[R] Sum Sq of SPSS and R different for repeated measures Anova

[Paul Lemmens]

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"