The arboretum procedure



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documentation -> From cyber-crime to insider trading, digital investigators are increasingly being asked to
documentation -> EnCase Forensic Transform Your Investigations
documentation -> File Sharing Documentation Prepared by Alan Halter Created: 1/7/2016 Modified: 1/7/2016
documentation -> Gaia Data Release 1 Documentation release 0

Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.


The DMREG Procedure

SCORE Statement

Specifies options for scoring data.

SCORE scoring-option(s);

Options

Scoring-options can be the following:

ADDITIONALRESIDUALS

Specifies that the OUT= data set contains additional residuals such as: RS_ for logistic regressions

and RS_, RT_, RD_, RDS_, RDT_ for normal error regression. See for more detail.

Alias:

ADDRES |AR



ALPHA=number

Specifies the significance level p for the construction of 100(1-p)% confidence interval for the

posterior probabilities. This number must be between 0 and 1.

Default:

0.05


CLP

Specifies that the OUT= data set contains the confidence limites for the posterior probabilities.

The significance leve is controlled by the ALPHA= option.

DATA= SAS-data-set

Specifies the input data set that contains inputs and optionally targets.



Default:

The default is the same as the DATA= data set in the PROC statement.



DMDB | NODMDB

Specifies whether an explicit DATA= data set has been DMDB-encoded or if the data set contains

raw data.

Default:

If the DATA= option is not specified in the SCORE statement, the training

data is used and the NODMDB option is invalid.

Caution:

If the DATA= in the SCORE statement specifies a data set other than the

training data, either DMDB or NODMDB must be specified in the SCORE

statement.



OUT= SAS-data-set


Specifies the output data set with outputs.

Default:

DATAn

Names for computed variables are normally taken from the data dictionary. If necessary, names

for these variables can be generated by concatenating a prefix to the name of the corresponding

target variable according to the rules in the following tables:

Statistics Generated in the

OUT=SAS-data-set for Normal

EerrorRegression

NAME

LABEL

P_targetname

Predicted:

targetname

E_targetname

Error


Function:

targetname

R_targetname

Residuals:

targetname

RD_targetname

Deviance

Residuals:

targetname

If the target is declared as a

categorical variable, the

OUT=SAS-data-set also includes:

F_targetname

From:


targetname

I_targetname

Into:

targetname



If the ADDITIONALRESIDUALS

option is also specified, the

OUT=SAS-data-set includes:



RS_targetname

Standardized

Residuals:

targetname

RT_targetname

Studentized

Residuals:

targetname

RDS_targetname

Standardized

Deviance

Residuals:

targetname

RDT_targetname Studentized

Deviance

Residuals:

targetname

Note:   In the table above, targetname is the name of the target variable. For example, if

PURCHASE is the targetname, the predicted value statistic is named P_PURCHA and the

studentized deviance residual is named RDT_PURC. [If the constructed names are longer than the

maximum of eight characters allowed for SAS variable names, they are truncated to eight

characters.]  

Statistics Generated in the OUT= SAS-data-set for

Binomial or Multinomial Regression

NAME


LABEL

P_targetname&value

Predicted:

targetname=targetvalue

F_targetname

From: targetname

I_targetname

Into: targetname

E_targetname

Error Function:

R_targetname&value

Residual:

targetname=targetvalue



If the ADDITIONALRESIDUALS option is

specified, the OUT=SAS-data-set includes:

RS_targetname&value Standardized Residual:

targetname=targetvalue



Note:   In the table above, targetname&value is a combination of the target name (targetname) and

target value (targetvalue). For example, if PURCHASE is the targetname and "YES" and "NO" are

the two values possible for targetvalue, the predicted value statistics are named P_PURYES and

P_PURNO.  



OUTFIT= SAS-data-set

Specifies the output data set with fit statistics. For more information, see .



OUTSTEP

Scores the data for each model selection step.



ROLE=role-value

Specifies the role of the DATA= data set. The ROLE= option primarily affects which fit statistics

are computed and what their names and labels are.

Role-value can be:

TRAIN


This value is the default when the same data set name is used in the DATA= option in both

the PROC and SCORE statements. Specifying TRAIN with any data set other than the

actual training set is an error.

VALID | VALIDATION

This value is the default when the DATA= data set name in the SCORE statement is the

same as the data set in the VALIDATA= in the PROC statement.

TEST

This value is the default when the DATA= data set name in the SCORE statement is the



same as the data set name in the TESTDATA= option of the PROC statement.

SCORE


Predicted values are produced but residuals, error functions, and other fit statistics are not

produced.

Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.




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