Tagloss_L: Return the -log likelihood of a set of individuals under a...
In phenology: Tools to Manage a Parametric Function that Describes Phenology and More
Tagloss_L R Documentation
Return the -log likelihood of a set of individuals under a model of tagloss.
Description
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().
Usage
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?
Details
Tagloss_L returns the -log likelihood of a set of individuals under a model of tagloss.
Value
Return the -log likelihood of a set of individuals
Author(s)
Marc Girondot
See Also
Other Model of Tag-loss:
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc_p(),
Tagloss_mcmc(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.TaglossData(),
plot.Tagloss()
Examples
## 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)
phenology documentation built on Oct. 16, 2023, 9:06 a.m.
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| Tagloss_L | R Documentation |
Return the -log likelihood of a set of individuals under a model of tagloss.
Description
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().
Usage
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? |
Details
Tagloss_L returns the -log likelihood of a set of individuals under a model of tagloss.
Value
Return the -log likelihood of a set of individuals
Author(s)
Marc Girondot
See Also
Other Model of Tag-loss:
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc_p(),
Tagloss_mcmc(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.TaglossData(),
plot.Tagloss()
Examples
## 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)
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