Description Usage Arguments Value
Computes random split validation for glmnet, produces a plot, and returns a value for lambda
1 2 3 4 5 6 7 | hqsar(X_md_train, X_md_test, X_ge_train, X_ge_test, y_train, y_test,
type.transformation.md = "none", type.transformation.ge = "none",
alpha = c(0.1, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2), alpha1 = c(0.1,
0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2), cost.md = 3, cost.ge = 3,
bb.md = 2, bb.ge = 2, type.solution = "lci", measure = "MSE",
intercept = TRUE, th = 1.96, n.splits = 99, inner.train.prop = 0.9,
nLambda = 100, n.splits.val = 25, n.splits.scr = 25, nCores = 30)
|
type.solution |
is a string indicating the type of solution to compute. Possible values are: min, uci and lci; if standard min or max of the average CV function. if uci, take the the most parsimonous solution within the tot percentage of confidence bands around the standard solution. if lci a less partimosious solution within the tot percentage of confidence bands around the standard solution is selected |
measure |
is the measure used to perform the choice of the optimal lambda value. Possible values are MSE and R2. Default value is MSE |
intercept |
is a boolean valus indicating if we want to fit or not the intercept. Default valuw is TRUE |
th |
is the size of the confidence interval. Default value is 1.96 |
n.splits |
is the number of random split to be performed. Default value is 25 |
inner.train.prop |
is the percentage of samples from the train test to be used as training set in the random-split method. Default value is 0.9 (90) |
nLambda |
number of lambda to be tested in the LASSO model |
n.splits.val |
is the number of random split to compute validation metrics. Default value is 25 |
n.splits.scr |
is the number of random split to perform the y-scrambling test. Default value is 25 |
nCores |
is the number of cores to be used |
X_md |
is the dataset matrix with samples on rows and MDs on columns. |
X_ge |
is the dataset matrix with samples on rows and genes on columns. |
y |
is the numeric vector of response variables. |
type.transformation |
is a string indicating the type of transformation to be performed at the data before fitting the model. It can be one of the following: "none","abs","abs.alpha.power","mul","log.abs","log.abs.alpha.power"; none is the default parameter. |
alpha_values |
is a numeric vector of alpha value to be used in the abs.alpha.power transformation. Default value is c(0.1,0.25,0.5,0.75,1,1.25,1.5,1.75,2) |
cost |
is a numeric constant used when type.transformation = mul. |
bb |
is the base of the logarithm used when type.transformation = log.abs or log.abs.alpha.power |
an object of class tqsar containing a list of final models, a list of williams plot object a dataframe with all the metrices computed for the different models, and a list of lambda values used to train the LASSO model.
finalModels |
a list with one or more models coming from the RCVLasso function |
williams_plots |
a list of one or more williams plot objects |
Metrics |
a dataframe with all the internal and external metrics estimated for every model |
lambda |
a list of one or more vector of lambda used to tune the LASSO models |
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