tests: Add "categorical" keyword to tests that use categoricals.

This makes it easier to run tests just for these.

diff --git a/tests/language/stats/examine.at b/tests/language/stats/examine.at
index 6ccfac555a7d150a2d4e5f95d806c65bb3e1f361..9d4374d376e0e96c731382a8f4bceb0121436189 100644 (file)
--- a/tests/language/stats/examine.at
+++ b/tests/language/stats/examine.at
@@ -17,6 +17,7 @@ dnl
 AT_BANNER([EXAMINE])
 
 AT_SETUP([EXAMINE])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [
 DATA LIST LIST /QUALITY * W * BRAND * .
 BEGIN DATA
@@ -188,6 +189,7 @@ Breaking Strain,Aspeger,Mean,,2.25,.45
 AT_CLEANUP
 
 AT_SETUP([EXAMINE -- extremes])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 data list free /V1 W
 begin data.
@@ -249,6 +251,7 @@ AT_CLEANUP
 
 
 AT_SETUP([EXAMINE -- extremes with fractional weights])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([extreme.sps], [dnl
 set format=F20.3.
 data list notable list /w * x *.
@@ -325,6 +328,7 @@ dnl Test the PERCENTILES subcommand of the EXAMINE command.
 dnl In particular test that it behaves properly when there are only 
 dnl a few cases.
 AT_SETUP([EXAMINE -- percentiles])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 DATA LIST LIST /X *.
 BEGIN DATA.
@@ -417,6 +421,7 @@ X,Empirical with averaging,2.00,2.00,2.00,5.00,8.00,8.00,8.00
 AT_CLEANUP
 
 AT_SETUP([EXAMINE -- missing values])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 DATA LIST LIST /x * y *.
 BEGIN DATA.
@@ -457,6 +462,7 @@ AT_CLEANUP
 
 
 AT_SETUP([EXAMINE -- user missing values])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine-m.sps], [dnl
 DATA LIST notable LIST /x * y *.
 BEGIN DATA.
@@ -483,6 +489,7 @@ y,2,66.6667%,1,33.3333%,3,100%
 AT_CLEANUP
 
 AT_SETUP([EXAMINE -- missing values and percentiles])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 DATA LIST LIST /X *.
 BEGIN DATA.
@@ -503,6 +510,7 @@ AT_CLEANUP
 dnl Tests the trimmed mean calculation in the case
 dnl where the data is weighted towards the centre.
 AT_SETUP([EXAMINE -- trimmed mean])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 DATA LIST LIST /X * C *.
 BEGIN DATA.
@@ -550,6 +558,7 @@ X,Mean,,2.02,.03
 AT_CLEANUP
 
 AT_SETUP([EXAMINE -- crash bug])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 data list list /a * x * y *.
 begin data.
@@ -568,6 +577,7 @@ AT_CLEANUP
 
 dnl Test that two consecutive EXAMINE commands don't crash PSPP.
 AT_SETUP([EXAMINE -- consecutive runs don't crash])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 data list list /y * z *.
 begin data.
@@ -586,6 +596,7 @@ AT_CLEANUP
 
 dnl Test that /DESCRIPTIVES does not crash in presence of missing values.
 AT_SETUP([EXAMINE -- missing values don't crash])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 data list list /x * y *.
 begin data.
@@ -603,6 +614,7 @@ AT_CLEANUP
 
 dnl Test that having only a single case doesn't crash.
 AT_SETUP([EXAMINE -- single case doesn't crash])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 DATA LIST LIST /quality * .
 BEGIN DATA
@@ -622,6 +634,7 @@ AT_CLEANUP
 
 dnl Test that all-missing data doesn't crash.
 AT_SETUP([EXAMINE -- all-missing data doesn't crash])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 DATA LIST LIST /x *.
 BEGIN DATA.
@@ -644,6 +657,7 @@ AT_CLEANUP
 
 dnl Test that big input doesn't crash (bug 11307).
 AT_SETUP([EXAMINE -- big input doesn't crash])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([examine.sps], [dnl
 INPUT PROGRAM.
        LOOP #I=1 TO 50000.
@@ -664,6 +678,7 @@ AT_CLEANUP
 dnl Another test that big input doesn't crash.
 dnl The actual bug that this checks for has been lost.
 AT_SETUP([EXAMINE -- big input doesn't crash 2])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([make-big-input.pl], 
   [for ($i=0; $i<100000; $i++) { print "AB12\n" };
    for ($i=0; $i<100000; $i++) { print "AB04\n" };
@@ -691,6 +706,7 @@ AT_CLEANUP
 
 dnl Test that the ID command works with non-numberic variables
 AT_SETUP([EXAMINE -- non-numeric ID])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([examine-id.sps], [dnl
 data list notable list /x * y (a12).
@@ -743,6 +759,7 @@ AT_CLEANUP
 
 dnl Test for a crash which happened on cleanup from a bad input syntax
 AT_SETUP([EXAMINE -- Bad Input])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([examine-bad.sps], [dnl
 data list list /h * g *.
@@ -773,7 +790,7 @@ AT_CLEANUP
 
 dnl Check the MISSING=REPORT option
 AT_SETUP([EXAMINE -- MISSING=REPORT])
-
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([examine-report.sps], [dnl
 set format = F22.0.
@@ -901,6 +918,7 @@ dnl Run a test of the basic STATISTICS using a "real"
 dnl dataset and comparing with "real" results kindly
 dnl provided by Olaf Nöhring
 AT_SETUP([EXAMINE -- sample unweighted])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([sample.sps], [dnl
 set format = F22.4.
@@ -1044,6 +1062,7 @@ AT_CLEANUP
 
 dnl Test for a crash which happened on bad input syntax
 AT_SETUP([EXAMINE -- Empty Parentheses])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([examine-empty-parens.sps], [dnl
 DATA LIST notable LIST /X *
@@ -1068,6 +1087,7 @@ AT_CLEANUP
 
 dnl Test for another crash which happened on bad input syntax
 AT_SETUP([EXAMINE -- Bad variable])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([examine-bad-variable.sps], [dnl
 data list list /h * g *.
@@ -1091,6 +1111,7 @@ AT_CLEANUP
 
 dnl Test for yet another crash. This time for extremes vs. missing weight values.\0
 AT_SETUP([EXAMINE -- Extremes vs. Missing Weights])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([examine-missing-weights.sps], [dnl
 data list notable list /h * g *.

diff --git a/tests/language/stats/glm.at b/tests/language/stats/glm.at
index bbc343bc50443abaa5aaf99fbae4b5263fac25f0..564c8dee402083253fe4202397f5f16908debc57 100644 (file)
--- a/tests/language/stats/glm.at
+++ b/tests/language/stats/glm.at
@@ -17,6 +17,7 @@ dnl
 AT_BANNER([GLM procedure])
 
 AT_SETUP([GLM latin square design])
+AT_KEYWORDS([categorical categoricals])
 
 dnl This example comes from :
 dnl  http://ssnds.uwo.ca/statsexamples/spssanova/latinsquareresults.html
@@ -88,6 +89,7 @@ Corrected Total,329.62,35,,,
 AT_CLEANUP
 
 AT_SETUP([GLM 2 by 2 factorial design])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([2by2.sps], [dnl
 set format = F20.3.
@@ -140,6 +142,7 @@ AT_CLEANUP
 
 
 AT_SETUP([GLM Type I and II Sums of Squares])
+AT_KEYWORDS([categorical categoricals])
 
 dnl  The following example comes from 
 dnl  http://www.uvm.edu/~dhowell/StatPages/More_Stuff/Type1-3.pdf
@@ -273,6 +276,7 @@ AT_CLEANUP
 
 
 AT_SETUP([GLM excluded intercept])
+AT_KEYWORDS([categorical categoricals])
 
 dnl  The following example comes from 
 dnl
@@ -344,6 +348,7 @@ AT_CLEANUP
 
 
 AT_SETUP([GLM missing values])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([glm.data], [dnl
 1 1 6  3.5

diff --git a/tests/language/stats/logistic.at b/tests/language/stats/logistic.at
index 3d7ae76c38de9de5ac8635edea76e58799fc3b37..d604c0ad14ccbce2c27d69af26e99aea33ca1680 100644 (file)
--- a/tests/language/stats/logistic.at
+++ b/tests/language/stats/logistic.at
@@ -95,6 +95,7 @@ dnl  Note: In the above data cases 305, 316 318 and 329 have identical values
 dnl of the 2nd and 3rd variables.  We use this for weight testing.
 
 AT_SETUP([LOGISTIC REGRESSION basic test])
+AT_KEYWORDS([categorical categoricals])
 
 LOGIT_TEST_DATA
 
@@ -147,6 +148,7 @@ Step 1,survrate,-.081,.019,17.756,1,.000,.922
 AT_CLEANUP
 
 AT_SETUP([LOGISTIC REGRESSION missing values])
+AT_KEYWORDS([categorical categoricals])
 
 LOGIT_TEST_DATA
 
@@ -195,6 +197,7 @@ dnl Check that a weighted dataset is interpreted correctly
 dnl To do this, the same data set is used, one weighted, one not.
 dnl The weighted dataset omits certain cases which are identical
 AT_SETUP([LOGISTIC REGRESSION weights])
+AT_KEYWORDS([categorical categoricals])
 
 LOGIT_TEST_DATA
 
@@ -256,6 +259,7 @@ dnl Check that the /NOCONST option works as intended.
 dnl The results this produces are very similar to those
 dnl at the example in http://www.ats.ucla.edu/stat/SPSS/faq/logregconst.htm
 AT_SETUP([LOGISTIC REGRESSION without constant])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([non-const.sps], [dnl
 set format=F20.3.
@@ -313,6 +317,7 @@ AT_CLEANUP
 dnl Check that if somebody passes a dependent variable which is not dichtomous,
 dnl then an error is raised.
 AT_SETUP([LOGISTIC REGRESSION non-dichotomous dep var])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([non-dich.sps], [dnl
 data list notable list /y x1 x2 x3 x4.
@@ -338,6 +343,7 @@ dnl An example to check the behaviour of LOGISTIC REGRESSION with a categorical
 dnl variable.  This examṕle was inspired from that at:
 dnl http://www.ats.ucla.edu/stat/spss/dae/logit.htm 
 AT_SETUP([LOGISTIC REGRESSION with categorical])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([lr-cat.data], [dnl
  620 3.07 2 4 
@@ -806,6 +812,7 @@ AT_CLEANUP
 
 dnl  This example is inspired by http://www.ats.ucla.edu/stat/spss/output/logistic.htm
 AT_SETUP([LOGISTIC REGRESSION with cat var 2])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([lr-cat2.data], [dnl
      60.00     1.00      8.00     50.00 
@@ -1073,6 +1080,7 @@ AT_CLEANUP
 dnl Check that it doesn't crash if a categorical variable
 dnl has only one distinct value
 AT_SETUP([LOGISTIC REGRESSION identical categories])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([crash.sps], [dnl
 data list notable list /y x1 x2*.
@@ -1093,6 +1101,7 @@ AT_CLEANUP
 dnl Test that missing values on the categorical predictors are treated
 dnl properly.
 AT_SETUP([LOGISTIC REGRESSION missing categoricals])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([data.txt], [dnl
       .00     3.69      .00 
@@ -1230,6 +1239,7 @@ dnl Use an example with categoricals, because that was buggy at
 dnl one point.  The data in this example comes from:
 dnl  http://people.ysu.edu/~gchang/SPSSE/SPSS_lab2Regression.pdf
 AT_SETUP([LOGISTIC REGRESSION confidence interval])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([ci.sps], [dnl
 set FORMAT=F20.3

diff --git a/tests/language/stats/means.at b/tests/language/stats/means.at
index aa59242d5c3a5b50c7e9203ae1020c88a71fbfe6..d4db9ec8af3ca941b2f5e35886f072cdf0f62c05 100644 (file)
--- a/tests/language/stats/means.at
+++ b/tests/language/stats/means.at
@@ -17,6 +17,7 @@ dnl
 AT_BANNER([MEANS procedure])
 
 AT_SETUP([MEANS simple example])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([means-simple.sps], [dnl
 SET FORMAT=F12.5.
@@ -78,6 +79,7 @@ AT_CLEANUP
 
 
 AT_SETUP([MEANS very simple example])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([means-vsimple.sps], [dnl
 SET FORMAT=F12.5.
@@ -112,6 +114,7 @@ AT_CLEANUP
 
 
 AT_SETUP([MEANS default missing])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([means-dmiss.sps], [dnl
 SET FORMAT=F12.2.
@@ -166,6 +169,7 @@ AT_CLEANUP
 
 
 AT_SETUP([MEANS linear stats])
+AT_KEYWORDS([categorical categoricals])
 
 dnl Slightly more involved example to test the linear statistics
 AT_DATA([means-linear.sps], [dnl
@@ -215,6 +219,7 @@ AT_CLEANUP
 
 
 AT_SETUP([MEANS standard errors])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([means-stderr.sps], [dnl
 set format F12.4.
@@ -261,6 +266,7 @@ AT_CLEANUP
 
 
 AT_SETUP([MEANS harmonic and geometric means])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([means-hg.sps], [dnl
 set format F12.4.
@@ -303,6 +309,7 @@ AT_CLEANUP
 
 
 AT_SETUP([MEANS all/none/default])
+AT_KEYWORDS([categorical categoricals])
 
 dnl Make sure that /CELLS = {ALL,NONE,DEFAULT} work properly
 AT_DATA([means-stat-keywords.sps], [dnl
@@ -363,6 +370,7 @@ AT_CLEANUP
 
 
 AT_SETUP([MEANS missing=table ])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([means-miss-table.sps], [dnl
 data list notable list /a * b * g1.
@@ -473,6 +481,7 @@ AT_CLEANUP
 
 
 AT_SETUP([MEANS user missing values])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([means-missing.sps], [dnl
 data list notable list /a * b * g1.
@@ -571,6 +580,7 @@ AT_CLEANUP
 
 
 AT_SETUP([MEANS empty factor spec])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([means-bad.sps], [dnl
 data list list /outcome *.
@@ -591,6 +601,7 @@ AT_CLEANUP
 
 
 AT_SETUP([MEANS parser bug])
+AT_KEYWORDS([categorical categoricals])
 
 dnl This bug caused an infinite loop
 AT_DATA([means-bad.sps], [dnl

diff --git a/tests/language/stats/oneway.at b/tests/language/stats/oneway.at
index 6cec7597497aa48aae862d25a9149d3b7689e1f8..58fd75e289ae712c1fc47e70b38ce88afc9a7256 100644 (file)
--- a/tests/language/stats/oneway.at
+++ b/tests/language/stats/oneway.at
@@ -17,6 +17,7 @@ dnl
 AT_BANNER([ONEWAY procedure])
 
 AT_SETUP([ONEWAY basic operation])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([oneway.sps],
   [DATA LIST NOTABLE LIST /QUALITY * BRAND * .
 BEGIN DATA
@@ -86,6 +87,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY with splits])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([oneway-splits.sps],
 [DATA LIST NOTABLE LIST /QUALITY * BRAND * S *.
 BEGIN DATA
@@ -193,6 +195,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY with missing values])
+AT_KEYWORDS([categorical categoricals])
 dnl Check that missing are treated properly
 AT_DATA([oneway-missing1.sps],
 [DATA LIST NOTABLE LIST /v1 * v2 * dep * vn *.
@@ -298,6 +301,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY descriptives subcommand])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([oneway-descriptives.sps],
   [DATA LIST NOTABLE LIST /QUALITY * BRAND * .
@@ -347,6 +351,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY homogeneity subcommand])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([oneway-homogeneity.sps],
   [DATA LIST NOTABLE LIST /QUALITY * BRAND * .
@@ -392,6 +397,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY multiple variables])
+AT_KEYWORDS([categorical categoricals])
 dnl check that everything works ok when several different dependent variables are specified.
 dnl This of course does not mean that we're doing a multivariate analysis.  It's just like
 dnl running several tests at once.
@@ -500,6 +506,7 @@ AT_CLEANUP
 
 dnl Tests that everything treats weights properly
 AT_SETUP([ONEWAY vs. weights])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([oneway-unweighted.sps],
 [DATA LIST NOTABLE LIST /QUALITY * BRAND * W *.
@@ -571,6 +578,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY posthoc LSD and BONFERRONI])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([oneway-pig.sps],[dnl
 SET FORMAT F12.3.
 data list notable list /pigmentation * family *.
@@ -653,6 +661,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY posthoc Tukey HSD and Games-Howell])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([oneway-tukey.sps],[dnl
 set format = f11.3.
 data list notable list /libido * dose *.
@@ -709,6 +718,7 @@ Games-Howell,Placebo,1 Dose,-1.000,.887,.479,-3.356,1.356
 AT_CLEANUP
 
 AT_SETUP([ONEWAY posthoc Sidak])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([oneway-sidak.sps],[dnl
 SET FORMAT F20.4.
 
@@ -769,6 +779,7 @@ Table: Multiple Comparisons (score)
 AT_CLEANUP
 
 AT_SETUP([ONEWAY posthoc Scheffe])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([oneway-scheffe.sps],[dnl
 set format = f11.3.
 data list notable list /usage * group *.
@@ -851,6 +862,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY bad contrast count])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([oneway-bad-contrast.sps],[dnl
 DATA LIST NOTABLE LIST /height * weight * temperature * sex *.
@@ -922,6 +934,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY crash on single category independent variable])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([crash.sps],[
 input program.
 loop #i = 1 to 10.
@@ -943,6 +956,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY crash on missing dependent variable])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([crash2.sps],[dnl
 data list notable list /dv1 * dv2  *  y * .
 begin data.
@@ -971,6 +985,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY Games-Howell test with few cases])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([crash3.sps],[dnl
 data list notable list /dv * y * .
 begin data.
@@ -993,6 +1008,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY Crash on empty data])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([crash4.sps],[dnl
 DATA LIST NOTABLE LIST /height * weight * temperature * sex *.
 BEGIN DATA.
@@ -1015,6 +1031,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY Crash on invalid dependent variable])
+AT_KEYWORDS([categorical categoricals])
 AT_DATA([crash5.sps],[dnl
 data list notable list /a * b *.
 begin data.
@@ -1035,6 +1052,7 @@ AT_CLEANUP
 
 
 AT_SETUP([ONEWAY Crash on unterminated string])
+AT_KEYWORDS([categorical categoricals])
 
 AT_DATA([crash6.sps], [dnl
 DATA LIST NOTABLE LIST /height * weight * temperature * sex *.