addLBK4matching: Extract necessary data from loanbook and format it
In 2DegreesInvesting/pacta: Paris Agreement Capital Transition Assessment

Description Usage Arguments Relation types Value types

Function takes vectors of values and products dataframe that can be used for matching

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addLBK4matching(level, ids, names, values, sectors, relation = "Id",
  value.type = "Legal Name", source = "Loanbook", simplify = FALSE,
  cut.ownership = FALSE)

`level`	mathicng level
`ids`	vector of IDs, for example customer Borrower IDs
`names`	vector of names, that characterize ID (for example Borrower Name)
`values`	vector of names, for example Borrower Name
`sectors`	vector of sector sclassification
`relation`	describes how CustomerID related to OriginalValue in the output; see section "Relation types" for more details
`value.type`	describes what type of data OriginalValue contains; see section "Value types" for more details
`source`	describes source of information; it can be loanbook or manial client linkage file
`simplify`	defines if matching should be performed using simplified values
`cut.ownership`	cuts-off ownership type

Relation type describes relation between Value for matching and client ID. For example if we use client's name, then relation will be Id; if we use names of SPVs that belong to client, then we will use code Legal child.

In general value just characterizes company. It can be any unformation useful for matching. Value type describes what kind of value we use. It can be Company name (Name) in a standard case, but it can also be security ISIN code issued by company (ISIN) or IMO of a ship owned by company (IMO).

2DegreesInvesting/pacta documentation built on June 17, 2019, 12:04 p.m.