Date: Mon, 25 May 2020 01:37:37 +0000 (GMT) Message-ID: <413091158.6583.1590370657547@c4e8295e3740> Subject: Exported From Confluence MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_Part_6582_1648661912.1590370657546" ------=_Part_6582_1648661912.1590370657546 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Content-Location: file:///C:/exported.html Total Effects and Direct Effects Calculation

# Total Effects and Direct Effects Calculation

### Questio= n

How are Total Effects on Target and Direct= Effects on Target calculated in BayesiaLab?

Both the Total and Direct Effects are the derivative of their correspond= ing Effect Function computed at the a-priori mean value (delta =3D 0).

To= tal Effect Function

The Total Effect Function is estimated by using the Mean Value Analysis = (based in the MinXEnt) to go through the variation domain of a variable in = order to measure its impact on the Target mean.

Example
=20 Adding 1 to the mean value of Corresponds (gre= en curve) increases the target node Pleasure_(4) = by 0.78 =20

To= tal Effects

Total Effects are the derivatives of the= Total Effect functions, taken at the a-priori mean values of the variables= , the Standardized value normalizing the effect by taking int= o account the ration between the standard deviation of the variable and the= one of the Target. Example
=20 =20 =20 =20 =20
 Node Value/Mean Standardized Total Effects Total Effects Pleasure 6.05 0.96 0.8 Corresponds 5.76 0.95 0.79 Easy to wear 6.5 0.83 0.79 Intensity 3.07 -0.19 -0.55
=20

Di= rect Effect Function

The Direct Effect Function is estimated by using the Mean Value An= alysis (based in the MinXEnt) to go through the variation domain of a varia= ble in order to measure its impact on the Target mean, while holding fixed the= probability distributions of all the variables, except:

• The Not-Observable variables b= elonging to the Class =E2=80=9CFactor=E2=80=9D
• The variables belonging to the Class =E2=80=9CNon_Confounder=E2=80=9D .
Example
=20 Adding 1 to the mean value of Corresponds (green curve) while holding fixed all the marginal probabili= ty distributions of all the other variables increases the target node Pleasure_(4) by 0.27 <= /span>

=20
Di= rect Effects

Direct Effects are the derivatives of th= e Direct Effect functions, taken at the a-priori mean values of the variabl= es, the Standardized value normalizing the effect by taking int= o account the ration between the standard deviation of the variable and the= one of the Target. Example
=20 =20 =20 =20 =20
 Node Value/Mean Standardized Direct Effects Direct Effects Pleasure 6.05 0.48 0.4 Corresponds 5.76 0.4 0.33 Easy to wear 6.5 0.1 0.1 Intensity 3.07 -0.01 -0.03
=20