general-lassosig-methods: Call 'LassoSig' for multiple response variables
In kieranrcampbell/SpatialStats: Statistics for spatially resolved proteomics data

Description Usage Arguments Value See Also

Call LassoSig for multiple response variables

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GeneralLassoSig(Y, X, B = 100, s = c("lambda.min", "lambda.1se", "halfway",
  "usefixed"), gamma.min = 0.05, fixedP = NULL, include = NULL,
  nfolds = 10, intercept = TRUE)

`Y`	Response matrix for general regression with `GeneralLassoSig`
`X`	Design matrix
`B`	Number of times to partition the sample
`s`	The value of lambda to use in lasso. Can be: An actual value of lambda you have determined `lambda.min` The lambda that minimises the mean squared error from n-fold cross-validation (recommended) `lambda.1se` The lambda that accounts for the smallest number of parameters and is within 1 standard error from `lambda.min` `halfway` The lambda that is halfway between `lambda.min` and `lambda.1se` `usefixed` Use the smallest lambda that accounts for a given number of parameters (set by `fixedP`)
`include`	Set of predictors to be force-included in OLS analysis
`gamma.min`	Lower bound for gamma in the adaptive search for the best p-value (default 0.05)
`fixedP`	The fixed number of parameters to use (if `s == "usefixed"`)
`nfolds`	Number of folds of cross-validation in the glmnet n-fold crossvalidation
`intercept`	Whether to include an intercept in the OLS regression (default = `TRUE`)