[R-sig-ME] Factors in nlme: singularity in backsolve error
Alexander Shenkin
alexander.shenkin at ouce.ox.ac.uk
Fri Jul 15 17:16:11 CEST 2016
Hello all,
I am parameterizing exponential fits for some metabolic scaling models.
I have done this in lmer already, without problem, using logged
dependent and independent variables. However, I would now like to
incorporate other parameters that aren't necessarily exponentially
related to the dependent variable. Hence, I've turned to nlme, but I
don't have much experience with it. Apologies in advance for newbie
mistakes.
With the code below, I am getting the following error. I'm guessing
that it has something to do with the 'site' factor being misspecified:
Error in nlme.formula(dep ~ scaling_fun(alpha, beta, ind, site), data =
scale_df, :
Singularity in backsolve at level 0, block 1
When I fit a simpler function that does not involve 'site', the model
seems to work correctly.
Any thoughts would be greatly appreciated! I've added the dput object
at the bottom of the email to avoid distraction...
Thanks,
Allie
> head(scale_df)
dep ind spp site
2 0.28069471 -0.0322841 157 A
3 -0.69719050 -1.2568901 183 A
4 0.29252012 0.1592420 246 A
5 0.72030740 -0.3282789 154 A
6 -0.08601891 0.3623756 110 A
7 0.30793594 0.2230840 154 A
scaling_fun <- function(alpha, beta, ind, site) {
return(beta + ind^alpha + site*(ind^alpha))
}
# results in singularity in backsolve error
nlme(dep ~ trait_scaling_fun(alpha, beta, ind, site),
data = scale_df,
fixed = list(alpha + beta + site ~ 1), random = alpha ~ 1|spp,
start = list(fixed = c(0.7, 0, 1)))
##############################
# simpler function converges #
##############################
scaling_fun <- function(alpha, beta, ind) {
return(beta + ind^alpha)
}
nlme(dep ~ scaling_fun(alpha, beta, ind),
data = scale_df,
fixed = list(alpha + beta ~ 1), random = alpha ~ 1|spp,
start = list(fixed = c(0.7, 0)))
# dput for data
scale_df = structure(list(dep = structure(c(0.280694709349609,
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13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 8L, 8L,
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10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
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10L, 10L, 10L, 10L, 10L, 10L, 10L, 9L, 9L, 9L, 9L, 9L, 9L,
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9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
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202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 210L, 211L, 212L,
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300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L,
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427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 435L, 436L, 437L,
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555L, 556L, 557L, 558L, 559L, 560L, 561L, 562L, 563L, 564L, 565L,
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600L, 601L, 602L, 603L, 604L, 605L, 606L, 607L, 608L, 609L, 610L,
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700L, 701L, 702L, 703L, 704L, 705L, 706L, 707L, 708L, 709L, 710L,
711L, 712L, 713L, 714L, 715L, 716L, 717L, 718L, 719L, 720L, 721L,
722L, 723L, 724L, 725L, 726L, 727L, 728L, 729L, 730L, 731L, 732L,
733L, 734L, 735L, 736L, 737L, 738L, 739L, 740L, 741L, 742L, 743L,
744L, 745L, 746L, 747L, 748L, 749L, 750L, 751L, 752L, 753L, 754L,
755L, 756L, 757L, 758L, 760L, 761L, 762L, 763L, 764L, 765L, 766L,
767L, 768L, 777L, 778L, 779L, 780L, 781L, 782L, 783L, 784L, 785L,
786L, 787L, 788L, 789L, 790L, 791L, 792L, 793L, 794L, 795L, 796L,
797L, 798L, 799L, 800L, 801L, 802L, 803L, 804L, 805L, 806L, 807L,
808L, 809L, 810L, 811L, 812L, 813L, 814L, 815L, 816L, 817L, 818L,
819L, 820L, 821L, 822L, 823L, 824L, 825L, 826L, 827L, 828L, 829L,
830L, 831L, 832L, 833L, 834L, 835L, 836L, 837L, 838L, 839L, 840L,
841L, 842L, 847L, 848L, 849L, 850L, 851L, 852L, 853L, 854L, 855L,
856L, 857L, 858L, 859L, 860L, 861L, 862L, 863L, 864L, 865L, 866L,
867L, 868L, 869L, 870L, 871L, 872L, 873L, 874L, 875L, 876L, 877L,
878L, 879L, 880L, 882L, 883L, 884L, 885L, 886L, 887L, 888L, 889L,
890L, 891L, 892L, 893L, 894L, 896L, 897L, 898L, 899L, 900L, 902L,
903L, 904L, 905L, 906L, 907L, 908L, 909L, 910L, 911L, 912L, 913L,
914L, 915L, 916L, 917L, 918L, 919L, 920L, 921L, 922L, 923L, 924L,
925L, 926L, 927L, 928L, 929L, 930L, 931L, 932L, 933L, 934L, 935L,
936L, 937L, 938L, 939L, 940L, 941L, 942L, 943L, 944L, 945L, 946L,
947L, 948L, 949L, 950L, 951L, 952L, 953L, 954L, 955L, 956L, 957L,
958L, 959L, 960L, 961L, 962L, 963L, 964L, 965L, 966L, 967L, 968L,
969L, 970L, 971L, 972L, 973L, 974L, 975L, 976L, 977L, 978L, 979L,
980L, 981L, 982L, 983L, 984L, 985L, 986L, 987L, 988L, 989L, 990L,
991L, 992L, 993L, 994L, 995L, 996L, 997L, 998L, 999L, 1000L,
1001L, 1002L, 1003L, 1004L, 1005L, 1006L, 1007L, 1008L, 1009L,
1010L, 1011L, 1012L, 1013L, 1014L, 1015L, 1016L, 1017L, 1018L,
1019L, 1024L, 1025L, 1026L, 1027L, 1028L, 1029L, 1030L, 1031L,
1032L, 1033L, 1034L, 1035L, 1036L, 1037L, 1038L, 1039L, 1040L,
1041L, 1042L, 1043L, 1044L, 1045L, 1046L, 1047L, 1048L, 1049L,
1050L, 1051L, 1052L, 1053L, 1054L, 1055L, 1056L, 1057L, 1058L,
1059L, 1060L, 1061L, 1062L, 1063L, 1064L, 1065L, 1066L, 1067L,
1069L, 1070L, 1071L, 1073L, 1074L, 1075L, 1076L, 1077L, 1078L,
1080L, 1081L, 1082L), class = "data.frame")
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