SURVIVAL.COX.PATH OpenBI

A penalized Cox model's coefficients along the penalty's path.

Group: Survival analysis. OpenBI’s own: DAX has no function of this name.

Syntax

SURVIVAL.COX.PATH ( <table>, <duration>, <event> [, <options>] [, <entry>], <name>, <covariate> [, <name>, <covariate>] … )

Returns

Table. A row a lambda and term, the largest lambda first. The columns: Lambda, Log lambda, Variable, Coefficient and Deviance ratio.

Remarks

• The fit is the elastic net for Cox's model, as R's glmnet computes it (Efron's handling of ties): alpha=1 is the lasso, which can drop terms altogether; alpha=0 is ridge, which only shrinks them.
• The terms are put on a common scale before the penalty, as glmnet does, and the coefficients come back on the covariates' own scale.
• The path runs over nlambda values of lambda (100 by default, from 5 to 500), from the smallest that keeps every term at 0 down to ratio times it (0.0001, or 0.01 when there are fewer rows than terms).
• Plot Coefficient against Log lambda, a line a Variable: the lasso's picture of which terms come in first as the penalty eases.
• There is no cross-validation here; to choose a lambda, use SURVIVAL.COX.CV.

Compared with DAX

An OpenBI function: DAX has none like it, so a measure or query that uses it works in OpenBI only, not in Power BI or Analysis Services.

Examples

Five steps of the lasso

At the largest lambda every coefficient is 0; at the smallest they are nearly the plain Cox fit's.

EVALUATE
VAR Customers =
    DATATABLE (
        "Plan", STRING, "Months", INTEGER, "Left", INTEGER, "Spend", INTEGER, "Tickets", INTEGER,
        {
            { "Basic", 3, 1, 20, 4 }, { "Basic", 5, 1, 25, 3 }, { "Basic", 6, 0, 40, 1 }, { "Basic", 8, 1, 30, 5 },
            { "Plus", 9, 1, 35, 2 }, { "Basic", 10, 0, 45, 0 }, { "Basic", 12, 1, 28, 3 }, { "Plus", 14, 0, 60, 1 },
            { "Plus", 15, 1, 38, 4 }, { "Basic", 16, 0, 50, 1 }, { "Plus", 18, 1, 42, 2 }, { "Plus", 20, 0, 55, 0 },
            { "Basic", 22, 1, 33, 2 }, { "Plus", 24, 0, 65, 1 }, { "Plus", 25, 1, 48, 3 }, { "Basic", 27, 0, 36, 0 },
            { "Plus", 30, 1, 52, 1 }, { "Plus", 32, 0, 70, 0 }, { "Basic", 34, 0, 44, 2 }, { "Plus", 36, 0, 58, 1 },
            { "Basic", 4, 1, 22, 2 }, { "Plus", 7, 1, 30, 3 }, { "Basic", 13, 1, 26, 1 }, { "Plus", 28, 0, 62, 2 }
        }
    )
RETURN
    SURVIVAL.COX.PATH ( Customers, [Months], [Left], "nlambda=5", "Plan", [Plan], "Spend", [Spend], "Tickets", [Tickets] )

The elastic net

With alpha=0.5, half lasso and half ridge.

EVALUATE
VAR Customers =
    DATATABLE (
        "Plan", STRING, "Months", INTEGER, "Left", INTEGER, "Spend", INTEGER, "Tickets", INTEGER,
        {
            { "Basic", 3, 1, 20, 4 }, { "Basic", 5, 1, 25, 3 }, { "Basic", 6, 0, 40, 1 }, { "Basic", 8, 1, 30, 5 },
            { "Plus", 9, 1, 35, 2 }, { "Basic", 10, 0, 45, 0 }, { "Basic", 12, 1, 28, 3 }, { "Plus", 14, 0, 60, 1 },
            { "Plus", 15, 1, 38, 4 }, { "Basic", 16, 0, 50, 1 }, { "Plus", 18, 1, 42, 2 }, { "Plus", 20, 0, 55, 0 },
            { "Basic", 22, 1, 33, 2 }, { "Plus", 24, 0, 65, 1 }, { "Plus", 25, 1, 48, 3 }, { "Basic", 27, 0, 36, 0 },
            { "Plus", 30, 1, 52, 1 }, { "Plus", 32, 0, 70, 0 }, { "Basic", 34, 0, 44, 2 }, { "Plus", 36, 0, 58, 1 },
            { "Basic", 4, 1, 22, 2 }, { "Plus", 7, 1, 30, 3 }, { "Basic", 13, 1, 26, 1 }, { "Plus", 28, 0, 62, 2 }
        }
    )
RETURN
    SURVIVAL.COX.PATH ( Customers, [Months], [Left], "alpha=0.5, nlambda=5", "Plan", [Plan], "Spend", [Spend], "Tickets", [Tickets] )

Related functions

SURVIVAL.COX.CV, SURVIVAL.COX.PENALIZED, SURVIVAL.COX.

Last updated 3 November 2026 · OpenBI Studio 0.3.74