Description Usage Arguments Details Value Note Author(s) References See Also Examples
dimVAR
is a support function that assesses the proportion of and cumulative variances for a
range of factor solutions.
1 |
R |
(Regularized) correlation |
maxdim |
A |
graph |
A |
verbose |
A |
To assess a factor solution under m factors one might look at the proportion of explained variance.
The dimVAR
function calculates the proportion of variance explained by any factor as well as
the proportion of variance explained by all factors for each factor solution raging from 1 to maxdim
.
Qualitatively, we want the proportion of variance explained by all factors to be appreciable (rules of thumb would say in excess of 70%).
Moreover, one would want the proportion of variance explained by the kth factor in relation to the (k - 1)th factor to be appreciable and the proportion of variance of the (k + 1)th factor in relation to the kth factor to be negligible.
When graph = TRUE
also a graph is returned visualizing the total cumulative variance against the dimension of the factor solution.
Hence, it plots the total cumulative variances against the respective factor solutions ranging from 1 to maxdim
.
The point at which the graph flattens out is indicative of a formative number of latent factors.
Returns an object of class list
.
$CumVar |
Contains a |
$varianceTables |
This slot is itself a |
The argument maxdim
cannot exceed the Ledermann-bound (Ledermann, 1937): \lfloor [2p + 1 - (8p + 1)^{1/2}]/2\rfloor, where p indicates the observed-feature dimension.
Usually, one wants to set maxdim
much lower than this bound.
The tabulations in the $varianceTables
slot are based on unrotated maxmimum likelihood factor solutions. Note that the total cumulative variance does not depend on the choice of (orthogonal) rotation.
Carel F.W. Peeters <cf.peeters@vumc.nl>
Ledermann, W. (1937). On the rank of the reduced correlational matrix in multiple factor analysis. Psychometrika, 2:85–93.
Peeters, C.F.W. et al. (2019). Stable prediction with radiomics data. arXiv:1903.11696 [stat.ML].
1 2 3 4 5 6 7 8 9 10 | ## Simulate some high-dimensional data according to the factor model
simDAT <- FAsim(p = 50, m = 5, n = 40)
## Regularize the correlation matrix
RegR <- regcor(simDAT$data)
## Assess proportion and cumulative variances for a range of factor solutions
## Inspect, for example, the variance table for the 5-factor solution
V <- dimVAR(RegR$optCor, maxdim = 20)
V$varianceTables$`dimension = 5`
|
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