This function is under active development. It is meant to reduce the entire time-series of normalized and baseline-corrected pupillary data in just a few scores obtained by rotated Principal Component Analysis. PCA is an effective way to reduce data dimensionality as to have more manageable dependent variables, which may additionally help having more precise estimates (or fingerprints) of pupil signal and the underlying cognitive processes. Rotation in the oblique sense does not force PCs to be orthogonal and may help the interpretation of the resulting loadings. This function uses 'psych::principal()' and as such may differ in how standardization is performed.
Usage
reduce_rPCA(
data,
dv,
time,
id,
trial,
Ncomp = NULL,
center = FALSE,
scale = FALSE,
rotate = "promax",
add
)Arguments
- data
A data.frame containing all the necessary variables.
- dv
A string indicating the name of the dependent variable.
- time
A string indicating the name of the time variable.
- id
A string indicating the name of the id (participant) variable.
- trial
A string indicating the name of the trial variable.
- Ncomp
Number of components to retain. The default (NULL) automatically retains 95% of the explained variance. Note that is, however, based on unrotated PCA, and rotated variables generally explain less variance. If Ncomp== "all" returns all the components. If Ncomp <1 this is interpreted as if the user wishes to retain a given proportion of variance (e.g. 0.6), but again the unrotated PCA is used to find the number of components.
- center
Whether variables, i.e. pupil size for each timepoint, should be scaled beforehand. Defaults to FALSE assuming that measures are already normalized (with z-scores) and baseline-corrected. Note that this only impacts summaryPCA and the number of components retained because psych uses the covariance matrix.
- scale
Whether variables, i.e. pupil size for each timepoint, should be scaled beforehand. Defaults to FALSE assuming that measures are already normalized (with z-scores) and baseline-corrected. Note that this only impacts summaryPCA and the number of components retained because psych uses the covariance matrix.
- rotate
Defaults to "promax" for oblique rotation. Could be set to "none" to have PCA in the style of the 'psych' package. Accepts what is accepted by 'psych::principal()'.
- add
String(s) indicating which variables names, if any, should be appendend to the scores dataframe.