
n_cores argument that allows users to specify the number of processes (cores/threads) to use. Note that the benefit of using multiple processes is most evident when increasing the number of imputed datasets.
[Next vignette: 4. Post-processing]