Anyone familiar with the basic syntax of C programs can learn to use the C-style features of Dap quickly and easily from the manual and the examples contained in it; advanced features of C are not necessary, although they are available. (The manual contains a brief introduction to the C syntax needed for Dap.) Because Dap processes files one line at a time, rather than reading entire files into memory, it can be, and has been, used on data sets that have very many lines and/or very many variables.
I wrote Dap to use in my statistical consulting practice because the utterly famous, industry standard statistics system (you know the one I mean) is not available on GNU/Linux and costs a bundle every year under a lease arrangement. And now you can run programs written for that system directly on Dap! I was generally happy with that system, except for the graphics, which are all but impossible to use, but there were a number of clumsy constructs left over from its ancient origins. Thus, I decided to mimic the core of the functionality of that system in the context of the C language, which allows much more programming flexibility.
Dap is a GNU program and is free software distributed under a GNU-style copyleft.
The following instructions tell you how to install Dap.
GENERALITIES
This document concerns building and installing Dap from sources.
Dap will configure and build under a number of common Unix-like
platforms.The
directions here are for GNU/Linux; the configure and build for other
platforms are similar.
There are two mailing lists regarding Dap, one for bugs and one for comments, which can be accessed through http://mail.gnu.org/archive/html/bug-dap and http://mail.gnu.org/archive/html/dap-users. These lists only accept PLAIN TEXT. Postings to bug-dap must contain the word "bug".
Dap is a GNU program and is free software distributed under a GNU-style copyleft. See the file COPYING for details.
GETTING AND UNPACKING THE SOURCES
The simplest way is to download the most recent `dap-x.y.tar.gz' package into a directory that we'll call `DAP_HOME' and untar it with:
gunzip -c dap-x.y.tar.gz | tar -xvf -
This should create directories `src', `doc', and `examples'.
COMPILATION
If you want the executables, includes, library, and info files installed in subdirectories `bin', `include', `lib', and `info', respectively, of `/usr/local', then simply issue the following commands:
./configure
make install
Otherwise, after the `./configure' command, edit the `Makefile' files in DAP_HOME, DAP_HOME/src, and DAP_HOME/doc to select a different place to install before you type the `make install' command. Note: when DAP_HOME/src/dap.c compiles, you will get a warning:
implicit declaration of function `strcat'
and when DAP_HOME/src/dap0.c compiles, you will get a warning:
implicit declaration of function `dap_main'
Ignore these warnings. Now rehash.
ENVIRONMENT
The following environment variables are used by Dap:
DAPEDITOR
path name for Emacs
DAPEDOPTS
options for the Emacs front-end
DAPPAGER
for viewing tabular output from Dap
DAPPAGEOPTS options for that pager
DAPCOMPILER for compiling programs to run under Dap
DAPCOMPOPTS options for that compiler
DAPVIEWER for viewing graphical
output from Dap
DAPVIEWOPTS options for that viewer
DAPPP
path name for the Dap preprocessor
(default: /usr/local/bin/dappp)
DAPRUNS
path name for the Dap process that runs the
preprocessor and complier
(default: /usr/local/bin/dapruns)
INCDIR
directory for compiler to find <dap.h>
(default: /usr/local/include)
LIBDIR
directory for compiler to find libdap.a
(default: /usr/local/lib)
All but the last four are further documented in the manual.
READING THE MANUAL
To read the manual in info, you will need to have `/usr/local/info'
(or whatever directory you installed the info file in) in your
`INFOPATH'.
The
following command (which you will probably want to put in
yourshell's
`rc' file) will do that if your shell is `csh':
setenv INFOPATH ".:/usr/info:/usr/local/info"
Then issue the command:
info dap
If you prefer dvi or html manuals, they can be made from the file:
DAP_HOME/doc/dap.texi
(See the documentation for texi2dvi, dvips, and texinfo.)
The manual will tell you how to run and use dap. The program and data files for the examples in the manual are in the directory DAP_HOME/doc/examples.
MACHINE DEPENDENCY
Dap assumes that you have a machine with 64-bit double precision floating point numbers conforming to the IEEE floating point standard. If that is not the case, then you may have to modify `machdep.c'; good luck.
BUG REPORTS AND COMMENTS
Send bug reports to <bug-dap@gnu.org>. Postings to bug-dap must be in PLAIN TEXT and contain the word "bug".
If you use dap, please let me know about your experience using it,
and
suggestions, by mailing
to <dap-users@gnu.org>. Thanks.
Sample output [Back to Table of Contents]
The following are samples of tabular output from Dap. They, the programs that produced them, and graphical output (not shown here) are all provided with the distribution. These examples are from:
[AMD] Milliken, G.A. and Johnson, D.E. 1984. Analysis of
Messy
Data. Van Nostrand Reinhold: New York. 473pp.
[ED] Cochran, W.G. and Cox, G.M. 1957. Experimental
Designs.
John Wiley & Sons: New York. 611pp.
[MS] Bickel, P.J. and Doksum, K.A. 1977. Mathematical Statistics:
Basic
Ideas and Selected Topics. Holden-Day: Oakland. 493 pp.
[LM] Rao, C.R. and Toutenberg, H. 1995. Linear Models: Least
Squares
and Alternatives. Springer-Verlag: New York. 352 pp.
[CDA] Agresti, A. 1990. Categorical Data
Analysis.
John Wiley & Sons: New York. 558pp.
Unbalanced ANOVA Crossed, nested ANOVA Random model, unbalanced Mixed model, balanced Mixed model, unbalanced Split plot Latin square Missing treatment combinations Linear regression Linear regression, model building Ordinal cross-classification Stratified 2x2 tables Loglinear models Logit model for linear-by-linear association Logistic regression
Unbalanced ANOVA [Back
to Sample output]
AMD: pp. 128 - 134
=================================
Dap 1. Mon Jan 12 03:20:03 2004
Response variable: y
Treatment Levels
-------- ------
treat
treat1 treat2
block
block1 block2 block3
=================================
Dap 2. Mon Jan 12 03:20:03 2004
Testing Ho: treat block treat*block
Number of observations = 16
H0 SS = 238.937, df = 5, MS = 47.7875
Error SS = 20, df = 10, MS = 2
R-sq = 0.922761
F0 = 23.8937
Prob[F > F0] = 0.00003
=================================
Dap 3. Mon Jan 12 03:20:03 2004
Testing Ho: treat
Number of observations = 16
H0 SS = 61.7143, df = 1, MS = 61.7143
Error SS = 20, df = 10, MS = 2
F0 = 30.8571
Prob[F > F0] = 0.00025
=================================
Dap 4. Mon Jan 12 03:20:03 2004
Least-squares means for: treat
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 5. Mon Jan 12 03:20:04 2004
_stat_ for _lsm_ / treat
======================================================
|
|23 |27
|
|--------+--------+--------|------------+------------|
|_type_ |_LSMEAN_|_treat |treat1
|treat2 |
|========|========|========|============|============|
|EFFN |0
| | 7.71429|
7.71429|
|--------+--------+--------|------------+------------|
|LSMDIFF |23 |treat1 |
| 4|
| |--------+--------|------------+------------|
| |27
|treat2 | -4|
|
|--------+--------+--------|------------+------------|
|MINDIFF |23 |treat1 |
| 1.60445|
| |--------+--------|------------+------------|
| |27
|treat2 | 1.60445|
|
|--------+--------+--------|------------+------------|
|PROB |23 |treat1
| | 0.000242431|
| |--------+--------|------------+------------|
| |27
|treat2 | 0.000242431|
|
------------------------------------------------------
=================================
Dap 6. Mon Jan 12 03:20:04 2004
Testing Ho: block
Number of observations = 16
H0 SS = 77.1692, df = 2, MS = 38.5846
Error SS = 20, df = 10, MS = 2
F0 = 19.2923
Prob[F > F0] = 0.00037
=================================
Dap 7. Mon Jan 12 03:20:04 2004
Least-squares means for: block
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 8. Mon Jan 12 03:20:04 2004
_stat_ for _lsm_ / block
===================================================================
|
|23 |24
|28 |
|--------+--------+--------|------------+------------+------------|
|_type_ |_LSMEAN_|_block |block1
|block2 |block3
|
|========|========|========|============|============|============|
|EFFN |0
| |
4.8| 4.8|
6|
|--------+--------+--------|------------+------------+------------|
|LSMDIFF |23 |block1 |
| 1|
5|
| |--------+--------|------------+------------+------------|
| |24
|block2 | -1|
| 4|
| |--------+--------|------------+------------+------------|
| |28
|block3 | -5|
-4| |
|--------+--------+--------|------------+------------+------------|
|MINDIFF |23 |block1 |
| 2.03401| 1.92963|
| |--------+--------|------------+------------+------------|
| |24
|block2 | 2.03401|
| 1.92963|
| |--------+--------|------------+------------+------------|
| |28
|block3 | 1.92963|
1.92963|
|
|--------+--------+--------|------------+------------+------------|
|PROB |23 |block1
| |
0.299003| 0.000179317|
| |--------+--------|------------+------------+------------|
| |24
|block2 | 0.299003|
| 0.000952328|
| |--------+--------|------------+------------+------------|
| |28
|block3 | 0.000179317| 0.000952328|
|
-------------------------------------------------------------------
=================================
Dap 9. Mon Jan 12 03:20:04 2004
Testing Ho: treat*block
Number of observations = 16
H0 SS = 71.6308, df = 2, MS = 35.8154
Error SS = 20, df = 10, MS = 2
F0 = 17.9077
Prob[F > F0] = 0.00050
Crossed, nested ANOVA [Back to Sample output]
AMD: pp. 249 - 251
=================================
Dap 1. Mon Jan 12 03:20:11 2004
Response variable: y
Treatment Levels
-------- ------
a
1 2
b
1 2
c
1 2
=================================
Dap 2. Mon Jan 12 03:20:11 2004
Testing Ho: a b a*b b*c a*b*c
Number of observations = 16
H0 SS = 845.438, df = 7, MS = 120.777
Error SS = 6.5, df = 8, MS = 0.8125
R-sq = 0.99237
F0 = 148.648
Prob[F > F0] = 0.00001
=================================
Dap 3. Mon Jan 12 03:20:11 2004
Testing Ho: a
Number of observations = 16
H0 SS = 39.0625, df = 1, MS = 39.0625
Error SS = 6.5, df = 8, MS = 0.8125
F0 = 48.0769
Prob[F > F0] = 0.00013
=================================
Dap 4. Mon Jan 12 03:20:11 2004
Testing Ho: b
Number of observations = 16
H0 SS = 770.062, df = 1, MS = 770.062
Error SS = 6.5, df = 8, MS = 0.8125
F0 = 947.769
Prob[F > F0] = 0.00001
=================================
Dap 5. Mon Jan 12 03:20:11 2004
Testing Ho: a*b
Number of observations = 16
H0 SS = 10.5625, df = 1, MS = 10.5625
Error SS = 6.5, df = 8, MS = 0.8125
F0 = 13
Prob[F > F0] = 0.00693
=================================
Dap 6. Mon Jan 12 03:20:11 2004
Testing Ho: c*b
Number of observations = 16
H0 SS = 24.125, df = 2, MS = 12.0625
Error SS = 6.5, df = 8, MS = 0.8125
F0 = 14.8462
Prob[F > F0] = 0.00203
=================================
Dap 7. Mon Jan 12 03:20:11 2004
Testing Ho: a*c*b
Number of observations = 16
H0 SS = 1.625, df = 2, MS = 0.8125
Error SS = 6.5, df = 8, MS = 0.8125
F0 = 1
Prob[F > F0] = 0.40960
=================================
Dap 8. Mon Jan 12 03:20:11 2004
Testing Ho: a
Denominator: a * b
EMS(a*b) =
4 Var(a*b)
1 Var(Error)
EMS(a) =
8 Var(a)
4 Var(a*b)
1 Var(Error)
Error for a =
1 MS(a*b)
Number of observations = 16
H0 SS = 39.0625, df = 1, MS = 39.0625
Residual df = 1, MS = 10.5625
F0 = 3.69822
Prob[F > F0] = 0.30528
=================================
Dap 9. Mon Jan 12 03:20:11 2004
Testing Ho: b
Denominator: a * b b * c a * b * c
EMS(a*b) =
4 Var(a*b)
2 Var(a*b*c)
1 Var(Error)
EMS(b*c) =
4 Var(b*c)
2 Var(a*b*c)
1 Var(Error)
EMS(a*b*c) =
2 Var(a*b*c)
1 Var(Error)
EMS(b) =
8 Var(b)
4 Var(a*b)
4 Var(b*c)
2 Var(a*b*c)
1 Var(Error)
Error for b =
1 MS(a*b)
1 MS(b*c)
-1 MS(a*b*c)
Number of observations = 16
H0 SS = 770.062, df = 1, MS = 770.062
Residual df = 2.57671, MS = 21.8125
F0 = 35.3037
Prob[F > F0] = 0.01701
=================================
Dap 10. Mon Jan 12 03:20:11 2004
Testing Ho: a * b
Denominator: a * b * c
EMS(a*b*c) =
2 Var(a*b*c)
1 Var(Error)
EMS(a*b) =
4 Var(a*b)
2 Var(a*b*c)
1 Var(Error)
Error for a*b =
1 MS(a*b*c)
Number of observations = 16
H0 SS = 10.5625, df = 1, MS = 10.5625
Residual df = 2, MS = 0.8125
F0 = 13
Prob[F > F0] = 0.06906
=================================
Dap 11. Mon Jan 12 03:20:11 2004
Testing Ho: c * b
Denominator: a * c * b
EMS(a*b*c) =
2 Var(a*b*c)
1 Var(Error)
EMS(b*c) =
4 Var(b*c)
2 Var(a*b*c)
1 Var(Error)
Error for b*c =
1 MS(a*b*c)
Number of observations = 16
H0 SS = 24.125, df = 2, MS = 12.0625
Residual df = 2, MS = 0.8125
F0 = 14.8462
Prob[F > F0] = 0.06311
Random model, unbalanced [Back to Sample output]
AMD: pp. 265 - 273
=================================
Dap 1. Mon Jan 12 03:20:12 2004
Response variable: efficiency
Treatment Levels
-------- ------
plant
1 2 3
site
1 2 3 4
worker 1
2 3
=================================
Dap 2. Mon Jan 12 03:20:13 2004
Testing Ho: plant plant*site plant*worker plant*site*worker
Number of observations = 118
H0 SS = 10046.9, df = 35, MS = 287.054
Error SS = 408.617, df = 82, MS = 4.98313
R-sq = 0.960919
F0 = 57.6052
Prob[F > F0] = 0.00001
=================================
Dap 3. Mon Jan 12 03:20:13 2004
Testing Ho: plant
Number of observations = 118
H0 SS = 3866.33, df = 2, MS = 1933.16
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 387.942
Prob[F > F0] = 0.00001
=================================
Dap 4. Mon Jan 12 03:20:13 2004
Testing Ho: plant*worker
Number of observations = 118
H0 SS = 1949.66, df = 6, MS = 324.943
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 65.2085
Prob[F > F0] = 0.00001
=================================
Dap 5. Mon Jan 12 03:20:13 2004
Testing Ho: plant*site
Number of observations = 118
H0 SS = 610.302, df = 9, MS = 67.8114
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 13.6082
Prob[F > F0] = 0.00001
=================================
Dap 6. Mon Jan 12 03:20:13 2004
Testing Ho: plant*site*worker
Number of observations = 118
H0 SS = 1921.29, df = 18, MS = 106.738
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 21.4199
Prob[F > F0] = 0.00001
=================================
Dap 7. Mon Jan 12 03:20:13 2004
Testing Ho: site * plant
Denominator: site * worker * plant
EMS(plant*site*worker) =
2.85695 Var(plant*site*worker)
1 Var(Error)
EMS(plant*site) =
8.0468 Var(plant*site)
2.68227 Var(plant*site*worker)
1 Var(Error)
Error for plant*site =
0.0611422 MS(Error)
0.938858 MS(plant*site*worker)
Number of observations = 118
H0 SS = 610.302, df = 9, MS = 67.8114
Residual df = 18.1096, MS = 100.517
F0 = 0.674628
Prob[F > F0] = 0.72173
=================================
Dap 8. Mon Jan 12 03:20:13 2004
Response variable: efficiency
Treatment Levels
-------- ------
plant
1 2 3
site
1 2 3 4
worker 1
2 3
=================================
Dap 9. Mon Jan 12 03:20:13 2004
Testing Ho: plant plant*worker plant*site*worker
Number of observations = 118
H0 SS = 10046.9, df = 35, MS = 287.054
Error SS = 408.617, df = 82, MS = 4.98313
R-sq = 0.960919
F0 = 57.6052
Prob[F > F0] = 0.00001
=================================
Dap 10. Mon Jan 12 03:20:14 2004
Testing Ho: plant
Number of observations = 118
H0 SS = 3866.33, df = 2, MS = 1933.16
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 387.942
Prob[F > F0] = 0.00001
=================================
Dap 11. Mon Jan 12 03:20:14 2004
Testing Ho: plant*worker
Number of observations = 118
H0 SS = 1949.66, df = 6, MS = 324.943
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 65.2085
Prob[F > F0] = 0.00001
=================================
Dap 12. Mon Jan 12 03:20:14 2004
Testing Ho: site*worker*plant
Number of observations = 118
H0 SS = 2677.73, df = 27, MS = 99.1752
Error SS = 408.617, df = 82, MS = 4.98313
F0 = 19.9022
Prob[F > F0] = 0.00001
=================================
Dap 13. Mon Jan 12 03:20:14 2004
Testing Ho: worker * plant
Denominator: site * worker * plant
EMS(plant*site*worker) =
3.06428 Var(plant*site*worker)
1 Var(Error)
EMS(plant*worker) =
9.45329 Var(plant*worker)
2.36332 Var(plant*site*worker)
1 Var(Error)
Error for plant*worker =
0.22875 MS(Error)
0.77125 MS(plant*site*worker)
Number of observations = 118
H0 SS = 1949.66, df = 6, MS = 324.943
Residual df = 27.8087, MS = 77.6288
F0 = 4.18585
Prob[F > F0] = 0.00403
=================================
Dap 14. Mon Jan 12 03:20:14 2004
Testing Ho: plant
Denominator: worker * plant site * worker * plant
EMS(plant*worker) =
9.45329 Var(plant*worker)
2.36332 Var(plant*site*worker)
1 Var(Error)
EMS(plant*site*worker) =
3.06428 Var(plant*site*worker)
1 Var(Error)
EMS(plant) =
27.598 Var(plant)
9.19933 Var(plant*worker)
2.29983 Var(plant*site*worker)
1 Var(Error)
Error for plant =
0.0268648 MS(Error)
0.973135 MS(plant*worker)
Number of observations = 118
H0 SS = 3866.33, df = 2, MS = 1933.16
Residual df = 6.00508, MS = 316.347
F0 = 6.1109
Prob[F > F0] = 0.03567
Mixed model, balanced [Back to Sample output]
AMD: pp. 285-289
=================================
Dap 1. Mon Jan 12 03:20:16 2004
Response variable: productivity
Treatment Levels
-------- ------
machine 1 2 3
person 1
2 3 4 5 6
=================================
Dap 2. Mon Jan 12 03:20:16 2004
Testing Ho: machine person machine*person
Number of observations = 54
H0 SS = 3423.69, df = 17, MS = 201.393
Error SS = 33.2867, df = 36, MS = 0.92463
R-sq = 0.990371
F0 = 217.81
Prob[F > F0] = 0.00001
=================================
Dap 3. Mon Jan 12 03:20:16 2004
Testing Ho: machine
Number of observations = 54
H0 SS = 1755.26, df = 2, MS = 877.632
Error SS = 33.2867, df = 36, MS = 0.92463
F0 = 949.171
Prob[F > F0] = 0.00001
=================================
Dap 4. Mon Jan 12 03:20:16 2004
Testing Ho: person
Number of observations = 54
H0 SS = 1241.89, df = 5, MS = 248.379
Error SS = 33.2867, df = 36, MS = 0.92463
F0 = 268.625
Prob[F > F0] = 0.00001
=================================
Dap 5. Mon Jan 12 03:20:16 2004
Testing Ho: machine*person
Number of observations = 54
H0 SS = 426.53, df = 10, MS = 42.653
Error SS = 33.2867, df = 36, MS = 0.92463
F0 = 46.1298
Prob[F > F0] = 0.00001
=================================
Dap 6. Mon Jan 12 03:20:16 2004
Testing Ho: person
Denominator: machine * person
EMS(machine*person) =
3 Var(machine*person)
1 Var(Error)
EMS(person) =
9 Var(person)
3 Var(machine*person)
1 Var(Error)
Error for person =
1 MS(machine*person)
Number of observations = 54
H0 SS = 1241.89, df = 5, MS = 248.379
Residual df = 10, MS = 42.653
F0 = 5.82325
Prob[F > F0] = 0.00895
=================================
Dap 7. Mon Jan 12 03:20:16 2004
Testing Ho: machine
Denominator: machine * person
EMS(machine*person) =
3 Var(machine*person)
1 Var(Error)
EMS(machine) =
18 Var(machine)
3 Var(machine*person)
1 Var(Error)
Error for machine =
1 MS(machine*person)
Number of observations = 54
H0 SS = 1755.26, df = 2, MS = 877.632
Residual df = 10, MS = 42.653
F0 = 20.5761
Prob[F > F0] = 0.00029
=================================
Dap 8. Mon Jan 12 03:20:16 2004
Least-squares means for: machine
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 9. Mon Jan 12 03:20:17 2004
_stat_ for _lsm_ / machine
===================================================================
|
|52.3556 |60.3222 |66.2722
|
|--------+--------+--------|------------+------------+------------|
|_type_ |_LSMEAN_|_machine|1
|2 |3
|
|========|========|========|============|============|============|
|EFFN |0
| |
18| 18|
18|
|--------+--------+--------|------------+------------+------------|
|LSMDIFF |52.3556 |1 |
| 7.96667| 13.9167|
| |--------+--------|------------+------------+------------|
| |60.3222 |2
| -7.96667|
| 5.95|
| |--------+--------|------------+------------+------------|
| |66.2722 |3
| -13.9167| -5.95|
|
|--------+--------+--------|------------+------------+------------|
|MINDIFF |52.3556 |1 |
| 4.85062| 4.85062|
| |--------+--------|------------+------------+------------|
| |60.3222 |2
| 4.85062|
| 4.85062|
| |--------+--------|------------+------------+------------|
| |66.2722 |3
| 4.85062| 4.85062|
|
|--------+--------+--------|------------+------------+------------|
|PROB |52.3556 |1
| |
0.00439263| 7.90648e-05|
| |--------+--------|------------+------------+------------|
| |60.3222 |2
| 0.00439263|
| 0.0210791|
| |--------+--------|------------+------------+------------|
| |66.2722 |3
| 7.90648e-05| 0.0210791|
|
-------------------------------------------------------------------
Mixed model, unbalanced [Back to Sample output]
AMD: pp. 290 - 295
=================================
Dap 1. Mon Jan 12 03:20:18 2004
Response variable: productivity
Treatment Levels
-------- ------
machine 1 2 3
person 1
2 3 4 5 6
=================================
Dap 2. Mon Jan 12 03:20:18 2004
Testing Ho: machine person machine*person
Number of observations = 44
H0 SS = 3061.74, df = 17, MS = 180.103
Error SS = 22.6867, df = 26, MS = 0.872564
R-sq = 0.992645
F0 = 206.406
Prob[F > F0] = 0.00001
=================================
Dap 3. Mon Jan 12 03:20:18 2004
Testing Ho: machine
Number of observations = 44
H0 SS = 1238.2, df = 2, MS = 619.099
Error SS = 22.6867, df = 26, MS = 0.872564
F0 = 709.517
Prob[F > F0] = 0.00001
=================================
Dap 4. Mon Jan 12 03:20:18 2004
Testing Ho: person
Number of observations = 44
H0 SS = 1011.05, df = 5, MS = 202.211
Error SS = 22.6867, df = 26, MS = 0.872564
F0 = 231.743
Prob[F > F0] = 0.00001
=================================
Dap 5. Mon Jan 12 03:20:18 2004
Testing Ho: machine*person
Number of observations = 44
H0 SS = 404.315, df = 10, MS = 40.4315
Error SS = 22.6867, df = 26, MS = 0.872564
F0 = 46.3364
Prob[F > F0] = 0.00001
=================================
Dap 6. Mon Jan 12 03:20:19 2004
Testing Ho: person
Denominator: machine * person
EMS(machine*person) =
2.31622 Var(machine*person)
1 Var(Error)
EMS(person) =
6.72245 Var(person)
2.24082 Var(machine*person)
1 Var(Error)
Error for person =
0.0325538 MS(Error)
0.967446 MS(machine*person)
Number of observations = 44
H0 SS = 1011.05, df = 5, MS = 202.211
Residual df = 10.0145, MS = 39.1437
F0 = 5.16586
Prob[F > F0] = 0.01334
=================================
Dap 7. Mon Jan 12 03:20:19 2004
Testing Ho: machine
Denominator: machine * person
EMS(machine*person) =
2.31622 Var(machine*person)
1 Var(Error)
EMS(machine) =
12.8219 Var(machine)
2.13699 Var(machine*person)
1 Var(Error)
Error for machine =
0.0773812 MS(Error)
0.922619 MS(machine*person)
Number of observations = 44
H0 SS = 1238.2, df = 2, MS = 619.099
Residual df = 10.0362, MS = 37.3704
F0 = 16.5666
Prob[F > F0] = 0.00067
=================================
Dap 8. Mon Jan 12 03:20:19 2004
Least-squares means for: machine
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 9. Mon Jan 12 03:20:19 2004
_stat_ for _lsm_ / machine
===================================================================
|
|52.3136 |60.022
|66.2722 |
|--------+--------+--------|------------+------------+------------|
|_type_ |_LSMEAN_|_machine|1
|2 |3
|
|========|========|========|============|============|============|
|EFFN |0
| | 11.4088|
12.8061| 18|
|--------+--------+--------|------------+------------+------------|
|LSMDIFF |52.3136 |1 |
| 7.7084| 13.9586|
| |--------+--------|------------+------------+------------|
| |60.022 |2
| -7.7084|
| 6.25021|
| |--------+--------|------------+------------+------------|
| |66.2722 |3
| -13.9586| -6.25021|
|
|--------+--------+--------|------------+------------+------------|
|MINDIFF |52.3136 |1 |
| 5.54278| 5.15225|
| |--------+--------|------------+------------+------------|
| |60.022 |2
| 5.54278|
| 4.97724|
| |--------+--------|------------+------------+------------|
| |66.2722 |3
| 5.15225| 4.97724|
|
|--------+--------+--------|------------+------------+------------|
|PROB |52.3136 |1
| |
0.0112602| 0.000124789|
| |--------+--------|------------+------------+------------|
| |60.022 |2
| 0.0112602|
| 0.0188437|
| |--------+--------|------------+------------+------------|
| |66.2722 |3
| 0.000124789| 0.0188437|
|
-------------------------------------------------------------------
Split plot [Back to Sample output]
AMD: pp. 297 - 308
=================================
Dap 1. Mon Jan 12 02:33:16 2004
Whole plot (block, fertilizer) analysis
Response variable: yield
Treatment Levels
-------- ------
fertilizer 1 2 3 4
block
1 2
variety 1 2
=================================
Dap 2. Mon Jan 12 02:33:16 2004
Whole plot (block, fertilizer) analysis
Testing Ho: fertilizer block
Denominator: fertilizer*block variety fertilizer*variety block*variety
fertilizer*block*variety
Number of observations = 16
H0 SS = 171.293, df = 4, MS = 42.8231
Residual SS = 19.1575, df = 11, MS = 1.74159
R-sq = 0.899409
F0 = 24.5885
Prob[F > F0] = 0.00002
=================================
Dap 3. Mon Jan 12 02:33:16 2004
Whole plot (block, fertilizer) analysis
Testing Ho: fertilizer
Denominator: fertilizer*block variety fertilizer*variety block*variety
fertilizer*block*variety
Number of observations = 16
H0 SS = 40.19, df = 3, MS = 13.3967
Residual SS = 19.1575, df = 11, MS = 1.74159
F0 = 7.6922
Prob[F > F0] = 0.00479
=================================
Dap 4. Mon Jan 12 02:33:17 2004
Whole plot (block, fertilizer) analysis
Testing Ho: block
Denominator: fertilizer*block variety fertilizer*variety block*variety
fertilizer*block*variety
Number of observations = 16
H0 SS = 131.103, df = 1, MS = 131.103
Residual SS = 19.1575, df = 11, MS = 1.74159
F0 = 75.2774
Prob[F > F0] = 0.00001
=================================
Dap 5. Mon Jan 12 02:33:17 2004
Whole plot (block, fertilizer) analysis
Testing Ho: fertilizer
Denominator: fertilizer * block
EMS(fertilizer*block) =
2 Var(fertilizer*block)
1 Var(Error)
EMS(fertilizer) =
4 Var(fertilizer)
2 Var(fertilizer*block)
1 Var(Error)
Error for fertilizer =
1 MS(fertilizer*block)
Number of observations = 16
H0 SS = 40.19, df = 3, MS = 13.3967
Residual df = 3, MS = 2.30917
F0 = 5.80152
Prob[F > F0] = 0.09137
=================================
Dap 6. Mon Jan 12 02:33:17 2004
Whole plot (block, fertilizer) analysis
Least-squares means for: fertilizer
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 7. Mon Jan 12 02:33:18 2004
Whole plot (block, fertilizer) analysis
_stat_ for _lsm_ / fertilizer
================================================================================
|
|38.8 |39.4
|39.8 |42.9
|
|--------+--------+--------|------------+------------+------------+------------|
|_type_ |_LSMEAN_|_fertili|1
|3 |2
|4 |
|========|========|========|============|============|============|============|
|EFFN |0
| |
4| 4|
4| 4|
|--------+--------+--------|------------+------------+------------+------------|
|LSMDIFF |38.8 |1
| |
0.6| 1|
4.1|
| |--------+--------|------------+------------+------------+------------|
| |39.4
|3 |
-0.6|
| 0.4|
3.5|
| |--------+--------|------------+------------+------------+------------|
| |39.8
|2 |
-1| -0.4|
| 3.1|
| |--------+--------|------------+------------+------------+------------|
| |42.9
|4 |
-4.1| -3.5|
-3.1|
|
|--------+--------+--------|------------+------------+------------+------------|
|MINDIFF |38.8 |1
| |
3.41955| 3.41955| 3.41955|
| |--------+--------|------------+------------+------------+------------|
| |39.4
|3 | 3.41955|
| 3.41955| 3.41955|
| |--------+--------|------------+------------+------------+------------|
| |39.8
|2 | 3.41955|
3.41955|
| 3.41955|
| |--------+--------|------------+------------+------------+------------|
| |42.9
|4 | 3.41955|
3.41955| 3.41955|
|
|--------+--------+--------|------------+------------+------------+------------|
|PROB |38.8 |1
| |
0.615544| 0.420682| 0.031666|
| |--------+--------|------------+------------+------------+------------|
| |39.4
|3 | 0.615544|
| 0.734441| 0.0472285|
| |--------+--------|------------+------------+------------+------------|
| |39.8
|2 | 0.420682|
0.734441|
| 0.0632693|
| |--------+--------|------------+------------+------------+------------|
| |42.9
|4 | 0.031666|
0.0472285| 0.0632693|
|
--------------------------------------------------------------------------------
=================================
Dap 8. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Response variable: yield
Treatment Levels
-------- ------
fertilizer 1 2 3 4
block
1 2
variety 1 2
=================================
Dap 9. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: fertilizer block fertilizer*block variety fertilizer*variety
Denominator: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 182.02, df = 11, MS = 16.5473
Residual SS = 8.43, df = 4, MS = 2.1075
R-sq = 0.955736
F0 = 7.85161
Prob[F > F0] = 0.03064
=================================
Dap 10. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: fertilizer
Denominator: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 40.19, df = 3, MS = 13.3967
Residual SS = 8.43, df = 4, MS = 2.1075
F0 = 6.35666
Prob[F > F0] = 0.05300
=================================
Dap 11. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: block
Denominator: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 131.103, df = 1, MS = 131.103
Residual SS = 8.43, df = 4, MS = 2.1075
F0 = 62.2076
Prob[F > F0] = 0.00140
=================================
Dap 12. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: variety
Denominator: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 2.25, df = 1, MS = 2.25
Residual SS = 8.43, df = 4, MS = 2.1075
F0 = 1.06762
Prob[F > F0] = 0.35987
=================================
Dap 13. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: fertilizer*block
Denominator: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 6.9275, df = 3, MS = 2.30917
Residual SS = 8.43, df = 4, MS = 2.1075
F0 = 1.09569
Prob[F > F0] = 0.44760
=================================
Dap 14. Mon Jan 12 02:33:18 2004
Subplot (variety) analysis
Testing Ho: fertilizer*variety
Denominator: block*variety fertilizer*block*variety
Number of observations = 16
H0 SS = 1.55, df = 3, MS = 0.516667
Residual SS = 8.43, df = 4, MS = 2.1075
F0 = 0.245156
Prob[F > F0] = 0.86125
Latin square [Back to Sample output]
ED: pp. 122 - 125
=================================
Dap 1. Mon Jan 12 03:20:29 2004
Response variable: error
Treatment Levels
-------- ------
sampler A B C D
E F
area
1 2 3 4 5 6
order
6 5 1 2 4 3
=================================
Dap 2. Mon Jan 12 03:20:34 2004
Testing Ho: sampler area order
Denominator: sampler*area*order
Number of observations = 36
H0 SS = 263.064, df = 15, MS = 17.5376
Residual SS = 66.5633, df = 20, MS = 3.32817
R-sq = 0.798065
F0 = 5.26945
Prob[F > F0] = 0.00039
=================================
Dap 3. Mon Jan 12 03:20:34 2004
Testing Ho: sampler
Denominator: sampler*area*order
Number of observations = 36
H0 SS = 155.596, df = 5, MS = 31.1192
Residual SS = 66.5633, df = 20, MS = 3.32817
F0 = 9.35024
Prob[F > F0] = 0.00011
=================================
Dap 4. Mon Jan 12 03:20:34 2004
Least-squares means for: sampler
LSD method
Minimum significant differences are for level 0.05000
=================================
Dap 5. Mon Jan 12 03:20:35 2004
_stat_ for _lsm_ / sampler
==========================================================================================================
|
|1.2 |2.66667
|5.58333 |6.06667 |6.11667
|6.91667 |
|--------+--------+--------|------------+------------+------------+------------+------------+------------|
|_type_ |_LSMEAN_|_sampler|F
|E |B
|A |C
|D |
|========|========|========|============|============|============|============|============|============|
|EFFN |0
| |
6| 6|
6| 6|
6| 6|
|--------+--------+--------|------------+------------+------------+------------+------------+------------|
|LSMDIFF |1.2 |F
| |
1.46667| 4.38333| 4.86667|
4.91667| 5.71667|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |2.66667 |E
| -1.46667|
| 2.91667|
3.4| 3.45|
4.25|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |5.58333 |B
| -4.38333| -2.91667|
| 0.483333| 0.533333|
1.33333|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |6.06667 |A
| -4.86667|
-3.4| -0.483333|
| 0.05|
0.85|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |6.11667 |C
| -4.91667| -3.45|
-0.533333| -0.05|
| 0.8|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |6.91667 |D
| -5.71667| -4.25|
-1.33333| -0.85|
-0.8|
|
|--------+--------+--------|------------+------------+------------+------------+------------+------------|
|MINDIFF |1.2 |F
| |
2.19708| 2.19708| 2.19708|
2.19708| 2.19708|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |2.66667 |E
| 2.19708|
| 2.19708| 2.19708|
2.19708| 2.19708|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |5.58333 |B
| 2.19708| 2.19708|
| 2.19708| 2.19708|
2.19708|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |6.06667 |A
| 2.19708| 2.19708|
2.19708|
| 2.19708| 2.19708|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |6.11667 |C
| 2.19708| 2.19708|
2.19708| 2.19708|
| 2.19708|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |6.91667 |D
| 2.19708| 2.19708|
2.19708| 2.19708| 2.19708|
|
|--------+--------+--------|------------+------------+------------+------------+------------+------------|
|PROB |1.2 |F
| |
0.179066| 0.000482096| 0.000165276| 0.000148021| 2.59349e-05|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |2.66667 |E
| 0.179066|
| 0.0118367| 0.0042174| 0.00378313|
0.00064819|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |5.58333 |B
| 0.000482096| 0.0118367|
| 0.651264| 0.618142|
0.220099|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |6.06667 |A
| 0.000165276| 0.0042174| 0.651264|
| 0.962609| 0.429156|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |6.11667 |C
| 0.000148021| 0.00378313| 0.618142|
0.962609|
| 0.456392|
| |--------+--------|------------+------------+------------+------------+------------+------------|
| |6.91667 |D
| 2.59349e-05| 0.00064819| 0.220099|
0.429156| 0.456392|
|
----------------------------------------------------------------------------------------------------------
=================================
Dap 6. Mon Jan 12 03:20:35 2004
Testing Ho: area
Denominator: sampler*area*order
Number of observations = 36
H0 SS = 78.8692, df = 5, MS = 15.7738
Residual SS = 66.5633, df = 20, MS = 3.32817
F0 = 4.7395
Prob[F > F0] = 0.00512
=================================
Dap 7. Mon Jan 12 03:20:35 2004
Testing Ho: order
Denominator: sampler*area*order
Number of observations = 36
H0 SS = 28.5992, df = 5, MS = 5.71983
Residual SS = 66.5633, df = 20, MS = 3.32817
F0 = 1.71861
Prob[F > F0] = 0.17635
Missing treatment combinations [Back to Sample output]
AMD: pp. 173 - 177
=================================
Dap 1. Mon Jan 12 04:25:05 2004
Test Ho:
u11 - u21 - (u13 - u23) = 0
u21 - u31 - (u22 - u32) = 0
Testing Ho: treat*block
Number of observations = 10
H0 SS = 18.7778, df = 2, MS = 9.38889
Error SS = 14.5, df = 3, MS = 4.83333
F0 = 1.94253
Prob[F > F0] = 0.28763
=================================
Dap 2. Mon Jan 12 04:25:05 2004
Test Ho:
u11 + u13 - (u21 + u23) = 0
u21 + u22 - (u31 + u32) = 0
Testing Ho: treat
Number of observations = 10
H0 SS = 2.77778, df = 2, MS = 1.38889
Error SS = 14.5, df = 3, MS = 4.83333
F0 = 0.287356
Prob[F > F0] = 0.76882
Linear regression [Back to Sample output]
MS: pp. 95 - 97
=================================
Dap 1. Mon Jan 12 03:20:40 2004
Reduced | full model regressors: _intercept_ | soilphos
Number of observations = 9
Response: plantphos
F0(1, 7) = 12.8868, Prob[F > F0] = 0.00886
R-sq = 0.648008, Adj R-sq = 0.597723
Parameter
Estimate Std Error T0[
7] Prob[|T|>|T0|]
_intercept_
61.5804 6.24765
9.85656 0.00003
soilphos
1.41689 0.394698
3.58982 0.00886
Linear regression, model building [Back to Sample output]
LM: pp. 50 - 60
=================================
Dap 1. Mon Jan 12 03:20:39 2004
Correlations
_corr_ for _var2_
====================================================================================
|_var1_ |_type_ |x1
|x2 |x3
|x4 |y
|
|========|========|============|============|============|============|============|
|x1 |CORR |
1| 0.971236| -0.668403|
0.651995| 0.740306|
| |--------|------------+------------+------------+------------+------------|
| |N
| 10|
10| 10|
10| 10|
| |--------|------------+------------+------------+------------+------------|
| |PCORR
| 0| 2.89253e-06|
0.034618| 0.0410635| 0.0143438|
|--------+--------|------------+------------+------------+------------+------------|
|x2 |CORR |
0.971236| 1|
-0.59782| 0.526844| 0.627619|
| |--------|------------+------------+------------+------------+------------|
| |N
| 10|
10| 10|
10| 10|
| |--------|------------+------------+------------+------------+------------|
| |PCORR
| 2.89253e-06|
0| 0.0679458| 0.117662| 0.0520562|
|--------+--------|------------+------------+------------+------------+------------|
|x3 |CORR |
-0.668403| -0.59782|
1| -0.84101| -0.780072|
| |--------|------------+------------+------------+------------+------------|
| |N
| 10|
10| 10|
10| 10|
| |--------|------------+------------+------------+------------+------------|
| |PCORR
| 0.034618| 0.0679458|
0| 0.00229667| 0.00777379|
|--------+--------|------------+------------+------------+------------+------------|
|x4 |CORR |
0.651995| 0.526844| -0.84101|
1| 0.977603|
| |--------|------------+------------+------------+------------+------------|
| |N
| 10|
10| 10|
10| 10|
| |--------|------------+------------+------------+------------+------------|
| |PCORR
| 0.0410635| 0.117662| 0.00229667|
0| 1.07154e-06|
|--------+--------|------------+------------+------------+------------+------------|
|y |CORR
| 0.740306| 0.627619| -0.780072|
0.977603| 1|
| |--------|------------+------------+------------+------------+------------|
| |N
| 10|
10| 10|
10| 10|
| |--------|------------+------------+------------+------------+------------|
| |PCORR
| 0.0143438| 0.0520562| 0.00777379| 1.07154e-06|
0|
------------------------------------------------------------------------------------
=================================
Dap 2. Mon Jan 12 03:20:39 2004
Model building
Reduced | full model regressors: _intercept_ | x4
Number of observations = 10
Response: y
F0(1, 8) = 172.619, Prob[F > F0] = 0.00001
R-sq = 0.955708, Adj R-sq = 0.950171
Parameter
Estimate Std Error T0[
8] Prob[|T|>|T0|]
_intercept_
21.8042 2.83157
7.70041 0.00006
x4
1.02579 0.0780755 13.1384
0.00001
=================================
Dap 3. Mon Jan 12 03:20:39 2004
Model building
Reduced | full model regressors: _intercept_ x4 | x1
Number of observations = 10
Response: y
F0(2, 7) = 131.793, Prob[F > F0] = 0.00001
R-sq = 0.97413, Adj R-sq = 0.966739
F-change(1, 7) = 4.98488, Prob[F > F-change] = 0.06073
Parameter
Estimate Std Error T0[
7] Prob[|T|>|T0|]
_intercept_
12.9449 4.59315
2.81831 0.02584
x4
0.903324 0.084129
10.7374 0.00002
x1
1.88521 0.844369
2.23268 0.06073
=================================
Dap 4. Mon Jan 12 03:20:39 2004
Model building
Reduced | full model regressors: _intercept_ x4 x1 | x3
Number of observations = 10
Response: y
F0(3, 6) = 183.912, Prob[F > F0] = 0.00001
R-sq = 0.989242, Adj R-sq = 0.983863
F-change(1, 6) = 8.42848, Prob[F > F-change] = 0.02723
Parameter
Estimate Std Error T0[
6] Prob[|T|>|T0|]
_intercept_
2.55427 4.80051 0.532084
0.61379
x4
1.07907 0.0842512 12.8078
0.00002
x1
2.40786 0.615063
3.91482 0.00785
x3
0.936516 0.322582
2.90318 0.02723
=================================
Dap 5. Mon Jan 12 03:20:39 2004
Model building
Reduced | full model regressors: _intercept_ x4 x1 x3 | x2
Number of observations = 10
Response: y
F0(4, 5) = 126.46, Prob[F > F0] = 0.00004
R-sq = 0.990212, Adj R-sq = 0.982382
F-change(1, 5) = 0.495515, Prob[F > F-change] = 0.51290
Parameter
Estimate Std Error T0[
5] Prob[|T|>|T0|]
_intercept_
2.80985 5.02914 0.558715
0.60046
x4
1.12232 0.107355
10.4543 0.00014
x1
0.523737 2.75266
0.190266 0.85659
x3
0.994528 0.346992
2.86614 0.03516
x2
0.775412 1.10155
0.703928 0.51290
Ordinal cross-classification [Back to Sample output]
CDA: pp. 49 - 50
=================================
Dap 1. Mon Jan 12 03:20:25 2004
Variable: Levels
----------------
income: 00-06 06-15 15-25 25-
jobsat: 1verydis 2littledis 3modsat 4verysat
Chisq0[9] = 11.9886, Prob[Chisq > Chisq0] = 0.21396
Statistic
Value ASE
Gamma
0.127 0.041
Kendall's Tau-b 0.088 0.028
Somers' D C|R 0.082 0.026
Somers' D R|C 0.094 0.030
=================================
Dap 2. Mon Jan 12 03:20:25 2004
_cell_ for jobsat
=======================================================================
|income |_type_ |1verydis |2littledis
|3modsat |4verysat |
|========|========|============|============|============|============|
|00-06 |COUNT |
20| 24|
80| 82|
| |--------|------------+------------+------------+------------|
| |EXPECTED|
14.1754| 24.6926| 72.9345|
94.1976|
|--------+--------|------------+------------+------------+------------|
|06-15 |COUNT |
22| 38|
104| 125|
| |--------|------------+------------+------------+------------|
| |EXPECTED|
19.8868| 34.6415| 102.321|
132.151|
|--------+--------|------------+------------+------------+------------|
|15-25 |COUNT |
13| 28|
81| 113|
| |--------|------------+------------+------------+------------|
| |EXPECTED|
16.1709| 28.1687|
83.202| 107.458|
|--------+--------|------------+------------+------------+------------|
|25- |COUNT |
7| 18|
54| 92|
| |--------|------------+------------+------------+------------|
| |EXPECTED|
11.7669| 20.4972| 60.5427|
78.1931|
-----------------------------------------------------------------------
Stratified 2x2 tables [Back to Sample output]
CDA: pp. 232 - 233
=================================
Dap 1. Mon Jan 12 03:20:24 2004
Cochran-Mantel-Haenszel test for delay x response, stratified by
penicillin
M0-squared = 3.92857, Prob[M-squared > M0-squared] = 0.0475
=================================
Dap 2. Mon Jan 12 03:20:24 2004
For: penicillin = 0.125
_cell_ for
response
====================================
|delay |cured |died
|
|========|============|============|
|1.5h |
0| 5|
|--------|------------+------------|
|none |
0| 6|
------------------------------------
=================================
Dap 3. Mon Jan 12 03:20:24 2004
For: penicillin = 0.250
_cell_ for
response
====================================
|delay |cured |died
|
|========|============|============|
|1.5h |
0| 6|
|--------|------------+------------|
|none |
3| 3|
------------------------------------
=================================
Dap 4. Mon Jan 12 03:20:24 2004
For: penicillin = 0.500
_cell_ for
response
====================================
|delay |cured |died
|
|========|============|============|
|1.5h |
2| 4|
|--------|------------+------------|
|none |
6| 0|
------------------------------------
=================================
Dap 5. Mon Jan 12 03:20:24 2004
For: penicillin = 1.000
_cell_ for
response
====================================
|delay |cured |died
|
|========|============|============|
|1.5h |
6| 0|
|--------|------------+------------|
|none |
5| 1|
------------------------------------
=================================
Dap 6. Mon Jan 12 03:20:24 2004
For: penicillin = 4.000
_cell_ for
response
====================================
|delay |cured |died
|
|========|============|============|
|1.5h |
5| 0|
|--------|------------+------------|
|none |
2| 0|
------------------------------------
Loglinear models [Back to Sample output]
CDA: pp. 135 - 138, 171 - 174, 176 - 177
=================================
Dap 1. Mon Jan 12 03:19:54 2004
(DV, P) vs (D, V, P)
Loglinear model:
numerical indexes of classification variables
Number defendant victim penalty
------ --------- ------ -------
1 black
black no
2 white
white yes
=================================
Dap 2. Mon Jan 12 03:19:55 2004
(DV, P) vs (D, V, P)
Maximum likelihood estimation
Cell count: n
Class and aux variables: _defendant _victim _penalty
Statistic
df Prob
G2[Model] = 8.13 3
0.0434
G2[Reduced] = 137.93 4 0.0001
G2[Diff] = 129.80 1
0.0001
X2[Model] = 6.98 3
0.0727
Estimate
ASE Model Parameter
2.83789 0.117031
* _mu
0.39084 0.0946427
* defendant:1
-0.582116 0.0946427
* victim:1
0.827863 0.0946427
? defendant*victim:1:1
1.04323 0.0883545
* penalty:1
=================================
Dap 3. Mon Jan 12 03:19:55 2004
(DV, P) vs (D, V, P)
n for _type_ / penalty
===========================================================
|
|FIT |OBS
|
|--------------+--------------|------+------+------+------|
|defendant |victim
|no |yes |no |yes
|
|==============|==============|======|======|======|======|
|black |black
| 91.63| 11.37| 97.00| 6.00|
|
|--------------|------+------+------+------|
|
|white | 56.04| 6.96|
52.00| 11.00|
|--------------+--------------|------+------+------+------|
|white |black
| 8.01| 0.99| 9.00| 0.00|
|
|--------------|------+------+------+------|
|
|white |134.33| 16.67|132.00|
19.00|
-----------------------------------------------------------
=================================
Dap 4. Mon Jan 12 03:19:55 2004
(DV, VP) vs (DV, P)
Loglinear model:
numerical indexes of classification variables
Number defendant victim penalty
------ --------- ------ -------
1 black
black no
2 white
white yes
=================================
Dap 5. Mon Jan 12 03:19:55 2004
(DV, VP) vs (DV, P)
Maximum likelihood estimation
Cell count: n
Class and aux variables: _defendant _victim _penalty
Statistic
df Prob
G2[Model] = 1.88 2
0.3903
G2[Reduced] = 8.13 3
0.0434
G2[Diff] = 6.25
1 0.0125
X2[Model] = 1.43 2
0.4889
Estimate
ASE Model Parameter
2.72369
0.13779 * _mu
0.39084 0.0946425
* defendant:1
-0.79861 0.13779
* victim:1
0.827913 0.0946425
* defendant*victim:1:1
1.1713 0.115885
* penalty:1
0.264535 0.115885
? victim*penalty:1:1
=================================
Dap 6. Mon Jan 12 03:19:55 2004
(DV, VP) vs (DV, P)
n for _type_ / penalty
===========================================================
|
|FIT |OBS
|
|--------------+--------------|------+------+------+------|
|defendant |victim
|no |yes |no |yes
|
|==============|==============|======|======|======|======|
|black |black
| 97.48| 5.52| 97.00| 6.00|
|
|--------------|------+------+------+------|
|
|white | 54.17| 8.83|
52.00| 11.00|
|--------------+--------------|------+------+------+------|
|white |black
| 8.52| 0.48| 9.00| 0.00|
|
|--------------|------+------+------+------|
|
|white |129.83| 21.17|132.00|
19.00|
-----------------------------------------------------------
=================================
Dap 7. Mon Jan 12 03:19:55 2004
(DV, DP, VP) vs (DV, VP)
Loglinear model:
numerical indexes of classification variables
Number defendant victim penalty
------ --------- ------ -------
1 black
black no
2 white
white yes
=================================
Dap 8. Mon Jan 12 03:19:55 2004
(DV, DP, VP) vs (DV, VP)
Maximum likelihood estimation
Cell count: n
Class and aux variables: _defendant _victim _penalty
Statistic
df Prob
G2[Model] = 0.70 1
0.4026
G2[Reduced] = 1.88 2
0.3903
G2[Diff] = 1.18
1 0.2772
X2[Model] = 0.38 1
0.5401
Estimate
ASE Model Parameter
2.69211 0.142941
* _mu
0.479402 0.124301
* defendant:1
-0.854001 0.146841
* victim:1
0.839499 0.0954928
* defendant*victim:1:1
1.20005 0.119974
* penalty:1
-0.110105 0.100222
? defendant*penalty:1:1
0.331103 0.129837
* victim*penalty:1:1
=================================
Dap 9. Mon Jan 12 03:19:55 2004
(DV, DP, VP) vs (DV, VP)
n for _type_ / penalty
===========================================================
|
|FIT |OBS
|
|--------------+--------------|------+------+------+------|
|defendant |victim
|no |yes |no |yes
|
|==============|==============|======|======|======|======|
|black |black
| 97.33| 5.67| 97.00| 6.00|
|
|--------------|------+------+------+------|
|
|white | 51.67| 11.33|
52.00| 11.00|
|--------------+--------------|------+------+------+------|
|white |black
| 8.67| 0.33| 9.00| 0.00|
|
|--------------|------+------+------+------|
|
|white |132.33| 18.67|132.00|
19.00|
-----------------------------------------------------------
Logit model for linear-by-linear association [Back to Sample output]
CDA: pp. 261 - 269
=================================
Dap 1. Mon Jan 12 03:19:56 2004
Maximum likelihood estimation
Cell count: count
Class and aux variables: income jobsat
Statistic
df Prob
G2[Model] = 2.39 8
0.9669
G2[Reduced] = 12.04 9
0.2113
G2[Diff] = 9.65
1 0.0019
X2[Model] = 2.33 8
0.9693
Estimate
ASE Model Parameter
3.47292 0.102512
* mu
0.294928 0.132411
* <6
0.396336 0.06927
* 6-15
-0.0581172 0.068397
* 15-25
-0.805298 0.118785
* VeryDis
-0.385834 0.0853789
* LittleDis
0.548591 0.0620011
* ModSat
0.11199 0.0364075
? Inc*Sat
=================================
Dap 2. Mon Jan 12 03:19:57 2004
count for
_type_ / jobsat
==================================================================
| |FIT
|OBS
|
|--------|------+------+------+------+------+------+------+------|
|income |0 |1
|2 |3 |0
|1 |2 |3
|
|========|======|======|======|======|======|======|======|======|
|0 | 19.35| 29.43| 74.93| 82.30|
20.00| 24.00| 80.00| 82.00|
|--------|------+------+------+------+------+------+------+------|
|1 | 21.41| 36.43|103.73|127.43|
22.00| 38.00|104.00|125.00|
|--------|------+------+------+------+------+------+------+------|
|2 | 13.59| 25.86| 82.37|113.18|
13.00| 28.00| 81.00|113.00|
|--------|------+------+------+------+------+------+------+------|
|3 | 7.65| 16.28| 57.98|
89.10| 7.00| 18.00| 54.00| 92.00|
------------------------------------------------------------------
Logistic regression [Back to Sample output]
CDA: pp. 87 - 89
=================================
Dap 1. Mon Jan 12 03:20:27 2004
Reduced | full model regressors: _intercept_ | labind
Number of observations = 14
Number of trials = 27
Events / Trials: nremiss / ncases
-2 (Lred - Lfull) = 8.2988 = ChiSq0[1]
Prob[ChiSq > ChiSq0] = 0.00397
Parameter
Estimate Std Error Wald ChiSq Prob[ChiSq>Wald
ChiSq]
_intercept_
-3.77714 1.37863
7.5064 0.00615
labind
0.144863 0.0593411 5.95942
0.01464
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Copyright (C) 2004 Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111, USA
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Updated: $Date: 2004/03/14 20:22:20 $ $Author: sbassein $