# Return the -log likelihood of a set of individuals under a model of tagloss. This function must be used within optim(). model_before is applied to the par parameter. model_after is applied after par is separated in p1, p2, pL1, pL2, pR1 and pR2 parameters. progressbar is set to FALSE if mc.cores is different from 1. If days.maximum is not indicated, it is estimated using Tagloss_daymax(). ```r Tagloss_L( individuals, par, days.maximum = NULL, fixed.parameters = NULL, model_before = NULL, model_after = NULL, names.par = NULL, groups = NULL, mc.cores = detectCores(all.tests = FALSE, logical = TRUE), progressbar = FALSE ) ``` ## Arguments - `individuals`: Set of individuals - `par`: Set of parameters - `days.maximum`: Maximum number of days. Can be determined using Tagloss_daymax() - `fixed.parameters`: Set of fixed parameters - `model_before`: Transformation of parameters before to use Tagloss_model() - `model_after`: Transformation of parameters after to use Tagloss_model() - `names.par`: Name of parameters. Normally unused. - `groups`: Number of groups for parallel computing - `mc.cores`: Number of cores to use for parallel computing - `progressbar`: Is shown a progressbar? ## Returns Return the -log likelihood of a set of individuals ## Details Tagloss_L returns the -log likelihood of a set of individuals under a model of tagloss. ## Examples ```r ## Not run: library(phenology) # Example with 21 format of data data_f_21 <- Tagloss_format(outLR, model="21") par <- structure(c(49.5658922243074, 808.136085362158, 106.283783786853, 5.22150592456511, 8.00608716525864, 8.32718202233396, 150.612916258503, 715.865805125223, 2242.06574225966, 119.212383120678, 10.1860735529433, 7.14231725937626), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1")) pfixed <- NULL # All the data are analyzed; the N20 are very long to compute Tagloss_L(individuals=data_f_21, par=par, days.maximum=Tagloss_daymax(data_f_21), fixed.parameters=pfixed, mc.cores=1, progressbar=TRUE) # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) Tagloss_L(individuals=data_f_21_fast, par=par, days.maximum=Tagloss_daymax(data_f_21_fast), fixed.par=pfixed, mc.cores=1, progressbar=TRUE) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par) # Here it is the result of the previous function o <- structure(list(par = structure(c(49.5658922243074, 808.136085362158, 106.283783786853, 5.22150592456511, 8.00608716525864, 8.32718202233396, 150.612916258503, 715.865805125223, 2242.06574225966, 119.212383120678, 10.1860735529433, 7.14231725937626), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1")), value = 5841.93084262461, counts = structure(c(1093L, NA), .Names = c("function", "gradient")), convergence = 0L, message = NULL, hessian = structure(c(0.0469808583147824, 0.000133240973809734, 6.68478605803102e-05, -2.53581288234273, -1.25931342154217, -0.124650568977813, -2.46700437855907e-05, -1.11413100967184e-05, -3.18323145620525e-06, 0, -0.0182945996130002, -0.00510601694259094, 0.000133240973809734, 1.45519152283669e-05, 7.50333128962666e-06, -0.00452587300969753, -0.0191316757991444, -0.0255117811320815, -1.13686837721616e-06, -1.36424205265939e-06, -2.27373675443232e-07, 0, 0.000335830918629654, -0.000448608261649497, 6.68478605803102e-05, 7.50333128962666e-06, 4.32009983342141e-06, -0.00226373231271282, -0.00954059942159802, -0.0127809016703395, -4.54747350886464e-07, -4.54747350886464e-07, -2.27373675443232e-07, 0, 0.000176896719494835, -0.000224190443987027, -2.53581288234273, -0.00452587300969753, -0.00226373231271282, 223.422489398217, 41.4073996353181, 3.77875949197914, 0.000986460690910462, 0.000398813426727429, 0.000117665877041873, 0, 0.727547330825473, 0.194675862985605, -1.25931342154217, -0.0191316757991444, -0.00954059942159802, 41.4073996353181, 189.534394394286, 28.3386068531399, 0.00216437001654413, 0.00241834641201422, 0.000652562448522076, 0, 0.841939595375152, 1.0472297162778, -0.124650568977813, -0.0255117811320815, -0.0127809016703395, 3.77875949197914, 28.3386068531399, 70.250493081403, -0.00022441781766247, -0.000161662683240138, 0.000257614374277182, 0, -0.578908839088399, 1.08917492980254, -2.46700437855907e-05, -1.13686837721616e-06, -4.54747350886464e-07, 0.000986460690910462, 0.00216437001654413, -0.00022441781766247, 0.000148247636388987, 0.000145519152283669, 3.97903932025656e-05, 0, 0.0156976511789253, 0.0678746800986119, -1.11413100967184e-05, -1.36424205265939e-06, -4.54747350886464e-07, 0.000398813426727429, 0.00241834641201422, -0.000161662683240138, 0.000145519152283669, 0.000145519152283669, 3.9676706364844e-05, 0, 0.0138438736030366, 0.0678776359563926, -3.18323145620525e-06, -2.27373675443232e-07, -2.27373675443232e-07, 0.000117665877041873, 0.000652562448522076, 0.000257614374277182, 3.97903932025656e-05, 3.9676706364844e-05, 1.77351466845721e-05, 0, 0.00317095327773131, 0.0316927071253303, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0182945996130002, 0.000335830918629654, 0.000176896719494835, 0.727547330825473, 0.841939595375152, -0.578908839088399, 0.0156976511789253, 0.0138438736030366, 0.00317095327773131, 0, 8.85630879565724, 4.44044781033881, -0.00510601694259094, -0.000448608261649497, -0.000224190443987027, 0.194675862985605, 1.0472297162778, 1.08917492980254, 0.0678746800986119, 0.0678776359563926, 0.0316927071253303, 0, 4.44044781033881, 88.8524673428037 ), .Dim = c(12L, 12L), .Dimnames = list(c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1"), c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1" )))), .Names = c("par", "value", "counts", "convergence", "message", "hessian"), class = c("list", "Tagloss")) par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") plot(o, t=1500:3000, model="1", scale=1000, add=TRUE) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) plot(o, t=1:300, model="2", scale=1000, ylim=c(0, 3), col="red", hessian=o$hessian) plot(o, t=1:300, model="1", scale=1000, add=TRUE, hessian=o$hessian) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) ###### Example with fixed.parameters data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- structure(c(49.5658922243074, 5.22150592456511, 8.00608716525864, 50.612916258503, 6, 9), .Names = c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1")) pfixed <- c(D2D1_2=10000, D3D2_2=10000, C_2=0, D2D1_1=10000, D3D2_1=10000, C_1=0) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, fixed.parameters=pfixed) # Here it is the result of the previous function o <- structure(list(par = structure(c(55.2184044121564, 5.2630294044259, 8.13359029885985, 14269.9757684677, 21.8702023948044, 6.46586480967269 ), .Names = c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1")), value = 5853.64634357369, counts = structure(c(757L, NA), .Names = c("function", "gradient" )), convergence = 0L, message = NULL, hessian = structure(c(0.036636720324168, -2.26385645873961, -1.2330608569755, -2.95585778076202e-06, -2.27373675443232e-07, -0.0399197688238928, -2.26385645873961, 232.345637869003, 47.1904784262733, 0.000118689058581367, 7.50333128962666e-06, 1.69928603099834, -1.2330608569755, 47.1904784262733, 304.432723851278, 0.000196678229258396, 1.36424205265939e-06, 2.8553522497532, -2.95585778076202e-06, 0.000118689058581367, 0.000196678229258396, 4.54747350886464e-07, 0, 0.00741636085876962, -2.27373675443232e-07, 7.50333128962666e-06, 1.36424205265939e-06, 0, 4.00177668780088e-05, 8.79936123965308e-05, -0.0399197688238928, 1.69928603099834, 2.8553522497532, 0.00741636085876962, 8.79936123965308e-05, 107.941018768543), .Dim = c(6L, 6L), .Dimnames = list( c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1"), c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1")))), .Names = c("par", "value", "counts", "convergence", "message", "hessian"), class = c("list", "Tagloss")) par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") plot(o, t=1500:3000, model="1", scale=1000, add=TRUE) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) plot(o, t=1:300, model="2", scale=1000, ylim=c(0, 3), col="red", hessian=o$hessian) plot(o, t=1:300, model="1", scale=1000, add=TRUE, hessian=o$hessian) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) ###### Example with delta data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- structure(c(45.8764973711504, 5.22489974562498, 8.07602162728874, -0.865444694177429), .Names = c("D1_2", "A_2", "B_2", "delta" )) pfixed <- c(D2D1_2=10000, D3D2_2=10000, C_2=0) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, fixed.parameters=pfixed) # Here it is the result of the previous function o <- structure(list(par = structure(c(45.9035484983855, 5.22576211343279, 8.07585745169786, -0.865706100004634), .Names = c("D1_2", "A_2", "B_2", "delta")), value = 5913.716964613, counts = structure(c(91L, NA), .Names = c("function", "gradient")), convergence = 0L, message = NULL, hessian = structure(c(0.0644593001197791, -2.88983483187621, -1.49161280660337, -0.0875163550517755, -2.88983483187621, 221.02317802819, 45.3729608125286, 3.73816044429987, -1.49161280660337, 45.3729608125286, 440.129730122862, 30.4781699469459, -0.0875163550517755, 3.73816044429987, 30.4781699469459, 9.47964940678503), .Dim = c(4L, 4L), .Dimnames = list(c("D1_2", "A_2", "B_2", "delta"), c("D1_2", "A_2", "B_2", "delta")))), .Names = c("par", "value", "counts", "convergence", "message", "hessian"), class = c("list", "Tagloss")) par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") plot(o, t=1:3000, model="1", scale=1000, col="blue", add=TRUE, hessian=o$hessian) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) ###### Example with model_after data_f_LR <- Tagloss_format(outLR, model="LR") par <- structure(c(72.0399239978454, 58.1034231071992, 645.068735669251, 5.10791337470247, 3538.47220045768, 7.83358940767931), .Names = c("D1_L2", "D2D1_L2", "D3D2_L2", "A_L2", "B_L2", "C_L2")) pfixed <- NULL # A progress bar can be shown when one core is used system.time( print(Tagloss_L(individuals=data_f_LR, par=par, days.maximum=Tagloss_daymax(data_f_LR), fixed.parameters=pfixed, mc.cores=1, model_after="pR2=pL2;pR1=pL2;pL1=pL2", progressbar = TRUE)) ) # When parallel computing is done, no progress bar can be shown system.time( print(Tagloss_L(individuals=data_f_LR, par=par, days.maximum=Tagloss_daymax(data_f_LR), fixed.parameters=pfixed, model_after="pR2=pL2;pR1=pL2;pL1=pL2")) ) # The NLR_00 are very long to calculate data_f_LR_fast <- subset(data_f_LR, subset=(is.na(data_f_LR$NLR_00))) system.time( print(Tagloss_L(individuals=data_f_LR_fast, par=par, days.maximum=Tagloss_daymax(data_f_LR_fast), fixed.parameters=pfixed, model_after="pR2=pL2;pR1=pL2;pL1=pL2")) ) o <- Tagloss_fit(data=data_f_LR_fast, fitted.parameters=par, fixed.parameters=pfixed, model_after="pR2=pL2;pR1=pL2;pL1=pL2") par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") ## End(Not run) ``` ## See Also Other Model of Tag-loss: `Tagloss_LengthObs()`, `Tagloss_cumul()`, `Tagloss_daymax()`, `Tagloss_fit()`, `Tagloss_format()`, `Tagloss_mcmc()`, `Tagloss_mcmc_p()`, `Tagloss_model()`, `Tagloss_simulate()`, `logLik.Tagloss()`, `o_4p_p1p2`, `plot.Tagloss()`, `plot.TaglossData()` ## Author(s) Marc Girondot

Tagloss_L function