An embedded and charset-unspecified text was scrubbed... Name: not available URL: <https://stat.ethz.ch/pipermail/r-sig-mixed-models/attachments/20101011/3d36c917/attachment.pl>
why does my inter-annual SD = 0?
5 messages · Scott Bennett, Douglas Bates, Ben Bolker +1 more
Can you tell us how many levels there are of region, site, zone and year? If the data are collected in only a few years it is difficult to estimate a variance component and the ML and REML criteria drive the estimates to zero. You may be overmodeling the data but it is difficult to say without access to the data or at least a feeling for how many observations and in what configuration they are.
On Mon, Oct 11, 2010 at 10:32 AM, Scott Bennett <sbennett at ceab.csic.es> wrote:
Hi, I am applying a mixed model to calculate the variance components of different factors in our seagrass data. The model i was using looks something like: POMI14_vc <- lmer(POMI_14 ~ Depth + surveyor + (1|region/site/zone) + (1|year), ?data = P_oceanica) When I apply this model, however, year comes out with SD = 0. Year, in this data set signifies inter-annual variation (in the health status of seagrass meadows), of which there is a considerable amount. That makes me believe that there is is a feature of the model which is 'absorbing' the inter-annual variation. Can you suggest why this may be occuring? What modifiations could i use to fix this? kind regards Scott Bennett ? ? ? ?[[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
An embedded and charset-unspecified text was scrubbed... Name: not available URL: <https://stat.ethz.ch/pipermail/r-sig-mixed-models/attachments/20101011/454b74d8/attachment.pl>
Hi all, thanks for the quick replies! I have attached the data, if it helps for you to look at it directly. The end product that I would like to achieve is an estimate of the variance associated with each factor (depth, surveyor, year, water body, site and zone), to then model the probability of misclassifying the health status of a water body, based on the variability associated with each of the respective factors (An uncertainty analysis of our seagrass index). The index we are using POMI_14 (Posidonia oceanica multivariate index, Romero et al. 2008) is comprised of 14 metrics relates to the health and status of /P. oceanica./ There are only 4 years of data. The 2005, 2006 and 2008 data is from annual sampling of 30 seagrass meadows (sites) sampled at a single depth along the Catalan coast, Spain. The 2002 data is of a subset of those sites, but includes replication between 2 discrete depths (5m and 15m) and among three discrete zones nested within each site. The sites are nested within 'water-bodies'. A water body represents an area of coastal water (15 - 50 km in length) which has been classified based on its exposure to water quality pressures. The surveyor factor, is only from the 2008 series, where we calculated POMI based on two separate surveyors. Needless to say the design is unbalanced. In short the data looks like this: 'data.frame': 231 obs. of 8 variables: $ year : Factor w/ 4 levels "2002","2005",..: 4 4 4 4 4 4 4 4 4 4 ... $ WB : Factor w/ 17 levels "1","2","3","4",..: 1 2 3 3 3 4 5 6 7 8 ... $ Site : Factor w/ 30 levels "Balis ","Cadaques ",..: 22 19 27 7 9 2 16 21 20 15 ... $ Zone : Factor w/ 3 levels "a","b","c":..: 1 1 1 1 1 1 1 1 1 1 ... $ Depth : Factor w/ 2 levels "p","s": 1 1 1 1 1 1 1 1 1 1 ... $ surveyor: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ... $ POMI_14 : num 0.781 0.633 0.717 0.936 0.86 ... $ POMI_9 : num 0.803 0.67 0.745 0.942 0.873 ... I hope this makes things clearer. Any help will be greatly appreciated. Kind regards Scott Bennett
On 10-10-11 11:32 AM, Scott Bennett wrote:
Hi, I am applying a mixed model to calculate the variance components of different factors in our seagrass data. The model i was using looks something like: POMI14_vc <- lmer(POMI_14 ~ Depth + surveyor + (1|region/site/zone) + (1|year), data = P_oceanica) When I apply this model, however, year comes out with SD = 0. Year, in this data set signifies inter-annual variation (in the health status of seagrass meadows), of which there is a considerable amount. That makes me believe that there is is a feature of the model which is 'absorbing' the inter-annual variation. Can you suggest why this may be occuring? What modifiations could i use to fix this? kind regards Scott Bennett
Hard to say for sure without seeing the data.
How many years do you have? Are Depth and surveyor well
distributed across years?
What happens if you treat year as a fixed effect and calculate the
among-year variance on
the basis of the fixed effect estimates?
[[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
-------------- next part -------------- year,WB,Site,Zone,Depth,surveyor,POMI_14,POMI_9 2008,1,Rovellad ,a,p,1,0.781107622,0.80279966 2008,2,Port_Selva ,a,p,1,0.633441988,0.669767557 2008,3,Tamariua ,a,p,1,0.716925282,0.744977731 2008,3,Culip ,a,p,1,0.935657559,0.942033837 2008,3,Jugadora ,a,p,1,0.859538869,0.873458441 2008,4,Cadaques ,a,p,1,0.684976423,0.716194975 2008,5,Montjoi ,a,p,1,0.749745797,0.774545763 2008,6,Roses_2 ,a,p,1,0.705540388,0.73472107 2008,7,Roses_1 ,a,p,1,0.406455662,0.465275371 2008,10,Montgo ,a,p,1,0.468556701,0.521222254 2008,10,Medas ,a,p,1,0.675423785,0.707588995 2008,14,Sa_Tuna ,a,p,1,0.560703188,0.604237106 2008,14,Llafranc ,a,p,1,0.575450184,0.617522688 2008,14,Palamos ,a,p,1,0.567427391,0.610294947 2008,14,St_Feliu ,a,p,1,0.630364509,0.666995053 2008,14,Tossa ,a,p,1,0.696983928,0.727012548 2008,14,Canyelle ,a,p,1,0.666440392,0.699495849 2008,14,Fenals ,a,p,1,0.884870258,0.896279512 2008,16,Balis ,a,p,1,0.56050751,0.60406082 2008,16,Mataro ,a,p,1,0.566466791,0.609429541 2008,23,Sitges ,a,p,1,0.414958755,0.472935815 2008,24,Vilanova ,a,p,1,0.346702757,0.411443925 2008,25,Comarug ,a,p,1,0.303724782,0.372725028 2008,25,Torredem ,a,p,1,0.290578787,0.36088179 2008,29,Salou ,a,p,1,0.268360454,0.340865274 2008,30,Montroig ,a,p,1,0.399909348,0.459377791 2008,31,L_Hospit ,a,p,1,0.517707424,0.565502184 2008,31,Calafat ,a,p,1,0.620118104,0.657764058 2008,32,L_Ametll ,a,p,1,0.519628625,0.567232995 2008,32,Cap_Roig ,a,p,1,0.301643481,0.370849983 2006,1,Rovellad ,a,p,1,0.830061339,0.631092868 2006,3,Tamariua ,a,p,1,0.644537158,0.59184892 2006,3,Culip ,a,p,1,0.696764496,0.647210919 2006,3,Jugadora ,a,p,1,0.699754821,0.69911104 2006,5,Montjoi ,a,p,1,0.531083947,0.565331223 2006,7,Roses_1 ,a,p,1,0.338586935,0.416484533 2006,10,Montgo ,a,p,1,0.618630194,0.518771081 2006,10,Medas ,a,p,1,0.599916547,0.744704099 2006,14,Sa_Tuna ,a,p,1,0.438411429,0.579487076 2006,14,Llafranc ,a,p,1,0.297618103,0.422441203 2006,14,Palamos ,a,p,1,0.513128216,0.494888336 2006,14,St_Feliu ,a,p,1,0.578708665,0.717093178 2006,14,Tossa ,a,p,1,0.556797987,0.661815274 2006,14,Canyelle ,a,p,1,0.504377726,0.600790934 2006,14,Fenals ,a,p,1,0.65746308,0.797038708 2006,16,Balis ,a,p,1,0.398365877,0.540523441 2006,16,Mataro ,a,p,1,0.293938763,0.546353971 2006,23,Sitges ,a,p,1,0.419821804,0.295669234 2006,24,Vilanova ,a,p,1,0.473647656,0.447921739 2006,25,Comarug ,a,p,1,0.392950219,0.288899653 2006,25,Torredem ,a,p,1,0.488461479,0.284022191 2006,29,Salou ,a,p,1,0.47425399,0.284442662 2006,30,Montroig ,a,p,1,0.448925798,0.459134295 2006,31,L_Hospit ,a,p,1,0.672318243,0.579893531 2006,31,Calafat ,a,p,1,0.613972453,0.549185141 2006,32,L_Ametll ,a,p,1,0.740379146,0.63193381 2006,32,Cap_Roig ,a,p,1,0.377695427,0.425496625 2005,1,Rovellad ,a,p,1,0.613089417,0.6010795 2005,3,Tamariua ,a,p,1,0.627720981,0.664700276 2005,3,Culip ,a,p,1,0.654396909,0.707133319 2005,3,Jugadora ,a,p,1,0.757197896,0.812208357 2005,5,Montjoi ,a,p,1,0.675401462,0.724559462 2005,7,Roses_1 ,a,p,1,0.525139248,0.542237478 2005,10,Montgo ,a,p,1,0.5626171,0.557706059 2005,10,Medas ,a,p,1,0.716986675,0.775244974 2005,14,Sa_Tuna ,a,p,1,0.662041572,0.690167778 2005,14,Llafranc ,a,p,1,0.577818725,0.66366392 2005,14,Palamos ,a,p,1,0.506707278,0.570545365 2005,14,St_Feliu ,a,p,1,0.683543101,0.779121715 2005,14,Tossa ,a,p,1,0.651795093,0.688517285 2005,14,Canyelle ,a,p,1,0.525928563,0.574115038 2005,14,Fenals ,a,p,1,0.693760347,0.759027913 2005,16,Balis ,a,p,1,0.536876657,0.578529149 2005,16,Mataro ,a,p,1,0.521017269,0.545864726 2005,23,Sitges ,a,p,1,0.288373944,0.208780166 2005,24,Vilanova ,a,p,1,0.379861414,0.380757776 2005,25,Comarug ,a,p,1,0.254243373,0.163065328 2005,25,Torredem ,a,p,1,0.245195483,0.203003438 2005,29,Salou ,a,p,1,0.245663225,0.143969499 2005,30,Montroig ,a,p,1,0.414678981,0.400678852 2005,31,L_Hospit ,a,p,1,0.518064646,0.487310582 2005,31,Calafat ,a,p,1,0.537417484,0.548781878 2005,32,L_Ametll ,a,p,1,0.546363055,0.531374927 2005,32,Cap_Roig ,a,p,1,0.396583201,0.327903593 2008,1,Rovellad ,a,p,2,0.835797869,0.644942527 2008,2,Port_Selva ,a,p,2,0.791972534,0.653946258 2008,3,Tamariua ,a,p,2,0.662135843,0.685428269 2008,3,Culip ,a,p,2,0.720143502,0.734483079 2008,3,Jugadora ,a,p,2,0.495271707,0.441691062 2008,4,Cadaques ,a,p,2,0.535878572,0.517213736 2008,5,Montjoi ,a,p,2,0.782317124,0.548866508 2008,6,Roses_2 ,a,p,2,0.567130818,0.54638272 2008,7,Roses_1 ,a,p,2,0.153828129,0.35502239 2008,10,Montgo ,a,p,2,0.633124494,0.436583773 2008,10,Medas ,a,p,2,0.562227915,0.713541642 2008,14,Sa_Tuna ,a,p,2,0.657488613,0.546460338 2008,14,Llafranc ,a,p,2,0.520237409,0.547158904 2008,14,Palamos ,a,p,2,0.525982528,0.44949947 2008,14,St_Feliu ,a,p,2,0.596548236,0.60346327 2008,14,Tossa ,a,p,2,0.675325554,0.635845654 2008,14,Canyelle ,a,p,2,0.786167256,0.640580374 2008,14,Fenals ,a,p,2,0.780692849,0.816354936 2008,16,Balis ,a,p,2,0.393468792,0.767377744 2008,16,Mataro ,a,p,2,0.458078826,0.685723218 2008,23,Sitges ,a,p,2,0.583298366,0.354789535 2008,24,Vilanova ,a,p,2,0.425879086,0.458021967 2008,25,Comarug ,a,p,2,0.768751423,0.32299705 2008,25,Torredem ,a,p,2,0.614370138,0.29794184 2008,29,Salou ,a,p,2,0.509138199,0.209037759 2008,30,Montroig ,a,p,2,0.403996498,0.293455498 2008,31,L_Hospit ,a,p,2,0.803718446,0.620585882 2008,31,Calafat ,a,p,2,0.680318695,0.527645645 2008,32,L_Ametll ,a,p,2,0.591013671,0.439766127 2008,32,Cap_Roig ,a,p,2,0.490880149,0.328476907 2002,3,Jugadora ,a,s,1,0.852607907,0.787071029 2002,3,Jugadora ,b,s,1,0.798730237,0.736424513 2002,3,Jugadora ,c,s,1,0.757097491,0.675645884 2002,3,Jugadora ,a,p,1,0.645718219,0.612211263 2002,3,Jugadora ,b,p,1,0.659123934,0.626193861 2002,3,Jugadora ,c,p,1,0.593213678,0.562871663 2002,5,Montjoi ,a,s,1,0.694542203,0.675182139 2002,5,Montjoi ,b,s,1,0.754818378,0.611438355 2002,5,Montjoi ,c,s,1,0.736443912,0.70729293 2002,5,Montjoi ,a,p,1,0.529950591,0.500069421 2002,5,Montjoi ,b,p,1,0.578123401,0.485370127 2002,5,Montjoi ,c,p,1,0.555587076,0.492537087 2002,14,Fenals ,a,s,1,0.691045676,0.63625571 2002,14,Fenals ,b,s,1,0.751378474,0.615541791 2002,14,Fenals ,c,s,1,0.777184831,0.645390071 2002,14,Fenals ,a,p,1,0.554638625,0.54375977 2002,14,Fenals ,b,p,1,0.468867526,0.438602201 2002,14,Fenals ,c,p,1,0.533701927,0.552093118 2002,30,Montroig ,a,s,1,0.634379276,0.648467648 2002,30,Montroig ,b,s,1,0.690196317,0.695347997 2002,30,Montroig ,c,s,1,0.744229703,0.736817994 2002,30,Montroig ,a,p,1,0.60022089,0.457278455 2002,30,Montroig ,b,p,1,0.440371532,0.45543753 2002,30,Montroig ,c,p,1,0.510061436,0.381589738 2002,23,Sitges ,a,p,1,0.394930827,0.344377759 2002,23,Sitges ,b,p,1,0.436082269,0.332727936 2002,23,Sitges ,c,p,1,0.269961811,0.282910539 2002,25,Comarug ,a,p,1,0.394520304,0.351614983 2002,25,Comarug ,b,p,1,0.375438039,0.260383801 2002,25,Comarug ,c,p,1,0.303681362,0.240555212 2002,16,Mataro ,a,p,1,0.530247867,0.429819162 2002,16,Mataro ,b,p,1,0.495197654,0.475842286 2002,16,Mataro ,c,p,1,0.496528316,0.467607309 2002,10,Montgo ,a,p,1,0.641315708,0.541384837 2002,10,Montgo ,b,p,1,0.58235604,0.540696246 2002,10,Montgo ,c,p,1,0.578491456,0.477121097 2008,1,Rovellad ,a,p,1,0.624182987,0.671245913 2008,3,Tamariua ,a,p,1,0.704355158,0.72756147 2008,3,Culip ,a,p,1,0.80911293,0.782600491 2008,3,Jugadora ,a,p,1,0.610176765,0.614042332 2008,5,Montjoi ,a,p,1,0.506873409,0.518672196 2008,7,Roses_1 ,a,p,1,0.533132584,0.354350654 2008,10,Montgo ,a,p,1,0.401936325,0.459082049 2008,10,Medas ,a,p,1,0.717146046,0.711780534 2008,14,Sa_Tuna ,a,p,1,0.555387136,0.586032558 2008,14,Llafranc ,a,p,1,0.606630382,0.562351903 2008,14,Palamos ,a,p,1,0.452283014,0.429277725 2008,14,St_Feliu ,a,p,1,0.636435941,0.621054026 2008,14,Tossa ,a,p,1,0.667536527,0.673521476 2008,14,Canyelle ,a,p,1,0.573776528,0.637519481 2008,14,Fenals ,a,p,1,0.853203639,0.858952101 2008,16,Balis ,a,p,1,0.913492156,0.832514871 2008,16,Mataro ,a,p,1,0.826884694,0.747560523 2008,23,Sitges ,a,p,1,0.245477066,0.320827728 2008,24,Vilanova ,a,p,1,0.45419567,0.463707177 2008,25,Comarug ,a,p,1,0.127709248,0.311373966 2008,25,Torredem ,a,p,1,0.157156183,0.272041879 2008,29,Salou ,a,p,1,0.128287029,0.146090398 2008,30,Montroig ,a,p,1,0.352565888,0.27387343 2008,31,L_Hospit ,a,p,1,0.533232202,0.463263165 2008,31,Calafat ,a,p,1,0.516596078,0.534305128 2008,32,L_Ametll ,a,p,1,0.48930088,0.434772377 2008,32,Cap_Roig ,a,p,1,0.370736122,0.344452881 2006,1,Rovellad ,a,p,1,0.830061339,0.631092868 2006,3,Tamariua ,a,p,1,0.644537158,0.59184892 2006,3,Culip ,a,p,1,0.696764496,0.647210919 2006,3,Jugadora ,a,p,1,0.699754821,0.69911104 2006,5,Montjoi ,a,p,1,0.531083947,0.565331223 2006,7,Roses_1 ,a,p,1,0.338586935,0.416484533 2006,10,Montgo ,a,p,1,0.618630194,0.518771081 2006,10,Medas ,a,p,1,0.599916547,0.744704099 2006,14,Sa_Tuna ,a,p,1,0.438411429,0.579487076 2006,14,Llafranc ,a,p,1,0.297618103,0.422441203 2006,14,Palamos ,a,p,1,0.513128216,0.494888336 2006,14,St_Feliu ,a,p,1,0.578708665,0.717093178 2006,14,Tossa ,a,p,1,0.556797987,0.661815274 2006,14,Canyelle ,a,p,1,0.504377726,0.600790934 2006,14,Fenals ,a,p,1,0.65746308,0.797038708 2006,16,Balis ,a,p,1,0.398365877,0.540523441 2006,16,Mataro ,a,p,1,0.293938763,0.546353971 2006,23,Sitges ,a,p,1,0.419821804,0.295669234 2006,24,Vilanova ,a,p,1,0.473647656,0.447921739 2006,25,Comarug ,a,p,1,0.392950219,0.288899653 2006,25,Torredem ,a,p,1,0.488461479,0.284022191 2006,29,Salou ,a,p,1,0.47425399,0.284442662 2006,30,Montroig ,a,p,1,0.448925798,0.459134295 2006,31,L_Hospit ,a,p,1,0.672318243,0.579893531 2006,31,Calafat ,a,p,1,0.613972453,0.549185141 2006,32,L_Ametll ,a,p,1,0.740379146,0.63193381 2006,32,Cap_Roig ,a,p,1,0.377695427,0.425496625 2005,1,Rovellad ,a,p,1,0.613089417,0.6010795 2005,3,Tamariua ,a,p,1,0.627720981,0.664700276 2005,3,Culip ,a,p,1,0.654396909,0.707133319 2005,3,Jugadora ,a,p,1,0.757197896,0.812208357 2005,5,Montjoi ,a,p,1,0.675401462,0.724559462 2005,7,Roses_1 ,a,p,1,0.525139248,0.542237478 2005,10,Montgo ,a,p,1,0.5626171,0.557706059 2005,10,Medas ,a,p,1,0.716986675,0.775244974 2005,14,Sa_Tuna ,a,p,1,0.662041572,0.690167778 2005,14,Llafranc ,a,p,1,0.577818725,0.66366392 2005,14,Palamos ,a,p,1,0.506707278,0.570545365 2005,14,St_Feliu ,a,p,1,0.683543101,0.779121715 2005,14,Tossa ,a,p,1,0.651795093,0.688517285 2005,14,Canyelle ,a,p,1,0.525928563,0.574115038 2005,14,Fenals ,a,p,1,0.693760347,0.759027913 2005,16,Balis ,a,p,1,0.536876657,0.578529149 2005,16,Mataro ,a,p,1,0.521017269,0.545864726 2005,23,Sitges ,a,p,1,0.288373944,0.208780166 2005,24,Vilanova ,a,p,1,0.379861414,0.380757776 2005,25,Comarug ,a,p,1,0.254243373,0.163065328 2005,25,Torredem ,a,p,1,0.245195483,0.203003438 2005,29,Salou ,a,p,1,0.245663225,0.143969499 2005,30,Montroig ,a,p,1,0.414678981,0.400678852 2005,31,L_Hospit ,a,p,1,0.518064646,0.487310582 2005,31,Calafat ,a,p,1,0.537417484,0.548781878 2005,32,L_Ametll ,a,p,1,0.546363055,0.531374927 2005,32,Cap_Roig ,a,p,1,0.396583201,0.327903593
Dear Scott, Thanks for sharing your data. That makes things a lot easier. IMHO you have several problems with your model. First you have only 4 years. Estimating a variance based on only 4 levels will give you very unreliable estimates. Therefore it is better to treat them as a fixed effect. Another possible problem might be the structure of your nested random effects. I noticed that many levels of WB have only one level of site, and many levels of site have only one levels of zone. In such a case there is competition between the nested effects. I'm not sure how lmer handles that. If it were just a few cases I guess it will not be a big issue. But your data has quite a lot of this things. A possible solution is to use (1|WB:site:zone) as random effect. HTH, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & Quality Assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx at inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey
-----Oorspronkelijk bericht----- Van: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Scott Bennett Verzonden: maandag 11 oktober 2010 19:39 Aan: r-sig-mixed-models at r-project.org Onderwerp: Re: [R-sig-ME] why does my inter-annual SD = 0? Hi all, thanks for the quick replies! I have attached the data, if it helps for you to look at it directly. The end product that I would like to achieve is an estimate of the variance associated with each factor (depth, surveyor, year, water body, site and zone), to then model the probability of misclassifying the health status of a water body, based on the variability associated with each of the respective factors (An uncertainty analysis of our seagrass index). The index we are using POMI_14 (Posidonia oceanica multivariate index, Romero et al. 2008) is comprised of 14 metrics relates to the health and status of /P. oceanica./ There are only 4 years of data. The 2005, 2006 and 2008 data is from annual sampling of 30 seagrass meadows (sites) sampled at a single depth along the Catalan coast, Spain. The 2002 data is of a subset of those sites, but includes replication between 2 discrete depths (5m and 15m) and among three discrete zones nested within each site. The sites are nested within 'water-bodies'. A water body represents an area of coastal water (15 - 50 km in length) which has been classified based on its exposure to water quality pressures. The surveyor factor, is only from the 2008 series, where we calculated POMI based on two separate surveyors. Needless to say the design is unbalanced. In short the data looks like this: 'data.frame': 231 obs. of 8 variables: $ year : Factor w/ 4 levels "2002","2005",..: 4 4 4 4 4 4 4 4 4 4 ... $ WB : Factor w/ 17 levels "1","2","3","4",..: 1 2 3 3 3 4 5 6 7 8 ... $ Site : Factor w/ 30 levels "Balis ","Cadaques ",..: 22 19 27 7 9 2 16 21 20 15 ... $ Zone : Factor w/ 3 levels "a","b","c":..: 1 1 1 1 1 1 1 1 1 1 ... $ Depth : Factor w/ 2 levels "p","s": 1 1 1 1 1 1 1 1 1 1 ... $ surveyor: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ... $ POMI_14 : num 0.781 0.633 0.717 0.936 0.86 ... $ POMI_9 : num 0.803 0.67 0.745 0.942 0.873 ... I hope this makes things clearer. Any help will be greatly appreciated. Kind regards Scott Bennett
On 10-10-11 11:32 AM, Scott Bennett wrote:
Hi, I am applying a mixed model to calculate the variance
components of
different factors in our seagrass data. The model i was
using looks
something like: POMI14_vc <- lmer(POMI_14 ~ Depth + surveyor + (1|region/site/zone) + (1|year), data = P_oceanica) When I apply this model, however, year comes out with SD =
0. Year,
in this data set signifies inter-annual variation (in the health status of seagrass meadows), of which there is a
considerable amount.
That makes me believe that there is is a feature of the
model which
is 'absorbing' the inter-annual variation. Can you suggest why this may be occuring? What
modifiations could i
use to fix this? kind regards Scott Bennett
Hard to say for sure without seeing the data. How many years do you have? Are Depth and surveyor well distributed across years? What happens if you treat year as a fixed effect and
calculate the
among-year variance on the basis of the fixed effect estimates?
[[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models