Paul Lemmens
2004-Apr-23 13:06 UTC
[R] Sum Sq of SPSS and R different for repeated measures Anova
Dear all,
I'm still learning and transitioning from SPSS to R (1.9.0, winXP) and
today I have data from two repeated measures experiments. For each of the
subjects I've averaged for two within-SS factors (2 x 2, 4 means per
subjects). One experiment had 16 subjects, the other one 25 (between-SS
factor exp). So I have something like:
avg.cond <- read.table('data.txt') # data set attached as text.
avg.cond[1:5,]
# pp pictcat cond rt exp
#1 1 animal con 517.8125 exp11b
#2 2 animal con 425.9375 exp11b
#3 3 animal con 379.6563 exp11b
#4 4 animal con 410.6563 exp11b
#5 5 animal con 420.3125 exp11b
Then I do the anova:
summary(taov <- aov(rt~exp*pictcat*cond+Error(pp/(pictcat*cond)),
data=subset(avg.cond, cond=='con'|cond=='incon')))
#
#Error: pp
# Df Sum Sq Mean Sq F value Pr(>F)
#exp 1 178 178 0.0102 0.9202
#Residuals 39 683329 17521
#
#Error: pp:pictcat
# Df Sum Sq Mean Sq F value Pr(>F)
#pictcat 1 62382 62382 87.334 1.68e-11 ***
#exp:pictcat 1 653 653 0.914 0.3449
#Residuals 39 27858 714
#---
#Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 `
' 1
#
#Error: pp:cond
# Df Sum Sq Mean Sq F value Pr(>F)
#cond 1 6550.2 6550.2 9.3057 0.004095 **
#exp:cond 1 206.3 206.3 0.2931 0.591313
#Residuals 39 27451.9 703.9
#---
#Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 `
' 1
#
#Error: pp:pictcat:cond
# Df Sum Sq Mean Sq F value Pr(>F)
#pictcat:cond 1 517.4 517.4 1.8747 0.1788
#exp:pictcat:cond 1 67.3 67.3 0.2439 0.6241
#Residuals 39 10763.2 276.0
Now I want to verify these results with how I used to do it in SPSS (from
it's menu): Analyze > General Linear Model > Repeated Measures (with
the
data set suitably rearranged to conform to SPSS' format). Comparing the
"Tests of Within-Subject Effects" (I table a cannot reproduce in this
medium) with the above results, I find identical results but for the main
effects of pictcat and cond and their interaction. The Sum of squares of R
are slightly larger (in itself not a bad thing because it results in larger
F-ratio's) than those of SPSS (resp., 62141, 5747, 416 for pictcat, cond,
pictcat:cond). The funny thing is that the SumSq are spot on for the
interactions with the between-SS factor exp. The data for both analyses are
exactly the same.
I've read about R using type I SS, but if I change the type III default of
SPSS into type I, the results get more different, instead more more
comparable. I've read about drop1, but
drop1(taov, test="F")
#Error in extractAIC(object, scale, k = k, ...) :
# no applicable method for "extractAIC"
Also
library(car)
Anova(taov, type="III")
#Error in Anova(taov, type = "III") : no applicable method for
"Anova"
which I found in the archives doesn't work. As the data for R and SPSS are
identical and because the interactions with exp are identical between R and
SPSS, there is something else going on (if it were the type I/III
difference then the exp interactions would have been different as well,
right?). I cannot deduce the reason of the difference. Can anybody help
with this?
thank you for your time,
regards,
Paul Lemmens
--
Paul Lemmens
NICI, University of Nijmegen ASCII Ribbon Campaign /"\
Montessorilaan 3 (B.01.05) Against HTML Mail \ /
NL-6525 HR Nijmegen X
The Netherlands / \
Phonenumber +31-24-3612648
Fax +31-24-3616066
-------------- next part --------------
"pp" "pictcat" "cond" "rt"
"exp"
"1" "1" "animal" "con" 517.8125
"exp11b"
"2" "2" "animal" "con" 425.9375
"exp11b"
"3" "3" "animal" "con" 379.6563
"exp11b"
"4" "4" "animal" "con" 410.6563
"exp11b"
"5" "5" "animal" "con" 420.3125
"exp11b"
"6" "6" "animal" "con" 381.1613
"exp11b"
"7" "7" "animal" "con" 423.1875
"exp11b"
"8" "8" "animal" "con" 397.8387
"exp11b"
"9" "9" "animal" "con" 404.5714
"exp11b"
"10" "10" "animal" "con" 481.5000
"exp11b"
"11" "11" "animal" "con" 364.6875
"exp11b"
"12" "12" "animal" "con" 397.0938
"exp11b"
"13" "13" "animal" "con" 375.4375
"exp11b"
"14" "14" "animal" "con" 345.5313
"exp11b"
"15" "15" "animal" "con" 378.3548
"exp11b"
"16" "16" "animal" "con" 476.5161
"exp11b"
"17" "17" "animal" "con" 455.3750
"exp11b"
"18" "18" "animal" "con" 374.3125
"exp11b"
"19" "19" "animal" "con" 387.6250
"exp11b"
"20" "20" "animal" "con" 355.8750
"exp11b"
"21" "21" "animal" "con" 321.7097
"exp11b"
"22" "22" "animal" "con" 452.7813
"exp11b"
"23" "23" "animal" "con" 373.1290
"exp11b"
"24" "24" "animal" "con" 438.4063
"exp11b"
"25" "25" "animal" "con" 456.8125
"exp11b"
"26" "1" "other" "con" 470.4375
"exp11b"
"27" "2" "other" "con" 463.0938
"exp11b"
"28" "3" "other" "con" 463.1563
"exp11b"
"29" "4" "other" "con" 490.9063
"exp11b"
"30" "5" "other" "con" 467.4375
"exp11b"
"31" "6" "other" "con" 428.8125
"exp11b"
"32" "7" "other" "con" 459.2813
"exp11b"
"33" "8" "other" "con" 441.9375
"exp11b"
"34" "9" "other" "con" 397.2903
"exp11b"
"35" "10" "other" "con" 467.2500
"exp11b"
"36" "11" "other" "con" 431.3548
"exp11b"
"37" "12" "other" "con" 460.5625
"exp11b"
"38" "13" "other" "con" 401.8438
"exp11b"
"39" "14" "other" "con" 417.8438
"exp11b"
"40" "15" "other" "con" 414.6875
"exp11b"
"41" "16" "other" "con" 489.9375
"exp11b"
"42" "17" "other" "con" 485.1563
"exp11b"
"43" "18" "other" "con" 367.6250
"exp11b"
"44" "19" "other" "con" 422.1875
"exp11b"
"45" "20" "other" "con" 381.2500
"exp11b"
"46" "21" "other" "con" 346.1875
"exp11b"
"47" "22" "other" "con" 442.3438
"exp11b"
"48" "23" "other" "con" 412.7097
"exp11b"
"49" "24" "other" "con" 444.5313
"exp11b"
"50" "25" "other" "con" 509.3750
"exp11b"
"51" "1" "animal" "incon" 519.0313
"exp11b"
"52" "2" "animal" "incon" 465.9355
"exp11b"
"53" "3" "animal" "incon" 362.6875
"exp11b"
"54" "4" "animal" "incon" 417.1875
"exp11b"
"55" "5" "animal" "incon" 404.5625
"exp11b"
"56" "6" "animal" "incon" 394.9063
"exp11b"
"57" "7" "animal" "incon" 431.2500
"exp11b"
"58" "8" "animal" "incon" 421.0938
"exp11b"
"59" "9" "animal" "incon" 416.2903
"exp11b"
"60" "10" "animal" "incon" 507.4688
"exp11b"
"61" "11" "animal" "incon" 334.3871
"exp11b"
"62" "12" "animal" "incon" 402.4667
"exp11b"
"63" "13" "animal" "incon" 377.9063
"exp11b"
"64" "14" "animal" "incon" 362.6452
"exp11b"
"65" "15" "animal" "incon" 342.9355
"exp11b"
"66" "16" "animal" "incon" 475.1250
"exp11b"
"67" "17" "animal" "incon" 557.7500
"exp11b"
"68" "18" "animal" "incon" 361.0968
"exp11b"
"69" "19" "animal" "incon" 421.2813
"exp11b"
"70" "20" "animal" "incon" 345.1563
"exp11b"
"71" "21" "animal" "incon" 358.2258
"exp11b"
"72" "22" "animal" "incon" 484.9677
"exp11b"
"73" "23" "animal" "incon" 374.5000
"exp11b"
"74" "24" "animal" "incon" 445.3438
"exp11b"
"75" "25" "animal" "incon" 458.5000
"exp11b"
"76" "1" "other" "incon" 526.5625
"exp11b"
"77" "2" "other" "incon" 504.3871
"exp11b"
"78" "3" "other" "incon" 434.9375
"exp11b"
"79" "4" "other" "incon" 431.6563
"exp11b"
"80" "5" "other" "incon" 486.1563
"exp11b"
"81" "6" "other" "incon" 424.8065
"exp11b"
"82" "7" "other" "incon" 456.5161
"exp11b"
"83" "8" "other" "incon" 481.0625
"exp11b"
"84" "9" "other" "incon" 397.0345
"exp11b"
"85" "10" "other" "incon" 506.9375
"exp11b"
"86" "11" "other" "incon" 423.6000
"exp11b"
"87" "12" "other" "incon" 525.1935
"exp11b"
"88" "13" "other" "incon" 417.3125
"exp11b"
"89" "14" "other" "incon" 420.4688
"exp11b"
"90" "15" "other" "incon" 422.1250
"exp11b"
"91" "16" "other" "incon" 481.0000
"exp11b"
"92" "17" "other" "incon" 517.5625
"exp11b"
"93" "18" "other" "incon" 367.1250
"exp11b"
"94" "19" "other" "incon" 460.4688
"exp11b"
"95" "20" "other" "incon" 406.4688
"exp11b"
"96" "21" "other" "incon" 383.0625
"exp11b"
"97" "22" "other" "incon" 570.7097
"exp11b"
"98" "23" "other" "incon" 429.8438
"exp11b"
"99" "24" "other" "incon" 465.6250
"exp11b"
"100" "25" "other" "incon" 511.8710
"exp11b"
"101" "1" "animal" "neut" 500.8750
"exp11b"
"102" "2" "animal" "neut" 431.7813
"exp11b"
"103" "3" "animal" "neut" 362.3125
"exp11b"
"104" "4" "animal" "neut" 385.3438
"exp11b"
"105" "5" "animal" "neut" 390.2500
"exp11b"
"106" "6" "animal" "neut" 415.4375
"exp11b"
"107" "7" "animal" "neut" 508.7813
"exp11b"
"108" "8" "animal" "neut" 395.6563
"exp11b"
"109" "9" "animal" "neut" 419.9000
"exp11b"
"110" "10" "animal" "neut" 469.1250
"exp11b"
"111" "11" "animal" "neut" 356.8125
"exp11b"
"112" "12" "animal" "neut" 394.5000
"exp11b"
"113" "13" "animal" "neut" 378.3125
"exp11b"
"114" "14" "animal" "neut" 363.9063
"exp11b"
"115" "15" "animal" "neut" 377.5484
"exp11b"
"116" "16" "animal" "neut" 489.9677
"exp11b"
"117" "17" "animal" "neut" 443.8750
"exp11b"
"118" "18" "animal" "neut" 385.9688
"exp11b"
"119" "19" "animal" "neut" 429.6774
"exp11b"
"120" "20" "animal" "neut" 344.2258
"exp11b"
"121" "21" "animal" "neut" 332.6207
"exp11b"
"122" "22" "animal" "neut" 438.7813
"exp11b"
"123" "23" "animal" "neut" 384.5161
"exp11b"
"124" "24" "animal" "neut" 425.8438
"exp11b"
"125" "25" "animal" "neut" 455.7188
"exp11b"
"126" "1" "other" "neut" 475.3750
"exp11b"
"127" "2" "other" "neut" 462.2903
"exp11b"
"128" "3" "other" "neut" 420.7188
"exp11b"
"129" "4" "other" "neut" 420.6875
"exp11b"
"130" "5" "other" "neut" 465.3750
"exp11b"
"131" "6" "other" "neut" 410.6250
"exp11b"
"132" "7" "other" "neut" 518.1250
"exp11b"
"133" "8" "other" "neut" 452.4194
"exp11b"
"134" "9" "other" "neut" 420.8667
"exp11b"
"135" "10" "other" "neut" 448.3125
"exp11b"
"136" "11" "other" "neut" 460.4333
"exp11b"
"137" "12" "other" "neut" 436.8387
"exp11b"
"138" "13" "other" "neut" 425.4688
"exp11b"
"139" "14" "other" "neut" 409.1563
"exp11b"
"140" "15" "other" "neut" 401.7500
"exp11b"
"141" "16" "other" "neut" 494.1563
"exp11b"
"142" "17" "other" "neut" 454.3438
"exp11b"
"143" "18" "other" "neut" 421.1250
"exp11b"
"144" "19" "other" "neut" 426.9688
"exp11b"
"145" "20" "other" "neut" 393.2813
"exp11b"
"146" "21" "other" "neut" 366.5625
"exp11b"
"147" "22" "other" "neut" 484.7813
"exp11b"
"148" "23" "other" "neut" 411.0313
"exp11b"
"149" "24" "other" "neut" 448.6250
"exp11b"
"150" "25" "other" "neut" 474.6875
"exp11b"
"151" "1" "animal" "silent" 496.1667
"exp11b"
"152" "2" "animal" "silent" 447.7312
"exp11b"
"153" "3" "animal" "silent" 393.0104
"exp11b"
"154" "4" "animal" "silent" 390.1354
"exp11b"
"155" "5" "animal" "silent" 424.0729
"exp11b"
"156" "6" "animal" "silent" 399.3298
"exp11b"
"157" "7" "animal" "silent" 450.5208
"exp11b"
"158" "8" "animal" "silent" 409.2500
"exp11b"
"159" "9" "animal" "silent" 419.7419
"exp11b"
"160" "10" "animal" "silent" 439.7396
"exp11b"
"161" "11" "animal" "silent" 366.6042
"exp11b"
"162" "12" "animal" "silent" 439.2111
"exp11b"
"163" "13" "animal" "silent" 391.3333
"exp11b"
"164" "14" "animal" "silent" 367.5638
"exp11b"
"165" "15" "animal" "silent" 352.5745
"exp11b"
"166" "16" "animal" "silent" 518.7204
"exp11b"
"167" "17" "animal" "silent" 462.0104
"exp11b"
"168" "18" "animal" "silent" 389.6882
"exp11b"
"169" "19" "animal" "silent" 440.1579
"exp11b"
"170" "20" "animal" "silent" 354.1684
"exp11b"
"171" "21" "animal" "silent" 334.9560
"exp11b"
"172" "22" "animal" "silent" 459.4842
"exp11b"
"173" "23" "animal" "silent" 403.8542
"exp11b"
"174" "24" "animal" "silent" 429.6458
"exp11b"
"175" "25" "animal" "silent" 441.8438
"exp11b"
"176" "1" "other" "silent" 499.5000
"exp11b"
"177" "2" "other" "silent" 460.4792
"exp11b"
"178" "3" "other" "silent" 434.3125
"exp11b"
"179" "4" "other" "silent" 428.8750
"exp11b"
"180" "5" "other" "silent" 479.2708
"exp11b"
"181" "6" "other" "silent" 418.8617
"exp11b"
"182" "7" "other" "silent" 460.8958
"exp11b"
"183" "8" "other" "silent" 462.7396
"exp11b"
"184" "9" "other" "silent" 411.1354
"exp11b"
"185" "10" "other" "silent" 437.8438
"exp11b"
"186" "11" "other" "silent" 430.8152
"exp11b"
"187" "12" "other" "silent" 439.4149
"exp11b"
"188" "13" "other" "silent" 407.5579
"exp11b"
"189" "14" "other" "silent" 405.6316
"exp11b"
"190" "15" "other" "silent" 398.0426
"exp11b"
"191" "16" "other" "silent" 484.2766
"exp11b"
"192" "17" "other" "silent" 465.6250
"exp11b"
"193" "18" "other" "silent" 377.5208
"exp11b"
"194" "19" "other" "silent" 429.3542
"exp11b"
"195" "20" "other" "silent" 390.1915
"exp11b"
"196" "21" "other" "silent" 355.4375
"exp11b"
"197" "22" "other" "silent" 461.4479
"exp11b"
"198" "23" "other" "silent" 432.6000
"exp11b"
"199" "24" "other" "silent" 446.1895
"exp11b"
"200" "25" "other" "silent" 523.1042
"exp11b"
"1100" "31" "animal" "con" 379.7188
"exp11f"
"210" "32" "animal" "con" 429.0000
"exp11f"
"310" "33" "animal" "con" 347.4688
"exp11f"
"410" "34" "animal" "con" 375.1333
"exp11f"
"510" "35" "animal" "con" 390.9375
"exp11f"
"610" "40" "animal" "con" 314.8387
"exp11f"
"710" "41" "animal" "con" 480.8125
"exp11f"
"810" "42" "animal" "con" 401.8125
"exp11f"
"910" "43" "animal" "con" 731.3750
"exp11f"
"1010" "44" "animal" "con" 399.9688
"exp11f"
"1110" "45" "animal" "con" 398.4483
"exp11f"
"1210" "46" "animal" "con" 406.1290
"exp11f"
"1310" "47" "animal" "con" 428.0968
"exp11f"
"1410" "48" "animal" "con" 390.6333
"exp11f"
"1510" "49" "animal" "con" 297.7419
"exp11f"
"1610" "50" "animal" "con" 338.0000
"exp11f"
"1710" "31" "other" "con" 434.2188
"exp11f"
"1810" "32" "other" "con" 488.6207
"exp11f"
"1910" "33" "other" "con" 422.9355
"exp11f"
"201" "34" "other" "con" 416.0625
"exp11f"
"211" "35" "other" "con" 409.1875
"exp11f"
"221" "40" "other" "con" 360.1563
"exp11f"
"231" "41" "other" "con" 574.5625
"exp11f"
"241" "42" "other" "con" 477.4516
"exp11f"
"251" "43" "other" "con" 744.6563
"exp11f"
"261" "44" "other" "con" 415.6452
"exp11f"
"271" "45" "other" "con" 418.7857
"exp11f"
"281" "46" "other" "con" 453.5938
"exp11f"
"291" "47" "other" "con" 440.2500
"exp11f"
"301" "48" "other" "con" 428.1250
"exp11f"
"311" "49" "other" "con" 350.1333
"exp11f"
"321" "50" "other" "con" 348.4375
"exp11f"
"331" "31" "animal" "incon" 399.0938
"exp11f"
"341" "32" "animal" "incon" 406.0333
"exp11f"
"351" "33" "animal" "incon" 388.7419
"exp11f"
"361" "34" "animal" "incon" 384.1935
"exp11f"
"371" "35" "animal" "incon" 429.3750
"exp11f"
"381" "40" "animal" "incon" 320.5000
"exp11f"
"391" "41" "animal" "incon" 511.9375
"exp11f"
"401" "42" "animal" "incon" 452.1563
"exp11f"
"411" "43" "animal" "incon" 666.0313
"exp11f"
"421" "44" "animal" "incon" 394.1613
"exp11f"
"431" "45" "animal" "incon" 399.3571
"exp11f"
"441" "46" "animal" "incon" 434.2903
"exp11f"
"451" "47" "animal" "incon" 435.1875
"exp11f"
"461" "48" "animal" "incon" 379.6250
"exp11f"
"471" "49" "animal" "incon" 302.9667
"exp11f"
"481" "50" "animal" "incon" 332.6250
"exp11f"
"491" "31" "other" "incon" 464.6875
"exp11f"
"501" "32" "other" "incon" 479.9688
"exp11f"
"511" "33" "other" "incon" 473.8750
"exp11f"
"521" "34" "other" "incon" 407.5000
"exp11f"
"531" "35" "other" "incon" 453.5484
"exp11f"
"541" "40" "other" "incon" 357.9355
"exp11f"
"551" "41" "other" "incon" 549.5625
"exp11f"
"561" "42" "other" "incon" 497.7742
"exp11f"
"571" "43" "other" "incon" 700.1563
"exp11f"
"581" "44" "other" "incon" 424.5172
"exp11f"
"591" "45" "other" "incon" 424.9630
"exp11f"
"601" "46" "other" "incon" 486.6563
"exp11f"
"611" "47" "other" "incon" 531.3438
"exp11f"
"621" "48" "other" "incon" 439.3438
"exp11f"
"631" "49" "other" "incon" 341.2143
"exp11f"
"641" "50" "other" "incon" 338.3548
"exp11f"
"651" "31" "animal" "neut" 401.1250
"exp11f"
"661" "32" "animal" "neut" 489.5484
"exp11f"
"671" "33" "animal" "neut" 342.4688
"exp11f"
"681" "34" "animal" "neut" 379.7586
"exp11f"
"691" "35" "animal" "neut" 395.9688
"exp11f"
"701" "40" "animal" "neut" 308.5806
"exp11f"
"711" "41" "animal" "neut" 507.3125
"exp11f"
"721" "42" "animal" "neut" 425.2813
"exp11f"
"731" "43" "animal" "neut" 773.1875
"exp11f"
"741" "44" "animal" "neut" 377.4194
"exp11f"
"751" "45" "animal" "neut" 406.0714
"exp11f"
"761" "46" "animal" "neut" 401.5938
"exp11f"
"771" "47" "animal" "neut" 418.8710
"exp11f"
"781" "48" "animal" "neut" 380.4516
"exp11f"
"791" "49" "animal" "neut" 300.7188
"exp11f"
"801" "50" "animal" "neut" 325.2258
"exp11f"
"811" "31" "other" "neut" 449.9063
"exp11f"
"821" "32" "other" "neut" 558.5000
"exp11f"
"831" "33" "other" "neut" 414.8065
"exp11f"
"841" "34" "other" "neut" 392.5161
"exp11f"
"851" "35" "other" "neut" 413.3125
"exp11f"
"861" "40" "other" "neut" 345.2581
"exp11f"
"871" "41" "other" "neut" 498.5313
"exp11f"
"881" "42" "other" "neut" 460.8750
"exp11f"
"891" "43" "other" "neut" 849.8125
"exp11f"
"901" "44" "other" "neut" 396.8333
"exp11f"
"911" "45" "other" "neut" 459.3000
"exp11f"
"921" "46" "other" "neut" 449.0000
"exp11f"
"931" "47" "other" "neut" 470.5313
"exp11f"
"941" "48" "other" "neut" 399.9677
"exp11f"
"951" "49" "other" "neut" 364.9688
"exp11f"
"961" "50" "other" "neut" 302.6129
"exp11f"
"971" "31" "animal" "silent" 399.0421
"exp11f"
"981" "32" "animal" "silent" 469.4066
"exp11f"
"991" "33" "animal" "silent" 375.7935
"exp11f"
"1001" "34" "animal" "silent" 388.0323
"exp11f"
"1011" "35" "animal" "silent" 404.2917
"exp11f"
"1021" "40" "animal" "silent" 319.1489
"exp11f"
"1031" "41" "animal" "silent" 469.3263
"exp11f"
"1041" "42" "animal" "silent" 448.0538
"exp11f"
"1051" "43" "animal" "silent" 679.1684
"exp11f"
"1061" "44" "animal" "silent" 398.3778
"exp11f"
"1071" "45" "animal" "silent" 393.2308
"exp11f"
"1081" "46" "animal" "silent" 436.5054
"exp11f"
"1091" "47" "animal" "silent" 402.4894
"exp11f"
"1101" "48" "animal" "silent" 398.0000
"exp11f"
"1111" "49" "animal" "silent" 305.5914
"exp11f"
"1121" "50" "animal" "silent" 332.3804
"exp11f"
"1131" "31" "other" "silent" 441.2947
"exp11f"
"1141" "32" "other" "silent" 518.7957
"exp11f"
"1151" "33" "other" "silent" 425.0213
"exp11f"
"1161" "34" "other" "silent" 401.6809
"exp11f"
"1171" "35" "other" "silent" 417.8105
"exp11f"
"1181" "40" "other" "silent" 351.2234
"exp11f"
"1191" "41" "other" "silent" 493.2708
"exp11f"
"1201" "42" "other" "silent" 477.5851
"exp11f"
"1211" "43" "other" "silent" 739.9063
"exp11f"
"1221" "44" "other" "silent" 397.1170
"exp11f"
"1231" "45" "other" "silent" 424.6316
"exp11f"
"1241" "46" "other" "silent" 461.3118
"exp11f"
"1251" "47" "other" "silent" 437.8065
"exp11f"
"1261" "48" "other" "silent" 427.5326
"exp11f"
"1271" "49" "other" "silent" 351.5699
"exp11f"
"1281" "50" "other" "silent" 328.5104
"exp11f"
Possibly Parallel Threads
Wisdom of the Ancients
