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st: RE: RE: bootstrap with a user-contributed command
Assuming you are running version 2.0.5 or later of -diagt-, the problem appears to be that -diagt- calls -diagti-, wherein r(spec) and r(sens) are generated, then -diagt- reads these values and attempts to repost them. The values appear to get lost when -bootstrap- is wrapped around everything.
Version 2.0.0 of -diagt- does its own computations (i.e., doesn't call -diagti-), so it works fine with -bootstrap-:
. diagti 80 17 11 44
True |
disease | Test result
status | Pos. Neg. | Total
-----------+----------------------+----------
Abnormal | 80 17 | 97
Normal | 11 44 | 55
-----------+----------------------+----------
Total | 91 61 | 152
[95% Conf. Inter.]
-------------------------------------------------------------------------
Sensitivity Pr( +| D) 82.47% 73.43% 89.45%
Specificity Pr( -|~D) 80.00% 67.03% 89.57%
Positive predictive value Pr( D| +) 87.91% 79.40% 93.81%
Negative predictive value Pr(~D| -) 72.13% 59.17% 82.85%
-------------------------------------------------------------------------
Prevalence Pr(D) 63.82% 55.64% 71.44%
-------------------------------------------------------------------------
Likelihood Ratio of Pos. Test LR+ 4.12 2.41 7.05
Likelihood Ratio of Neg. Test LR- 0.22 0.14 0.34
-------------------------------------------------------------------------
. which diagt
h:\ado\diagt.ado
*! diagt 2.0.0 21 Feb 2001 PTS -- added DR and FPR
. bootstrap sens=r(sens) spec=r(spec): diagt true test
(running diagt on estimation sample)
Warning: Since diagt is not an estimation command or does not set e(sample),
bootstrap has no way to determine which observations are used in
calculating the statistics and so assumes that all observations are used.
This means no observations will be excluded from the resampling because of
missing values or other reasons.
If the assumption is not true, press Break, save the data, and drop the
observations that are to be excluded. Be sure that the dataset in memory
contains only the relevant data.
Bootstrap replications (50)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
Bootstrap results Number of obs = 152
Replications = 50
command: diagt true test
sens: r(sens)
spec: r(spec)
------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sens | 82.47423 3.475202 23.73 0.000 75.66296 89.2855
spec | 80 4.271629 18.73 0.000 71.62776 88.37224
------------------------------------------------------------------------------
. bootstrap sens=r(sens) spec=r(spec), reps(1000): diagt true test
(running diagt on estimation sample)
Warning: Since diagt is not an estimation command or does not set e(sample),
bootstrap has no way to determine which observations are used in
calculating the statistics and so assumes that all observations are used.
This means no observations will be excluded from the resampling because of
missing values or other reasons.
If the assumption is not true, press Break, save the data, and drop the
observations that are to be excluded. Be sure that the dataset in memory
contains only the relevant data.
Bootstrap replications (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.................................................. 50
.................................................. 100
.................................................. 150
.................................................. 200
.................................................. 250
.................................................. 300
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.................................................. 450
.................................................. 500
.................................................. 550
.................................................. 600
.................................................. 650
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.................................................. 1000
Bootstrap results Number of obs = 152
Replications = 1000
command: diagt true test
sens: r(sens)
spec: r(spec)
------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sens | 82.47423 3.88919 21.21 0.000 74.85155 90.0969
spec | 80 5.674551 14.10 0.000 68.87808 91.12192
------------------------------------------------------------------------------
-----------------------------------
Thomas J. Steichen
[email protected]
-----------------------------------
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Alessandro A. Leidi
Sent: Monday, May 11, 2009 1:13 PM
To: [email protected]
Subject: st: RE: bootstrap with a user-contributed command
I had already tried bootstrapping -diagt- directly without a user-written program, see below.
-bootstrap- squawks like it does when used with -rclass- functions but it does not deliver for -diagt-.
Of course it may be my mistake, but where am I going wrong?
Thank you for your consideration.
Sandro Leidi
[email protected]
. quietly diagti 80 17 11 44
. desc
Contains data
obs: 152
vars: 2
size: 912 (99.9% of memory free)
--------------------------------------------------------------------------------------------------------------- storage display value
variable name type format label variable label
---------------------------------------------------------------------------------------------------------------true byte %8.0g true True disease status
test byte %8.0g test Test result
---------------------------------------------------------------------------------------------------------------Sorted by:
Note: dataset has changed since last saved
. bootstrap sens=r(sens) spec=r(spec): diagt true test
(running diagt on estimation sample)
Warning: Since diagt is not an estimation command or does not set e(sample), bootstrap has no way to determine which observations are used in calculating the statistics and so assumes that all observations are used. This means no observations will be excluded from the resampling because of missing values or other reasons.
If the assumption is not true, press Break, save the data, and drop the observations that are to be excluded.
Be sure that the dataset in memory contains only the relevant data.
Bootstrap replications (50)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
post __000002 not found
post __000002 not found
r(111);
.
st: RE: bootstrap with a user-contributed command
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