More literally,

NOTE: Argument 4 to function CDF at line 3 column 5 is invalid. is referring to the function provided

log(cdf('normal',_x&N{i},mu,sd)

The output tables that inserts -1 * sample size for the column of AD or answer to

AD = -&N - S;

is i n c o r r e c t, where S is probably set to zero b.c. 'argument 4' of CDF is invalid.

On Sat, Sep 5, 2009 at 6:18 AM, OR Stats <stats112@gmail.com> wrote:

> It now creates the datasets. But the S column is just all zeros and AD > column is all -samplesize (i.e., -50, -100, -200 etc.) > > the error log now is > > NOTE: Argument 4 to function CDF at line 3 column 5 is invalid. > > NOTE: Argument 4 to function CDF at line 3 column 52 is invalid. > > On Sat, Sep 5, 2009 at 12:39 AM, Dale McLerran <stringplayer_2@yahoo.com > > wrote: > >> My mistake. There was a legacy reference to array X >> from when you had asked first asked how to compute the >> A-D test for a distribution which you wish to specify. >> We now have four different arrays of various lengths. >> The macro should reference the array of the length >> currently being simulated. In order to reference the >> correct array, replace the code >> >> S + ((2*i - 1)/&N) * (log(cdf('normal',x{i},mu,sd)) + >> log(1 - cdf('normal',x{&N+1-i},mu,sd))); >> >> with >> >> S + ((2*i - 1)/&N) * (log(cdf('normal',_x&N{i},mu,sd)) + >> log(1 - cdf('normal',_x&N{&N+1-i},mu,sd))); >> >> Dale >> >> --------------------------------------- >> Dale McLerran >> Fred Hutchinson Cancer Research Center >> mailto: dmclerra@NO_SPAMfhcrc.org >> Ph: (206) 667-2926 >> Fax: (206) 667-5977 >> --------------------------------------- >> >> >> --- On Fri, 9/4/09, OR Stats <stats112@GMAIL.COM> wrote: >> >> > From: OR Stats <stats112@GMAIL.COM> >> > Subject: Re: Fastest Steps for Simulating: Anderson-Darling Goodness of >> Fit test for Non-typical distn >> > To: SAS-L@LISTSERV.UGA.EDU >> > Date: Friday, September 4, 2009, 7:34 PM >> > cool, good. The undeclared >> > array is still giving problems >> > >> > ERROR: Undeclared array referenced: x. >> > >> > ERROR: Variable x has not been declared as an array. >> > >> > ERROR: Undeclared array referenced: x. >> > >> > ERROR: Variable x has not been declared as an array. >> > >> > 1218 %AD(N=100) >> > >> > >> > On Fri, Sep 4, 2009 at 9:28 PM, Data _null_; <iebupdte@gmail.com> >> > wrote: >> > >> > > That is incorrect syntax for an iterative DO. >> > You need. >> > > >> > > do s=5,5.2,5.4; >> > > >> > > On 9/4/09, OR Stats <stats112@gmail.com> >> > wrote: >> > > > Hmm... still same error >> > > > 1124 do S=[5 5.2 5.4]; /* This line needs correct >> > specification */ >> > > > - >> > > > 386 >> > > > - >> > > > 200 >> > > > >> > > > ERROR 386-185: Expecting an arithmetic >> > expression. >> > > > >> > > > ERROR 200-322: The symbol is not recognized and >> > will be ignored. >> > > > >> > > > On Fri, Sep 4, 2009 at 9:15 PM, OR Stats <stats112@gmail.com> >> > wrote: >> > > > >> > > > > Ok. Too much coding on a Friday! >> > Thx!! >> > > > > >> > > > > >> > > > > >> > > > > >> > > > > >> > > > > On Fri, Sep 4, 2009 at 9:13 PM, Data _null_; >> > <iebupdte@gmail.com> >> > > wrote: >> > > > > >> > > > > > From your original post.... >> > > > > > >> > > > > > >> > > > > > > 1 Million times using 50, 100, >> > 200, and 300 rows of data at each >> > > > > > > iteration for three different >> > values of s (s1, s2, s3)? >> > > > > > >> > > > > > >> > > > > > >> > > > > > >> > > > > > >> > > > > > On 9/4/09, OR Stats <stats112@gmail.com> >> > wrote: >> > > > > > > Not sure what S1 S2 and S3 are >> > referring to? >> > > > > > > >> > > > > > > >> > > > > > > On Fri, Sep 4, 2009 at 8:56 PM, >> > Data _null_; <iebupdte@gmail.com> >> > > > wrote: >> > > > > > > > Did you notice this >> > comment... >> > > > > > > > >> > > > > > > > >> > > > > > > > /* This line needs >> > correct specification */ >> > > > > > > > >> > > > > > > > >> > > > > > > > >> > > > > > > > >> > > > > > > > >> > > > > > > > On 9/4/09, OR Stats <stats112@gmail.com> >> > wrote: >> > > > > > > > > I am getting the >> > following error msg's >> > > > > > > > > >> > > > > > > > > do S={S1 S2 S3}; /* This >> > line needs correct specification */ >> > > > > > > > > >> > > > > > > > > >> > - >> > > > > > > > > >> > > > > > > > > >> > 386 >> > > > > > > > > >> > > > > > > > > >> > 76 >> > > > > > > > > >> > > > > > > > > >> > -- >> > > > > > > > > >> > > > > > > > > >> > 202 >> > > > > > > > > >> > > > > > > > > ERROR 386-185: Expecting >> > an arithmetic expression. >> > > > > > > > > >> > > > > > > > > ERROR 76-322: Syntax >> > error, statement will be ignored. >> > > > > > > > > >> > > > > > > > > ERROR 202-322: The >> > option or parameter is not recognized and >> > > will >> > > > be >> > > > > > > > > ignored. >> > > > > > > > > ERROR: Undeclared >> > array referenced: x. >> > > > > > > > > >> > > > > > > > > ERROR: Variable x has >> > not been declared as an array. >> > > > > > > > > >> > > > > > > > > >> > > > > > > > > And what is S for as the >> > 2nd statement of ranuni(p,S)? >> > > > > > > > > >> > > > > > > > > >> > > > > > > > > On Sun, Aug 30, 2009 at >> > 11:19 PM, Dale McLerran >> > > > > > > <stringplayer_2@yahoo.com>wrote: >> > > > > > > > > >> > > > > > > > > > One million >> > times? Why? I really think that is overkill. >> > > > > > > > > > I would try to >> > cover more parameter combinations if it were >> > > > > > > > > > me. >> > > > > > > > > > >> > > > > > > > > > But you should be >> > able to use a single data step to generate >> > > > > > > > > > A-D statistics for >> > all of your parameter combinations. The >> > > > > > > > > > code below should >> > be pretty efficient. >> > > > > > > > > > >> > > > > > > > > > >> > > > > > > > > > %macro AD(N=); >> > > > > > > > > > do i=1 to >> > &N; >> > > > > > > > > > >> > /* The next line needs completion with the >> > appropriate G >> > > */ >> > > > > > > > > > >> > _x&N{i} = G(ranuni(6923479,S)); >> > > > > > > > > > end; >> > > > > > > > > > >> > > > > > > > > > call sortn(of >> > _X&N(*)); >> > > > > > > > > > mu = mean(of >> > x1-x&N); >> > > > > > > > > > var = var(of >> > x1-x&N); >> > > > > > > > > > sd = >> > sqrt(var); >> > > > > > > > > > S=0; >> > > > > > > > > > do i=1 to >> > &N; >> > > > > > > > > > S + >> > ((2*i - 1)/&N) * (log(cdf('normal',x{i},mu,sd)) + >> > > > > > > > > > >> > log(1 - >> > > > cdf('normal',x{&N+1-i},mu,sd))); >> > > > > > > > > > end; >> > > > > > > > > > AD = -&N >> > - S; >> > > > > > > > > > output >> > AD_&N; >> > > > > > > > > > %mend; >> > > > > > > > > > >> > > > > > > > > > >> > > > > > > > > > >> > > > > > > > > > /* Generate 10000 >> > samples of same size (N=9 in this case) >> > > > following */ >> > > > > > > > > > /* a normal >> > distribution and compute AD statistic for each >> > > > sample. */ >> > > > > > > > > > data AD_50 >> > > > > > > > > > >> > AD_100 >> > > > > > > > > > >> > AD_200 >> > > > > > > > > > >> > AD_300; >> > > > > > > > > > array _x50 >> > {50} x1-x50; >> > > > > > > > > > array _x100 >> > {100} x1-x100; >> > > > > > > > > > array _x200 >> > {200} x1-x200; >> > > > > > > > > > array _X300 >> > {300} x1-x300; >> > > > > > > > > > do S={S1 S2 >> > S3}; /* This line >> > needs correct >> > > > specification */ >> > > > > > > > > > do >> > rep=1 to 10000; >> > > > > > > > > > >> > %AD(N=50) >> > > > > > > > > > >> > %AD(N=100) >> > > > > > > > > > >> > %AD(N=200) >> > > > > > > > > > >> > %AD(N=300) >> > > > > > > > > > end; >> > > > > > > > > > end; >> > > > > > > > > > keep S AD; >> > > > > > > > > > run; >> > > > > > > > > > >> > > > > > > > > > >> > > > > > > > > > /* Determine >> > probability of observed data */ >> > > > > > > > > > /* using simulated >> > data AD distribution. */ >> > > > > > > > > > proc sort >> > data=AD_50; >> > > > > > > > > > by S AD; >> > > > > > > > > > run; >> > > > > > > > > > >> > > > > > > > > > proc sort >> > data=AD_100; >> > > > > > > > > > by S AD; >> > > > > > > > > > run; >> > > > > > > > > > >> > > > > > > > > > proc sort >> > data=AD_200; >> > > > > > > > > > by S AD; >> > > > > > > > > > run; >> > > > > > > > > > >> > > > > > > > > > proc sort >> > data=AD_300; >> > > > > > > > > > by S AD; >> > > > > > > > > > run; >> > > > > > > > > > >> > > > > > > > > > >> > > > > > > > > > The above is >> > untested code and should be tested with a >> > > > > > > > > > small number of >> > replicates before using it for a final >> > > > > > > > > > simulation. >> > Also, there will obviously need to be some >> > > > > > > > > > final step where >> > you determine the quantiles of the AD >> > > > > > > > > > statistics. >> > > > > > > > > > >> > > > > > > > > > Dale >> > > > > > > > > > >> > > > > > > > > > >> > --------------------------------------- >> > > > > > > > > > Dale McLerran >> > > > > > > > > > Fred Hutchinson >> > Cancer Research Center >> > > > > > > > > > mailto: dmclerra@NO_SPAMfhcrc.org >> > > > > > > > > > Ph: (206) >> > 667-2926 >> > > > > > > > > > Fax: (206) >> > 667-5977 >> > > > > > > > > > >> > --------------------------------------- >> > > > > > > > > > >> > > > > > > > > > >> > > > > > > > > > --- On Sun, >> > 8/30/09, OR Stats <stats112@GMAIL.COM> >> > wrote: >> > > > > > > > > > >> > > > > > > > > > > From: OR Stats >> > <stats112@GMAIL.COM> >> > > > > > > > > > > Subject: >> > Fastest Steps for Simulating: Anderson-Darling >> > > > Goodness of >> > > > > > > Fit >> > > > > > > > > > test for >> > Non-typical distn >> > > > > > > > > > > To: SAS-L@LISTSERV.UGA.EDU >> > > > > > > > > > > Date: Sunday, >> > August 30, 2009, 8:14 PM >> > > > > > > > > > > This is >> > good. I am ready now to run a large scale >> > > > simulation. >> > > > > > > What >> > > > > > > > > > that >> > > > > > > > > > > means is that >> > I want to compute the goodness of fit >> > > statistic >> > > > for (M >> > > > > > > x >> > > > > > > > > > > S) groups and >> > n times each group. >> > > > > > > > > > > >> > > > > > > > > > > Group defined >> > by (m,s); S = s1 s2 s3 and M = 50 100 200 >> > > 300. >> > > > > > > Basically, >> > > > > > > > > > > M is my >> > different sample sizes for which I am testing their >> > > > fit to >> > > > > > > > > > > function >> > G(random#,s) (i.e., inverse distribution). I >> > > would >> > > > like to >> > > > > > > run >> > > > > > > > > > > each group 1 >> > million times. For each s group, by >> > > generating >> > > > random >> > > > > > > > > > > numbers just >> > by 300 x 1million times, I'll have enough >> > > > simulated >> > > > > > > data >> > > > > > > > > > > y(s) to use >> > for the largest and smaller sample sizes. >> > > > > > > > > > > >> > > > > > > > > > > My final >> > column space would look like >> > > > > > > > > > > >> > i ranuni y_s1=G(ranuni,s1) y_s2=G(ranuni,s2) >> > > > > > > y_s3=G(ranuni,s3) >> > > > > > > > > > > 1 >> > > > > > > > > > > . >> > > > > > > > > > > . >> > > > > > > > > > > . >> > > > > > > > > > > m >> > > > > > > > > > > All rows in >> > the above table would be used to caculate >> > > function >> > > > f_s1, >> > > > > > > > > > > f_s2, f_s3 >> > (i.e., AD). This last step is repeated 1 >> > > Million >> > > > times. >> > > > > > > > > > > >> > > > > > > > > > > Can we do this >> > in one to two DATA STEPS? Which syntax >> > > would >> > > > be >> > > > > > > fastest >> > > > > > > > > > > since we have >> > to generate 300 Million random numbers, from >> > > > which we >> > > > > > > would >> > > > > > > > > > > split the >> > sample by 1 Million disjoint sets that we would >> > > then >> > > > > > > compute a >> > > > > > > > > > > statistic 1 >> > Million times using 50, 100, 200, and 300 rows >> > > of >> > > > data >> > > > > > > at >> > > > > > > > > > > each iteration >> > for three different values of s (s1, s2, >> > > s3)? >> > > > > > > > > > > >> > > > > > > > > > > Thank Q! >> > > > > > > > > > >> > > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > > >> > > > > > >> > > > > >> > > > > >> > > > >> > > > >> > > >> > >> > >