相同的错误来自哪里，LMER测试？

我试图在数据集上执行LMER测试（附加原始数据），所有列的行数相同（153）。然而，当我尝试拟合公式时，它给了我一个错误

错误：每个分组因子的级别数必须<观察数（问题：文件名）

假设您希望演讲者是随机效果，并且进一步假设文件名实际上是根据演讲者的首字母和发音音素标记的，那么您只需要在第一列中使用首字母作为随机效果。否则，在随机效应的每一个层次上，你只有一个观察，这没有多大意义

因此，如果您这样做：

dpun公式：C2~辅音+位置+（1 |文件名）
#>数据：内（pun_数据，文件名
#>收敛时的REML标准：-382.5
#> 
#>标度残差：
#>最小1季度中值3季度最大值
#> -2.1938 -0.3797 -0.1103  0.2766  7.2708 
#> 
#>随机效应：
#>组名为Variance Std.Dev。
#>文件名（截取）0.001161 0.03408
#>剩余0.003385 0.05818
#>OB数量：153，组：文件名，16
#> 
#>固定效果：
#>估计标准误差t值
#>（截距）0.19071 0.01429 13.347
#>辅音单音-0.10551 0.00963-10.956
#>PlaceDental/牙槽骨0.01575 0.01256 1.254
#>PlaceRetroflex-0.02010.01525-1.443
#>PlaceVelar-0.03163 0.01468-2.155
#> 
#>固定效应的相关性：
#>（国际）CNSNS PlcDn/PlcRtr
#>CnsnntSnglt-0.371
#>PlcDntl/lvl-0.561-0.028
#>PlaceRtrflx-0.479 0.021 0.538
#>PlaceVelar-0.504 0.033 0.557 0.467

你每个文件名只有一个观察值，这绝对没有意义。你无法估计n=1对我的响应延迟的影响，@Allan。你输入的代码，在data=in（pun_data，Filename）中

dpun <- lmer (C2 ~ Consonant +  Place + (1|Filename), data = pun_data)

"Filename","Consonant","Manner","Voicing","Place","Gender","Beforevowel","C2.xsampa","C2","V1.xsampa","V1","V2.xsampa","V2"
"AK_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","F","Short","p",0.080042611,"@",0.059323219,"e:",0.090162588
"DS_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","M","Short","p",0.084378223,"@",0.070595707,"e:",0.077437615
"MS_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","F","Short","p",0.083394356,"@",0.068241136,"e: ",0.075200995
"NS_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","F","Short","p",0.088423147,"@",0.064472947,"e:",0.082275418
"PS_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","F","Short","p",0.083825511,"@",0.068070108,"e:",0.088299906
"RB_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","M","Short","p",0.03153568,"@",0.072380665,"e:",0.093256049
"RJ_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","F","Short","p",0.059901696,"@",0.074511565,"e:",0.086348038
"RL_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","M","Short","p",0.053055919,"@",0.068229353,"e:",0.106420617
"RR_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","F","Short","p",0.074511565,"@",0.05598852,"e:",0.106420617
"RS_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","M","Short","p",0.087676077,"@",0.06156503,"e:",0.119033444
"RS1_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","M","Short","p",0.087676077,"@",0.065995738,"e: ",0.095309543
"SD_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","M","Short","p",0.108000723,"@",0.067095676,"e: ",0.075882479
"SK_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","F","Short","p",0.088362331,"@",0.074699013,"e:",0.093881287
"SS2_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","F","Short","p",0.121307068,"@",0.05314411,"e:",0.080787887
"SS_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","F","Short","p",0.094495163,"@",0.057527086,"e:",0.1158376
"VG_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","F","Short","p",0.095932888,"@",0.045631368,"e:",0.107748222
"VS_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","M","Short","p",0.0750986,"@",0.0565392,"e:",0.096957258
"YP_chape.TextGrid","Singleton","Stop","Voiceless","Bilabial","M","Short","p",0.065231908,"@",0.035393865,"e: ",0.094684148
"AK_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","F","Short","p:",0.164554347,"@",0.049063492,"e:",0.086199108
"DS_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","M","Short","p:",0.193487061,"@",0.042937931,"e:",0.092506693
"MS_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","F","Short","p:",0.297477548,"@",0.063837869,"e:",0.102937129
"PS_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","F","Short","p:",0.109370911,"@",0.058301146,"e:",0.093773011
"RB_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","M","Short","p:",0.141575459,"@",0.058301146,"e:",0.106805666
"RJ_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","F","Short","p:",0.120253656,"@",0.075551945,"e:",0.079540514
"RL_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","M","Short","p:",0.126627788,"@",0.046278333,"e:",0.079540514
"RR_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","F","Short","p:",0.144371796,"@",0.055158945,"e:",0.053715922
"RS_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","M","Short","p:",0.171831708,"@",0.035605235,"e: ",0.097907127
"RS1_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","M","Short","p:",0.171831708,"@",0.060864579,"e: ",0.073781959
"SD_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","M","Short","p:",0.254818455,"@",0.053464241,"e:",0.090889208
"SK_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","F","Short","p:",0.404017147,"@",0.051295513,"e:",0.113892205
"VG_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","F","Short","p:",0.213768279,"U",0.073607761,"e:",0.109098312
"VS_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","M","Short","p:",0.208510537,"U",0.06207953,"e:",0.090199928
"YP_chappe.TextGrid","Geminate","Stop","Voiceless","Bilabial","M","Short","p:",0.179598742,"U",0.05855083,"e: ",0.109084841
"AK_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d",0.073607761,"U",0.05494054,"e:",0.096553556
"DS_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d",0.081345232,"U",0.056651242,"e:",0.110866036
"MS_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d",0.066463105,"U",0.069207283,"e:",0.083045533
"NS_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d",0.070069095,"U",0.054456087,"e:",0.111350674
"PS_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d",0.066922298,"U",0.047966444,"e: ",0.088541492
"RB_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d",0.028220445,"U",0.058493475,"e:",0.091924885
"RJ_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d",0.063985902,"U",0.070652569,"e:",0.091924885
"RL_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d",0.070664851,"U",0.070652569,"e:",0.127421821
"RR_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d",0.08713925,"U",0.077999019,"e:",0.049593667
"RS_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d",0.076540283,"U",0.042945242,"e: ",0.076702926
"RS1_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d",0.076540283,"U",0.041665787,"e: ",0.058846014
"SD_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d",0.096503291,"U",0.044968093,"e:",0.103459332
"SK_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d",0.075648312,"U",0.059050244,"e:",0.089452658
"SS2_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d",0.085225473,"U",0.039207081,"e:",0.115078727
"SS_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d",0.087304762,"U",0.048676863,"e:",0.113500882
"VG_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d",0.071314559,"U",0.038559587,"e:",0.099234229
"VS_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d",0.06382568,"U",0.040247242,"e: ",0.117456237
"YP_kute.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d",0.070885475,"U",0.047615098,"e:",0.11321812
"AK_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","F","Short","t_d:",0.197175146,"U",0.044980788,"e:",0.076704079
"DS_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d:",0.188453944,"U",0.033653182,"e:",0.117986452
"MS_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","F","Short","t_d:",0.315141346,"U",0.040246986,"e:",0.115527864
"PS_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","F","Short","t_d:",0.123376007,"U",0.047676131,"e: ",0.106225766
"RB_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d:",0.130207542,"U",0.05322383,"e:",0.096120187
"RJ_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","F","Short","t_d:",0.129805129,"U",0.048369155,"e:",0.096120187
"RL_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d:",0.29711073,"U",0.046901222,"e:",0.117985886
"RS_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d:",0.151077251,"U",0.032514631,"e:",0.06616236
"RS1_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d:",0.155931926,"U",0.044997447,"e: ",0.061196529
"SD_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d:",0.200307302,"U",0.033319666,"e: ",0.091199279
"SK_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","F","Short","t_d:",0.61709581,"U",0.036363455,"e:",0.122806769
"VS_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d:",0.197066467,"U",0.036830765,"e:",0.096870754
"YP_kutte.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d:",0.159100337,"@",0.057985668,"e:",0.126980484
"AK_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","F","Short","t`",0.08422198,"@",0.055312929,"i:",0.123438846
"MS_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","F","Short","t`",0.101303456,"@",0.031297441,"i:",0.119326769
"NS_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","F","Short","t`",0.07964081,"@",0.062462805,"i:",0.081596899
"PS_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","F","Short","t`",0.059878136,"@",0.052763106,"i:",0.091024488
"RB_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","M","Short","t`",0.052763105,"@",0.050238671,"i:",0.087581268
"RJ_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","F","Short","t`",0.079189768,"@",0.059023408,"i:",0.084298787
"RL_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","M","Short","t`",0.070860979,"@",0.066604374,"i:",0.11954347
"RR_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","F","Short","t`",0.072826928,"@",0.054070654,"i:",0.085732967
"RS_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","M","Short","t`",0.083737739,"@",0.054070654,"i:",0.071513683
"RS1_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","M","Short","t`",0.083737739,"@",0.050450539,"i:",0.071513683
"SK_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","F","Short","t`",0.090034808,"@",0.047104732,"i:",0.076840052
"SS2_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","F","Short","t`",0.114043034,"@",0.041793764,"i:",0.062806309
"SS_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","F","Short","t`",0.07074892,"@",0.044585039,"i:",0.068836788
"YP_fati.TextGrid","Singleton","Stop","Voiceless","Retroflex","M","Short","t`",0.065094155,"@",0.058838506,"i:",0.105220691
"AK_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","F","Short","t`:",0.15216855,"@",0.049362393,"i:",0.102798309
"DS_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","M","Short","t`:",0.15856267,"@",0.047868677,"i:",0.100846991
"MS_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","F","Short","t`:",0.154153364,"@",0.035076215,"i:",0.08416162
"NS_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","F","Short","t`:",0.142475787,"@",0.065367528,"i:",0.112987637
"PS_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","F","Short","t`:",0.136898376,"@",0.046253072,"i:",0.092946883
"RB_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","M","Short","t`:",0.113903317,"@",0.050472582,"i:",0.07260641
"RJ_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","F","Short","t`:",0.119362767,"@",0.057295,"i:",0.076093764
"RL_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","M","Short","t`:",0.126923697,"@",0.061567024,"i:",0.097976188
"RR_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","F","Short","t`:",0.153710992,"@",0.061567024,"i:",0.103590766
"RS_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","M","Short","t`:",0.132728908,"@",0.067103253,"i:",0.08035696
"RS1_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","M","Short","t`:",0.132728908,"@",0.04666169,"i:",0.08035696
"SD_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","M","Short","t`:",0.152788943,"@",0.050818998,"i:",0.092863089
"SK_fatti.TextGrid","Geminate","Stop","Voiceless","Retroflex","F","Short","t`:",0.272622313,"@",0.033670525,"i:",0.08004031
"AK_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h",0.087118953,"@",0.036376071,"i:",0.082733291
"DS_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h",0.126160778,"@",0.040144076,"i:",0.086742939
"MS_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h",0.107629914,"@",0.033544421,"i:",0.10096529
"NS_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h",0.131042335,"@",0.06168022,"A:",0.113864323
"PS_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h",0.088907986,"@",0.039033887,"A:",0.091561501
"RB_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h",0.051822364,"@",0.063166253,"A:",0.092727311
"RJ_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h",0.080087856,"@",0.055632832,"A:",0.080853799
"RL_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h",0.079770811,"@",0.047300211,"A:",0.119600052
"RR_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h",0.127387134,"@",0.059215612,"A:",0.102882635
"RS_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h",0.084381417,"@",0.053924615,"A:",0.087612889
"RS1_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h",0.084381417,"@",0.062804201,"A:",0.078151615
"SD_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h",0.120000337,"@",0.062804201,"A:",0.085234274
"SK_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h",0.151374378,"@",0.042088581,"A:",0.085234274
"SS2_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h",0.144031531,"@",0.04602191,"A:",0.082569039
"SS_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h",0.126531003,"@",0.034761264,"A:",0.055636406
"VG_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h",0.089339028,"@",0.063813591,"A:",0.052626905
"VS_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h",0.128549141,"@",0.071296162,"A:",0.052837123
"YP_katha.TextGrid","Singleton","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h",0.097070908,"@",0.063461999,"A:",0.102101745
"DS_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h:",0.184249058,"@",0.047437072,"A:",0.084259102
"MS_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h:",0.152593152,"@",0.052644637,"A:",0.101510403
"PS_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h:",0.165796687,"@",0.065207188,"A:",0.099078116
"RB_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h:",0.156506556,"@",0.04763243,"A:",0.096438871
"RJ_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h:",0.179683984,"@",0.048679326,"A:",0.091327113
"RL_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h:",0.1724127,"@",0.033815767,"A:",0.096020931
"RR_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h:",0.178429235,"@",0.049143645,"A:",0.076598351
"RS_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h:",0.17675932,"@",0.043693337,"A:",0.096938533
"RS1_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h:",0.17675932,"@",0.043693337,"A:",0.095857832
"SD_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h:",0.232421525,"@",0.06588677,"A:",0.095857832
"SK_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","F","Short","t_d_h:",0.727669604,"@",0.040088857,"A:",0.076598351
"VS_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h:",0.205216779,"@",0.034612947,"A:",0.062849824
"YP_kattha.TextGrid","Geminate","Stop","Voiceless","Dental/alveolar","M","Short","t_d_h:",0.172436825,"@",0.035673669,"A:",0.065401727
"AK_saka.TextGrid","Singleton","Stop","Voiceless","Velar","F","Short","k",0.082999095,"@",0.045966546,"A:",0.099594185
"DS_saka.TextGrid","Singleton","Stop","Voiceless","Velar","M","Short","k",0.079832433,"@",0.055432118,"A:",0.081379067
"MS_saka.TextGrid","Singleton","Stop","Voiceless","Velar","F","Short","k",0.074699608,"@",0.051555896,"A:",0.104549295
"NS_saka.TextGrid","Singleton","Stop","Voiceless","Velar","F","Short","k",0.071537008,"@",0.054760697,"A:",0.114918
"RB_saka.TextGrid","Singleton","Stop","Voiceless","Velar","M","Short","k",0.035037395,"@",0.055258074,"A:",0.104823813
"RJ_saka.TextGrid","Singleton","Stop","Voiceless","Velar","F","Short","k",0.070894219,"@",0.05205498,"A:",0.077971282
"RL_saka.TextGrid","Singleton","Stop","Voiceless","Velar","M","Short","k",0.061954928,"@",0.041357333,"A:",0.102834449
"RR_saka.TextGrid","Singleton","Stop","Voiceless","Velar","F","Short","k",0.064012585,"@",0.053160186,"A:",0.079136186
"RS_saka.TextGrid","Singleton","Stop","Voiceless","Velar","M","Short","k",0.071062478,"@",0.066267591,"A:",0.0733936
"RS1_saka.TextGrid","Singleton","Stop","Voiceless","Velar","M","Short","k",0.071062478,"@",0.051256034,"A:",0.084360146
"SD_saka.TextGrid","Singleton","Stop","Voiceless","Velar","M","Short","k",0.082993345,"@",0.050863099,"A:",0.085928502
"SK_saka.TextGrid","Singleton","Stop","Voiceless","Velar","F","Short","k",0.046994992,"@",0.050863099,"A:",0.085928502
"SS2_saka.TextGrid","Singleton","Stop","Voiceless","Velar","F","Short","k",0.090827886,"@",0.072159193,"A:",0.117426754
"SS_saka.TextGrid","Singleton","Stop","Voiceless","Velar","F","Short","k",0.068736897,"@",0.04886283,"A:",0.072795644
"VS_saka.TextGrid","Singleton","Stop","Voiceless","Velar","M","Short","k",0.093149901,"@",0.051090687,"A:",0.063863358
"YP_saka.TextGrid","Singleton","Stop","Voiceless","Velar","M","Short","k",0.065983106,"@",0.039051862,"A:",0.067162304
"AK_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","F","Short","k:",0.201309756,"@",0.039380446,"A:",0.116549955
"DS_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","M","Short","k:",0.174189197,"@",0.05179998,"A:",0.117704
"MS_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","F","Short","k:",0.136923826,"@",0.039745773,"A:",0.109633842
"NS_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","F","Short","k:",0.169887835,"@",0.053065367,"A:",0.091358116
"RB_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","M","Short","k:",0.135643631,"@",0.05894072,"A:",0.109867582
"RJ_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","F","Short","k:",0.126292848,"@",0.028484188,"A:",0.093634597
"RL_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","M","Short","k:",0.123301982,"@",0.043527435,"A:",0.100481172
"RR_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","F","Short","k:",0.136636945,"@",0.042886721,"A:",0.110322392
"RS1_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","M","Short","k:",0.136547834,"@",0.039039127,"A:",0.092461091
"SD_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","M","Short","k:",0.152384501,"@",0.046993736,"A:",0.088398365
"SK_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","F","Short","k:",0.129634718,"@",0.032654821,"A:",0.082928171
"SS2_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","F","Short","k:",0.128603978,"@",0.031685037,"A:",0.068961553
"SS_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","F","Short","k:",0.162946091,"@",0.032649124,"A:",0.091058109
"VG_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","F","Short","k:",0.131130854,"@",0.04834749,"A:",0.114825291
"YP_sakka.TextGrid","Geminate","Stop","Voiceless","Velar","M","Short","k:",0.12967303,"@",0.047719675,"A:",0.101663656

summary(dpun)
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: C2 ~ Consonant + Place + (1 | Filename)
#>    Data: within(pun_data, Filename <- substr(Filename, 1, 2))
#> 
#> REML criterion at convergence: -382.5
#> 
#> Scaled residuals: 
#>     Min      1Q  Median      3Q     Max 
#> -2.1938 -0.3797 -0.1103  0.2766  7.2708 
#> 
#> Random effects:
#>  Groups   Name        Variance Std.Dev.
#>  Filename (Intercept) 0.001161 0.03408 
#>  Residual             0.003385 0.05818 
#> Number of obs: 153, groups:  Filename, 16
#> 
#> Fixed effects:
#>                      Estimate Std. Error t value
#> (Intercept)           0.19071    0.01429  13.347
#> ConsonantSingleton   -0.10551    0.00963 -10.956
#> PlaceDental/alveolar  0.01575    0.01256   1.254
#> PlaceRetroflex       -0.02201    0.01525  -1.443
#> PlaceVelar           -0.03163    0.01468  -2.155
#> 
#> Correlation of Fixed Effects:
#>             (Intr) CnsnnS PlcDn/ PlcRtr
#> CnsnntSnglt -0.371                     
#> PlcDntl/lvl -0.561 -0.028              
#> PlaceRtrflx -0.479  0.021  0.538       
#> PlaceVelar  -0.504  0.033  0.557  0.467