code segmented regression sas

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  • 8/19/2019 Code Segmented Regression SAS

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    SAS Code

    /* Set the graphics environment */

    goptions reset=all border cback=white htitle=12pt htext=10pt;

    FILENAME REFFILE "/home/suryachaitanya1/MyFolder/Switch_Data_Case.csv";

    /* Segmented Regression analysis in SAS */

    data WORK.TRAFFIC ;

    %let _EFIERR_ = 0; /* set the ERROR detection macro variable */

    infile REFFILE delimiter = ',' MISSOVER DSD firstobs=11 ;

    informat VAR1 anydtdtm40. ;

    informat VAR2 18.;

    informat VAR3 18.;informat VAR4 18.;

    informat VAR5 18.;

    format VAR1 datetime. ;

    format VAR2 18.;

    format VAR3 18.;

    format VAR4 18.;

    format VAR5 18.;

    input

    VAR1

    VAR2 $VAR3 $

    VAR4 $

    VAR5 $

    ;

    if _ERROR_ then call symputx('_EFIERR_',1); /* set ERROR detection macro variable

    */

    run;

    data traffic(rename = (VAR1 = Date VAR2 = Inbound VAR3=col2_cdefa VAR4=Outbound VAR5

    =col4_cdeff));

    set traffic;

    run;

    data traffic;

    set traffic;

    total_traffic=Inbound + Outbound;

    run;

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    data traffic2;

    set traffic;

    if datepart(Date) in ('13AUG15'd,'05NOV15'd) then do;

    total_traffic = .;

    end;run;

    data traffic2;

    set traffic2;

    if _n_ =1 then do;

    time = 1;

    retain time;

    end;

    else do;

    time = time + 1;retain time;

    end;

    if datepart(Date) ge '27Aug15'd then do;

    event = 1;

    end;

    else do;

    event = 0;

    end;

    run;

    data traffic2;

    set traffic2;

    if datepart(date) eq '27Aug15'd then do;

    time_from_event = 0;

    retain time_from_event;

    end;

    if datepart(date) gt '27Aug15'd then do;

    time_from_event = time_from_event + 1;

    retain time_from_event;

    end;

    if datepart(date) lt '27Aug15'd then do;

    time_from_event = 0;

    end;

    run;

    data traffic2;

    set traffic2;

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    time2= datepart(date)-'27Aug15'd;

    run;

    %macro traffic(n=);

    Proc Autoreg Data = traffic2 outest= param_estimates_autoreg;model &n = time2 event time_from_event/dwprob

    run;

    proc arima data=traffic2;

    identify var=&n stationarity=(dickey=0);

    quit;run;

    Proc Autoreg Data = traffic2 outest= param_estimates_autoreg;

    model &n = time2 event time_from_event/

    method = ml NLAG = 6 Backstep DWPROB LOGLIKL;output out = out1 p=pvar r = rvar;

    run;

    %mend;

    %traffic(n=total_traffic);

    *%traffic(n=InBound);

    *%traffic(n=OutBound);

    R Code

    install.packages("zoo")

    install.packages("fpp")

    install.packages("forecast")

    library(plyr)

    library(zoo)

    library(fpp)

    library(forecast)

    # Load Data Into R

    switchcase

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    # This is with out the first 9 rows

    switchcase2

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    stationarity