H0_test | R Documentation |
This function will extract data and parameter settings from a MUVR object and run standard permutation or resampling test. This will fit a standard case of multivariate predictive modelling in either a regression, classification or multilevel case. However, if an analysis has a complex sample dependency which requires constrained permutation of your response vector or if a variable pre-selection is performed for decreased computational burden, then permutaion/resampling loops should be constructed manually. In those cases, View(H0_test) can be a first start from which to build custom solutions for permutation analysis.
H0_test(
MUVRclassObject,
n = 50,
nRep,
nOuter,
varRatio,
parallel,
type = c("resampling", "permutation")
)
MUVRclassObject |
a 'MUVR' class object |
n |
number of permutations to run |
nRep |
number of repetitions for each permutation (defaults to value of actual model) |
nOuter |
number of outer validation segments for each permutation (defaults to value of actual model) |
varRatio |
varRatio for each permutation (defaults to value of actual model) |
parallel |
whether to run calculations using parallel processing which requires registered backend (defaults to parallelization for the actual model) |
type |
either permutation or resampling, to decide whether the permutation sampling is performed on original Y values or the probability(If Y categorical)/distributions(If Y continuous) of Y values |
permutation_output: A permutation matrix with permuted fitness statistics (nrow=n and ncol=3 for min/mid/max)
data("freelive2")
nRep <- 2
nOuter <- 4
varRatio <-0.6
regrModel <- MUVR2(X = XRVIP2,
Y = YR2,
nRep = nRep,
nOuter = nOuter,
varRatio = varRatio,
method = "PLS",
modReturn = TRUE)
H0_test(regrModel)
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