Victor M. Zammit <[email protected]>:
I don't understand this reply at all. Did you try the simulation
approach? It takes very little memory:
Contains data from \t.dta
obs: 100,000 simulate: makez
vars: 1 13 Nov 2008 11:04
size: 1,200,000 (99.9% of memory free)
g d=0
replace d=1.31 if t>1.31
replace d=1.697 if t>1.697
replace d=2.042 if t>2.042
replace d=2.457 if t>2.457
replace d=2.75 if t>2.75
tab d
d | Freq. Percent Cum.
------------+-----------------------------------
0 | 90,017 90.02 90.02
1.31 | 4,974 4.97 94.99
1.697 | 2,441 2.44 97.43
2.042 | 1,523 1.52 98.95
2.457 | 552 0.55 99.51
2.75 | 493 0.49 100.00
------------+-----------------------------------
Total | 100,000 100.00
_pctile t, nq(100)
ret li
r(r99) = 2.475399255752564
r(r95) = 1.697941184043884
r(r90) = 1.30941379070282
On Thu, Nov 13, 2008 at 12:21 PM, Victor M. Zammit <[email protected]> wrote:
> t-table.I understand that you need a number of random tries ,much bigger
> than that of forty thousand,to come to convergence.So I was wondering if
> that that constraint with memory ,could be handled.
>
>
> ----- Original Message -----
> From: "Austin Nichols" <[email protected]>
> To: <[email protected]>
> Sent: Thursday, November 13, 2008 4:09 PM
> Subject: Re: st: Re: Memory
>
>
>> Victor M. Zammit:
>> I also don't understand the point of this exercise--but you should
>> read -help simul- and try e.g.
>>
>> prog makez, rclass
>> syntax [, obs(integer 31)]
>> clear
>> set obs `obs'
>> gen a = invnorm(uniform())
>> ttest a=0
>> return scalar t=r(t)
>> end
>> simul, reps(100000) seed(123): makez
>>
>>
>> On Thu, Nov 13, 2008 at 9:56 AM, Nick Cox <[email protected]> wrote:
>> > I still don't understand what you are trying to do. But I can comment on
>> > your code.
>> >
>> > You are looping round 40,000 times and writing a single result to 40,000
>> > data files. Then you are looping round to put all those 40,000 data
>> > files in one.
>> >
>> > I'd do that directly this way using just one extra file:
>> >
>> > clear
>> > set obs 31
>> > gen a = .
>> > tempname out
>> > postfile `out' t using myresults.dta
>> > qui forval i = 1/40000 {
>> > replace a = invnorm(uniform())
>> > ttest a = 0
>> > post `out' (r(t))
>> > }
>> > postclose `out'
>> >
>> > I still doubt 40,000 is anywhere big enough to get an answer.
>> >
>> > Nick
>> > [email protected]
>> >
>> > Victor M. Zammit
>> >
>> > * a} The data that I have is from generating random samples of whatever
>> > size,in this case of size 31,from a normally distributed,infinitely
>> > large,
>> > population; ie
>> >
>> > local i = 1
>> >
>> > while `i'<= 40000 {
>> >
>> > drop _all
>> >
>> > set obs 31
>> >
>> > gen a = invnorm(uniform())
>> >
>> > qui ttest a = 0
>> >
>> > replace a = r(t) in 1
>> >
>> > keep in 1
>> >
>> > save a`i',replace
>> >
>> > local i = `i'+1
>> >
>> > }
>> >
>> > * I use 40000 due to memory constraint.Appending the a[i]'s together
>> > gives
>> > me a variable of 40000 observations ,ie
>> >
>> > use a1,clear
>> >
>> > local i = 2
>> >
>> > while `i'<= 40000 {
>> >
>> > append using a`i'.dta
>> >
>> > local i = `i'+1
>> >
>> > }
>> >
>> > save ais40000,replace
>> >
>> > * b) From ais40000.dta I get the density <= 1.31, presumably to get the
>> > density of 90% , <= 1.697 to get the density of 95% etc etc,according to
>> > the
>> > official ttable, ie
>> >
>> > capture program drop density
>> >
>> > program define density
>> >
>> > use ais40000,clear
>> >
>> > count if a<= `1'
>> >
>> > di " density >=" "`1'" " = " r(N)/40000
>> >
>> > end
>> >
>> > density 1.31
>> >
>> > density 1.697
>> >
>> > density 2.042
>> >
>> > density 2.457
>> >
>> > density 2.75
>> >
>> > * For smaller degrees of freedom,the discrepancy is much higher.I would
>> > like
>> > to know how if it is at all possible to resolve memory constraint .
*
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