pcev's People
pcev's Issues
Interaction terms
At the moment, interaction terms need to be included explicitly in the PCEV framework, i.e. covariate
needs to include a column corresponding to the multiplication of other variables. Can we do better? I think that would require a reformatting of the front-end, perhaps including a formula-type interface...
documentation of computePCEV
I've recently received questions about the output of computePCEV
and I realized that I never explain the different components of an object of class Pcev
. It would probably make the most sense to add this directly to the documentation page for computePCEV
, since this is the main function of the package.
No permutation
At the moment, setting nperm = 0
works, and it behaves as expected (i.e. the model is fitted and no permutation is performed. However, it triggers an error in the print
method. We need to replace the p-value with NA
or something.
Adaptive selection of blocks
In my most recent simulations (and also in the SMMR paper), I choose the blocks randomly, which still gives a decent performance. So I thought I could add that option in the package, in two ways:
-
If
index
is a single positive integer, this is assumed to be the number of blocks. -
If
index = "adaptive"
, then the number of blocks is chosen to be2*p/n
, so that there are aboutn/2
variables per block.
In boths cases, the variables are assigned to a given block at random.
Error When More Covariates Than Response Variables
Hello. When using the pcev package, I get an error when the number of covariates (q) is larger than the number of response variables (p). As you can see in the output below, the error message I get when calling computePCEV(Y, X) is "Error in weights[, 2:(ncol(pcevObj$X) - 1), drop = FALSE]: subscript out of bounds". Is there a reason why PCEV shouldn't be used when q > p, or is there an error in the package? Thank you.
# Attach pcev package
library(pcev)
# Define dimensions
p <- 2 # Number of response variables
q <- 3 # Number of covariates
n <- 100 # Number of observations
# Generate response matrix and covariate matrix
set.seed(10)
Y <- matrix(rnorm(n * p), nrow = n, ncol = p) # Response variables
X <- matrix(rnorm(n * q), nrow = n, ncol = q) # Covariates
# Attempt to perform PCEV on Y and X
pcev_out <- computePCEV(Y, X)
#> Error in weights[, 2:(ncol(pcevObj$X) - 1), drop = FALSE]: subscript out of bounds
Created on 2020-07-06 by the reprex package (v0.3.0)
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