X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=tests%2Flanguage%2Fstats%2Foneway.at;h=9eb1a0811bb9ceae7521eb1e6352f51bb069f872;hb=92635c65e5e265dc8114805af8974715539d90d2;hp=1d6b4c13a09f29522df42cd0b7b39c107693610e;hpb=4a49e8177df96e6947609fcb6066d1d68e0f6675;p=pspp diff --git a/tests/language/stats/oneway.at b/tests/language/stats/oneway.at index 1d6b4c13a0..9eb1a0811b 100644 --- a/tests/language/stats/oneway.at +++ b/tests/language/stats/oneway.at @@ -1,6 +1,23 @@ +dnl PSPP - a program for statistical analysis. +dnl Copyright (C) 2017, 2020 Free Software Foundation, Inc. +dnl +dnl This program is free software: you can redistribute it and/or modify +dnl it under the terms of the GNU General Public License as published by +dnl the Free Software Foundation, either version 3 of the License, or +dnl (at your option) any later version. +dnl +dnl This program is distributed in the hope that it will be useful, +dnl but WITHOUT ANY WARRANTY; without even the implied warranty of +dnl MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +dnl GNU General Public License for more details. +dnl +dnl You should have received a copy of the GNU General Public License +dnl along with this program. If not, see . +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 @@ -29,15 +46,16 @@ VALUE LABELS /brand 1 'Aspeger' 2 'Bloggs' 3 'Charlies'. ONEWAY quality BY brand /STATISTICS descriptives homogeneity - /CONTRAST = -2 1 1 + /CONTRAST = -2 1 1 /CONTRAST = 0 -1 1 . ]) -AT_CHECK([pspp -O format=csv oneway.sps], [0], [dnl +AT_CHECK([pspp -o pspp.csv -o pspp.txt oneway.sps]) +AT_CHECK([cat pspp.csv], [0], [dnl Table: Descriptives -,,,,,,95% Confidence Interval for Mean,,, -,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum +,Manufacturer,N,Mean,Std. Deviation,Std. Error,95% Confidence Interval for Mean,,Minimum,Maximum +,,,,,,Lower Bound,Upper Bound,, Breaking Strain,Aspeger,5,2.20,1.30,.58,.58,3.82,1.00,4.00 ,Bloggs,5,3.20,1.30,.58,1.58,4.82,2.00,5.00 ,Charlies,5,5.00,1.58,.71,3.04,6.96,3.00,7.00 @@ -54,22 +72,23 @@ Breaking Strain,Between Groups,20.13,2,10.07,5.12,.025 ,Total,43.73,14,,, Table: Contrast Coefficients -,,Manufacturer,, -,,Aspeger,Bloggs,Charlies -Contrast,1,-2,1,1 -,2,0,-1,1 +Contrast,Manufacturer,, +,Aspeger,Bloggs,Charlies +1,-2,1,1 +2,0,-1,1 Table: Contrast Tests ,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) -Breaking Strain,Assume equal variances,1,3.80,1.54,2.47,12,.029 -,,2,1.80,.89,2.03,12,.065 -,Does not assume equal,1,3.80,1.48,2.56,8.74,.031 +Breaking Strain,Assume equal variances,1,3.80,1.54,2.47,12.00,.029 +,,2,1.80,.89,2.03,12.00,.065 +,Does not assume equal variances,1,3.80,1.48,2.56,8.74,.031 ,,2,1.80,.92,1.96,7.72,.086 ]) 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 @@ -105,13 +124,15 @@ ONEWAY . ]) -AT_CHECK([pspp -O format=csv oneway-splits.sps], [0], -[Variable,Value,Label -S,1.00, +AT_CHECK([pspp -o pspp.csv -o pspp.txt oneway-splits.sps]) +AT_CHECK([cat pspp.csv], [0], [dnl +Table: Split Values +Variable,Value +S,1.00 Table: Descriptives -,,,,,,95% Confidence Interval for Mean,,, -,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum +,Manufacturer,N,Mean,Std. Deviation,Std. Error,95% Confidence Interval for Mean,,Minimum,Maximum +,,,,,,Lower Bound,Upper Bound,, Breaking Strain,Aspeger,5,2.20,1.30,.58,.58,3.82,1.00,4.00 ,Bloggs,2,3.50,2.12,1.50,-15.56,22.56,2.00,5.00 ,Total,7,2.57,1.51,.57,1.17,3.97,1.00,5.00 @@ -127,24 +148,25 @@ Breaking Strain,Between Groups,2.41,1,2.41,1.07,.349 ,Total,13.71,6,,, Table: Contrast Coefficients -,,Manufacturer, -,,Aspeger,Bloggs -Contrast,1,-2,2 -,2,-1,1 +Contrast,Manufacturer, +,Aspeger,Bloggs +1,-2,2 +2,-1,1 Table: Contrast Tests ,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) -Breaking Strain,Assume equal variances,1,2.60,2.52,1.03,5,.349 -,,2,1.30,1.26,1.03,5,.349 -,Does not assume equal,1,2.60,3.22,.81,1.32,.539 +Breaking Strain,Assume equal variances,1,2.60,2.52,1.03,5.00,.349 +,,2,1.30,1.26,1.03,5.00,.349 +,Does not assume equal variances,1,2.60,3.22,.81,1.32,.539 ,,2,1.30,1.61,.81,1.32,.539 -Variable,Value,Label -S,2.00, +Table: Split Values +Variable,Value +S,2.00 Table: Descriptives -,,,,,,95% Confidence Interval for Mean,,, -,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum +,Manufacturer,N,Mean,Std. Deviation,Std. Error,95% Confidence Interval for Mean,,Minimum,Maximum +,,,,,,Lower Bound,Upper Bound,, Breaking Strain,Bloggs,3,3.00,1.00,.58,.52,5.48,2.00,4.00 ,Charlies,5,5.00,1.58,.71,3.04,6.96,3.00,7.00 ,Total,8,4.25,1.67,.59,2.85,5.65,2.00,7.00 @@ -160,23 +182,23 @@ Breaking Strain,Between Groups,7.50,1,7.50,3.75,.101 ,Total,19.50,7,,, Table: Contrast Coefficients -,,Manufacturer, -,,Bloggs,Charlies -Contrast,1,-2,2 -,2,-1,1 +Contrast,Manufacturer, +,Bloggs,Charlies +1,-2,2 +2,-1,1 Table: Contrast Tests ,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) -Breaking Strain,Assume equal variances,1,4.00,2.07,1.94,6,.101 -,,2,2.00,1.03,1.94,6,.101 -,Does not assume equal,1,4.00,1.83,2.19,5.88,.072 +Breaking Strain,Assume equal variances,1,4.00,2.07,1.94,6.00,.101 +,,2,2.00,1.03,1.94,6.00,.101 +,Does not assume equal variances,1,4.00,1.83,2.19,5.88,.072 ,,2,2.00,.91,2.19,5.88,.072 ]) - 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 *. @@ -202,14 +224,14 @@ END DATA ONEWAY v1 v2 BY dep /STATISTICS descriptives homogeneity - /MISSING ANALYSIS + /MISSING ANALYSIS . ]) AT_DATA([oneway-missing2.sps], [DATA LIST NOTABLE LIST /v1 * v2 * dep * vn * . BEGIN DATA -4 . 1 2 +4 . 1 2 3 3 1 2 2 2 1 2 1 1 1 2 @@ -246,7 +268,7 @@ dnl Now a test with missing values in the independent variable AT_DATA([oneway-missing3.sps], [DATA LIST NOTABLE LIST /v1 * v2 * dep * vn * . BEGIN DATA -4 2 . 2 +4 2 . 2 3 3 1 2 2 2 1 2 1 1 1 2 @@ -282,6 +304,7 @@ AT_CLEANUP AT_SETUP([ONEWAY descriptives subcommand]) +AT_KEYWORDS([categorical categoricals]) AT_DATA([oneway-descriptives.sps], [DATA LIST NOTABLE LIST /QUALITY * BRAND * . @@ -310,10 +333,10 @@ ONEWAY . ]) -AT_CHECK([pspp -O format=csv oneway-descriptives.sps], [0], +AT_CHECK([pspp -O format=csv oneway-descriptives.sps], [0], [Table: Descriptives -,,,,,,95% Confidence Interval for Mean,,, -,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum +,BRAND,N,Mean,Std. Deviation,Std. Error,95% Confidence Interval for Mean,,Minimum,Maximum +,,,,,,Lower Bound,Upper Bound,, QUALITY,11.00,5,12.20,1.30,.58,10.58,13.82,11.00,14.00 ,25.00,5,13.20,1.30,.58,11.58,14.82,12.00,15.00 ,301.00,5,15.00,1.58,.71,13.04,16.96,13.00,17.00 @@ -331,6 +354,7 @@ AT_CLEANUP AT_SETUP([ONEWAY homogeneity subcommand]) +AT_KEYWORDS([categorical categoricals]) AT_DATA([oneway-homogeneity.sps], [DATA LIST NOTABLE LIST /QUALITY * BRAND * . @@ -359,7 +383,7 @@ ONEWAY . ]) -AT_CHECK([pspp -O format=csv oneway-homogeneity.sps], [0], +AT_CHECK([pspp -O format=csv oneway-homogeneity.sps], [0], [Table: Test of Homogeneity of Variances ,Levene Statistic,df1,df2,Sig. QUALITY,.09,2,12,.913 @@ -376,6 +400,7 @@ 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. @@ -415,13 +440,13 @@ ONEWAY x y z by g . ]) -AT_CHECK([pspp -o pspp.csv multivar.sps]) +AT_CHECK([pspp -o pspp.csv -o pspp.txt multivar.sps]) dnl Some machines return 3.88 instead of 3.87 below (see bug #31611). -AT_CHECK([sed 's/^,Within Groups,3.88/,Within Groups,3.87/' pspp.csv], [0], [dnl -Table: Descriptives -,,,,,,95% Confidence Interval for Mean,,, -,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum +AT_CHECK([sed -e 's/^,Within Groups,3.88/,Within Groups,3.87/' pspp.csv], [0], + [Table: Descriptives +,g,N,Mean,Std. Deviation,Std. Error,95% Confidence Interval for Mean,,Minimum,Maximum +,,,,,,Lower Bound,Upper Bound,, x,10.00,3,3.67,4.62,2.67,-7.81,15.14,1.00,9.00 ,20.00,5,.80,.45,.20,.24,1.36,.00,1.00 ,30.00,8,.88,.64,.23,.34,1.41,.00,2.00 @@ -457,24 +482,24 @@ z,Between Groups,17.47,3,5.82,.62,.610 ,Total,205.33,23,,, Table: Contrast Coefficients -,,g,,, -,,10.00,20.00,30.00,40.00 -Contrast,1,3,2,0,-5 -,2,2,-9,7,0 +Contrast,g,,, +,10.00,20.00,30.00,40.00 +1,3,2,0,-5 +2,2,-9,7,0 Table: Contrast Tests ,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) -x,Assume equal variances,1,-7.40,6.67,1.11,20,.280 -,,2,6.26,12.32,.51,20,.617 -,Does not assume equal,1,-7.40,10.04,-.74,4.53,.497 +x,Assume equal variances,1,-7.40,6.67,-1.11,20.00,.280 +,,2,6.26,12.32,.51,20.00,.617 +,Does not assume equal variances,1,-7.40,10.04,-.74,4.53,.497 ,,2,6.26,5.85,1.07,2.87,.366 -y,Assume equal variances,1,-6.88,1.16,5.94,20,.000 -,,2,3.50,2.14,1.63,20,.118 -,Does not assume equal,1,-6.88,.91,-7.51,7.00,.000 +y,Assume equal variances,1,-6.88,1.16,-5.94,20.00,.000 +,,2,3.50,2.14,1.63,20.00,.118 +,Does not assume equal variances,1,-6.88,.91,-7.51,7.00,.000 ,,2,3.50,1.32,2.65,7.00,.033 -z,Assume equal variances,1,-9.70,8.07,1.20,20,.243 -,,2,11.73,14.91,.79,20,.440 -,Does not assume equal,1,-9.70,9.57,-1.01,3.64,.373 +z,Assume equal variances,1,-9.70,8.07,-1.20,20.00,.243 +,,2,11.73,14.91,.79,20.00,.440 +,Does not assume equal variances,1,-9.70,9.57,-1.01,3.64,.373 ,,2,11.73,14.53,.81,9.88,.438 ]) @@ -484,6 +509,7 @@ 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 *. @@ -555,6 +581,7 @@ 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 *. @@ -588,10 +615,11 @@ oneway pigmentation by family . ]) -AT_CHECK([pspp -O format=csv oneway-pig.sps], [0], -[Table: Descriptives -,,,,,,95% Confidence Interval for Mean,,, -,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum +AT_CHECK([pspp -o pspp.csv -o pspp.txt oneway-pig.sps]) +AT_CHECK([cat pspp.csv], [0], [dnl +Table: Descriptives +,family,N,Mean,Std. Deviation,Std. Error,95% Confidence Interval for Mean,,Minimum,Maximum +,,,,,,Lower Bound,Upper Bound,, pigmentation,1.000,5,38.600,2.702,1.208,35.245,41.955,36.000,43.000 ,2.000,5,46.000,1.732,.775,43.849,48.151,43.000,47.000 ,3.000,5,46.400,4.336,1.939,41.016,51.784,40.000,50.000 @@ -605,8 +633,8 @@ pigmentation,Between Groups,478.950,3,159.650,12.927,.000 ,Total,676.550,19,,, Table: Multiple Comparisons (pigmentation) -,,,Mean Difference,,,95% Confidence Interval, -,(I) family,(J) family,(I - J),Std. Error,Sig.,Lower Bound,Upper Bound +,(J) Family,(J) Family,Mean Difference (I - J),Std. Error,Sig.,95% Confidence Interval, +,,,,,,Lower Bound,Upper Bound LSD,1.000,2.000,-7.400,2.223,.004,-12.112,-2.688 ,,3.000,-7.800,2.223,.003,-12.512,-3.088 ,,4.000,-13.800,2.223,.000,-18.512,-9.088 @@ -632,11 +660,11 @@ Bonferroni,1.000,2.000,-7.400,2.223,.025,-14.086,-.714 ,,2.000,6.400,2.223,.065,-.286,13.086 ,,3.000,6.000,2.223,.095,-.686,12.686 ]) - 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 *. @@ -666,16 +694,17 @@ oneway libido by dose /posthoc tukey gh. ]) -AT_CHECK([pspp -O format=csv oneway-tukey.sps], [0], -[Table: ANOVA +AT_CHECK([pspp -o pspp.csv -o pspp.txt oneway-tukey.sps]) +AT_CHECK([cat pspp.csv], [0], [dnl +Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. libido,Between Groups,20.133,2,10.067,5.119,.025 ,Within Groups,23.600,12,1.967,, ,Total,43.733,14,,, Table: Multiple Comparisons (libido) -,,,Mean Difference,,,95% Confidence Interval, -,(I) Dose of Viagra,(J) Dose of Viagra,(I - J),Std. Error,Sig.,Lower Bound,Upper Bound +,(J) Family,(J) Family,Mean Difference (I - J),Std. Error,Sig.,95% Confidence Interval, +,,,,,,Lower Bound,Upper Bound Tukey HSD,Placebo,1 Dose,-1.000,.887,.516,-3.366,1.366 ,,2 Doses,-2.800,.887,.021,-5.166,-.434 ,1 Dose,Placebo,1.000,.887,.516,-1.366,3.366 @@ -693,6 +722,7 @@ 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. @@ -726,7 +756,7 @@ ONEWAY /POSTHOC = SIDAK. ]) -AT_CHECK([pspp -O format=csv oneway-sidak.sps], [0], +AT_CHECK([pspp -O format=csv oneway-sidak.sps], [0], [Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. score,Between Groups,54.9500,3,18.3167,7.0449,.003 @@ -734,8 +764,8 @@ score,Between Groups,54.9500,3,18.3167,7.0449,.003 ,Total,96.5500,19,,, Table: Multiple Comparisons (score) -,,,Mean Difference,,,95% Confidence Interval, -,(I) program,(J) program,(I - J),Std. Error,Sig.,Lower Bound,Upper Bound +,(J) Family,(J) Family,Mean Difference (I - J),Std. Error,Sig.,95% Confidence Interval, +,,,,,,Lower Bound,Upper Bound Šidák,1.0000,2.0000,3.0000,1.0198,.056,-.0575,6.0575 ,,3.0000,-.4000,1.0198,.999,-3.4575,2.6575 ,,4.0000,3.2000,1.0198,.038,.1425,6.2575 @@ -753,6 +783,7 @@ 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 *. @@ -807,7 +838,7 @@ oneway usage by group /posthoc scheffe. ]) -AT_CHECK([pspp -O format=csv oneway-scheffe.sps], [0], +AT_CHECK([pspp -O format=csv oneway-scheffe.sps], [0], [Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. Days of Use,Between Groups,555.275,3,185.092,6.663,.001 @@ -815,8 +846,8 @@ Days of Use,Between Groups,555.275,3,185.092,6.663,.001 ,Total,1555.375,39,,, Table: Multiple Comparisons (Days of Use) -,,,Mean Difference,,,95% Confidence Interval, -,(I) group,(J) group,(I - J),Std. Error,Sig.,Lower Bound,Upper Bound +,(J) Family,(J) Family,Mean Difference (I - J),Std. Error,Sig.,95% Confidence Interval, +,,,,,,Lower Bound,Upper Bound Scheffé,one,two,3.800,2.357,.467,-3.112,10.712 ,,three,10.300,2.357,.001,3.388,17.212 ,,four,6.000,2.357,.110,-.912,12.912 @@ -835,6 +866,7 @@ 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 *. @@ -850,15 +882,18 @@ BEGIN DATA. END DATA. ONEWAY /VARIABLES= height weight temperature BY sex - /CONTRAST = -1 1 - /CONTRAST = -3 3 + /CONTRAST = -1 1 + /CONTRAST = -3 3 /CONTRAST = 2 -2 1 /CONTRAST = -9 9 - . + . ]) -AT_CHECK([pspp -O format=csv oneway-bad-contrast.sps], [0], [dnl +AT_CHECK([pspp -o pspp.csv -o pspp.txt oneway-bad-contrast.sps], [0], [dnl +oneway-bad-contrast.sps:18: warning: ONEWAY: In contrast list 3, the number of coefficients (3) does not equal the number of groups (2). This contrast list will be ignored. +]) +AT_CHECK([cat pspp.csv], [0], [dnl "oneway-bad-contrast.sps:18: warning: ONEWAY: In contrast list 3, the number of coefficients (3) does not equal the number of groups (2). This contrast list will be ignored." Table: ANOVA @@ -874,38 +909,38 @@ temperature,Between Groups,1.80,1,1.80,.13,.733 ,Total,86.36,7,,, Table: Contrast Coefficients -,,sex, -,,.00,1.00 -Contrast,1,-1,1 -,2,-3,3 -,3,-9,9 +Contrast,sex, +,.00,1.00 +1,-1,1 +2,-3,3 +3,-9,9 Table: Contrast Tests ,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) -height,Assume equal variances,1,-222.27,20.23,10.99,6,.000 -,,2,-666.80,60.68,10.99,6,.000 -,,3,-2000.40,182.03,10.99,6,.000 -,Does not assume equal,1,-222.27,27.67,-8.03,2.00,.015 +height,Assume equal variances,1,-222.27,20.23,-10.99,6.00,.000 +,,2,-666.80,60.68,-10.99,6.00,.000 +,,3,-2000.40,182.03,-10.99,6.00,.000 +,Does not assume equal variances,1,-222.27,27.67,-8.03,2.00,.015 ,,2,-666.80,83.02,-8.03,2.00,.015 ,,3,-2000.40,249.07,-8.03,2.00,.015 -weight,Assume equal variances,1,-36.16,2.74,13.21,6,.000 -,,2,-108.48,8.21,13.21,6,.000 -,,3,-325.44,24.63,13.21,6,.000 -,Does not assume equal,1,-36.16,2.19,-16.48,5.42,.000 +weight,Assume equal variances,1,-36.16,2.74,-13.21,6.00,.000 +,,2,-108.48,8.21,-13.21,6.00,.000 +,,3,-325.44,24.63,-13.21,6.00,.000 +,Does not assume equal variances,1,-36.16,2.19,-16.48,5.42,.000 ,,2,-108.48,6.58,-16.48,5.42,.000 ,,3,-325.44,19.75,-16.48,5.42,.000 -temperature,Assume equal variances,1,-.98,2.74,.36,6,.733 -,,2,-2.94,8.22,.36,6,.733 -,,3,-8.83,24.67,.36,6,.733 -,Does not assume equal,1,-.98,2.07,-.47,4.19,.660 +temperature,Assume equal variances,1,-.98,2.74,-.36,6.00,.733 +,,2,-2.94,8.22,-.36,6.00,.733 +,,3,-8.83,24.67,-.36,6.00,.733 +,Does not assume equal variances,1,-.98,2.07,-.47,4.19,.660 ,,2,-2.94,6.22,-.47,4.19,.660 ,,3,-8.83,18.66,-.47,4.19,.660 ]) - 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. @@ -927,23 +962,24 @@ 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. -2 . 2 -1 . 2 -1 . 1 -2 . 4 +2 . 2 +1 . 2 +1 . 1 +2 . 4 3 . 4 -4 . 4 -5 . 4 +4 . 4 +5 . 4 end data. -ONEWAY +ONEWAY /VARIABLES= dv1 dv2 BY y /STATISTICS = DESCRIPTIVES /POSTHOC = BONFERRONI LSD SCHEFFE SIDAK TUKEY - /MISSING = ANALYSIS + /MISSING = ANALYSIS . ]) @@ -955,6 +991,7 @@ 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. @@ -968,7 +1005,7 @@ end data. ONEWAY /VARIABLES= dv BY y /POSTHOC = GH - . + . ]) AT_CHECK([pspp -O format=csv crash3.sps], [0], [ignore]) @@ -977,6 +1014,7 @@ 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. @@ -995,3 +1033,113 @@ ONEWAY /VARIABLES= height weight temperature BY sex AT_CHECK([pspp -O format=csv crash4.sps], [0], [ignore]) 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. +3 0 +2 0 +6 2 +end data. + +oneway a"by b. + +]) + +AT_CHECK([pspp -O format=csv crash5.sps], [1], [ignore]) + +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 *. +BEGIN DATA. +1801 . . 0 +1606 . 0 . 1 +END DATA. + +ONEWAY /VARIABLES= height weight temperature BY sex + /CONTRAST =" 2 -2 1 + . +]) + +AT_CHECK([pspp -O format=csv crash6.sps], [1], [ignore]) + +AT_CLEANUP + + +AT_SETUP([ONEWAY contrast bug]) + +AT_KEYWORDS([categorical categoricals]) + + + +dnl this example comes from: https://case.truman.edu/files/2015/06/SPSS-One-Way-ANOVA.pdf +AT_DATA([contrasts.sps], +[ +SET FORMAT=F10.3. + +DATA LIST notable LIST /relieftime drugs *. +begin data. +12 0 +15 0 +18 0 +16 0 +20 0 +20 1 +21 1 +22 1 +19 1 +20 1 +17 2 +16 2 +19 2 +15 2 +19 2 +14 3 +13 3 +12 3 +14 3 +11 3 +end data. + +ONEWAY relieftime by drugs + /CONTRAST 3 -1 -1 -1 + /CONTRAST 0 2 -1 -1 + /CONTRAST 0 0 1 -1 + . +]) + +AT_CHECK([pspp -O format=csv contrasts.sps], [0], [Table: ANOVA +,,Sum of Squares,df,Mean Square,F,Sig. +relieftime,Between Groups,146.950,3,48.983,12.723,.000 +,Within Groups,61.600,16,3.850,, +,Total,208.550,19,,, + +Table: Contrast Coefficients +Contrast,drugs,,, +,.000,1.000,2.000,3.000 +1,3,-1,-1,-1 +2,0,2,-1,-1 +3,0,0,1,-1 + +Table: Contrast Tests +,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) +relieftime,Assume equal variances,1,-1.800,3.040,-.592,16.000,.562 +,,2,10.800,2.149,5.025,16.000,.000 +,,3,4.400,1.241,3.546,16.000,.003 +,Does not assume equal variances,1,-1.800,4.219,-.427,4.611,.689 +,,2,10.800,1.421,7.599,10.158,.000 +,,3,4.400,.990,4.445,7.315,.003 +]) + +AT_CLEANUP