Error in inla.inlaprogram.has.crashed()
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Yijing WANG
May 11, 2020, 6:01:14 AM5/11/20
to R-inla discussion group
Hello,
Recently, I use R-INLA to make a spatial-temporal model of 1902 districts in Japan for 32 years, and population was divided into 18 age groups. However, the console had the message showing "Error in inla.inlaprogram.has.crashed()" ?
（But when I use part of the my data to run, such as 5 age groups and 20 years, it works.)
Does you have any suggestions?
Many thanks!
------here are part of my code and error imformation------
> formula9<- y~ 1 + age+ year + f(dist,model = "bym",graph = jp.adj)
>
> result9<-inla(formula9, family = "poisson", data = data, E=n,
+ control.predictor=list(link=1, compute=TRUE),
+ control.compute =list(dic=TRUE,cpo=TRUE),
+ verbose=TRUE)
hgid: 8b30e851fed2 date: Tue Mar 17 10:38:12 2020 +0300
Report bugs to <help@r-inla.org>
Process file[/tmp/RtmpQBh7Yp/file11d107e022b2b/Model.ini] threads[72] blas_threads[1]
inla_build...
number of sections=[10]
parse section=[0] name=[INLA.libR] type=[LIBR]
inla_parse_libR...
section[INLA.libR]
R_HOME=[/export/home/fengchy3/.conda/envs/fengchy/lib/R]
parse section=[9] name=[INLA.Expert] type=[EXPERT]
inla_parse_expert...
section[INLA.Expert]
disable.gaussian.check=[0]
cpo.manual=[0]
jp.file=[(null)]
jp.model=[(null)]
parse section=[1] name=[INLA.Model] type=[PROBLEM]
inla_parse_problem...
name=[INLA.Model]
R-INLA tag=[Version_20.03.17]
Build tag=[Version_20.03.17]
openmp.strategy=[default]
pardiso-library installed and working? = [no]
smtp = [taucs]
strategy = [default]
store results in directory=[/tmp/RtmpQBh7Yp/file11d107e022b2b/results.files]
output:
cpo=[1]
po=[0]
dic=[1]
kld=[1]
mlik=[1]
q=[0]
graph=[0]
gdensity=[0]
hyperparameters=[1]
summary=[1]
return.marginals=[1]
nquantiles=[3] [ 0.025 0.5 0.975 ]
ncdf=[0] [ ]
parse section=[3] name=[Predictor] type=[PREDICTOR]
inla_parse_predictor ...
section=[Predictor]
dir=[predictor]
PRIOR->name=[loggamma]
hyperid=[53001|Predictor]
PRIOR->from_theta=[function (x) <<NEWLINE>>exp(x)]
PRIOR->to_theta = [function (x) <<NEWLINE>>log(x)]
PRIOR->PARAMETERS=[1, 1e-05]
initialise log_precision[12]
fixed=[1]
user.scale=[1]
n=[1085524]
m=[0]
ndata=[1085524]
compute=[1]
read offsets from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8]
read n=[2171048] entries from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8]
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8] 0/1085524 (idx,y) = (0, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8] 1/1085524 (idx,y) = (1, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8] 2/1085524 (idx,y) = (2, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8] 3/1085524 (idx,y) = (3, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8] 4/1085524 (idx,y) = (4, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8] 5/1085524 (idx,y) = (5, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8] 6/1085524 (idx,y) = (6, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8] 7/1085524 (idx,y) = (7, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8] 8/1085524 (idx,y) = (8, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104426e4b8] 9/1085524 (idx,y) = (9, 0)
read link.fitted.values from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84]
read n=[2171048] entries from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84]
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84] 0/1085524 (idx,y) = (0, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84] 1/1085524 (idx,y) = (1, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84] 2/1085524 (idx,y) = (2, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84] 3/1085524 (idx,y) = (3, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84] 4/1085524 (idx,y) = (4, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84] 5/1085524 (idx,y) = (5, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84] 6/1085524 (idx,y) = (6, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84] 7/1085524 (idx,y) = (7, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84] 8/1085524 (idx,y) = (8, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d102f00ca84] 9/1085524 (idx,y) = (9, 0)
Aext=[(null)]
AextPrecision=[1e+08]
output:
summary=[1]
return.marginals=[1]
nquantiles=[3] [ 0.025 0.5 0.975 ]
ncdf=[0] [ ]
parse section=[2] name=[INLA.Data1] type=[DATA]
inla_parse_data [section 1]...
tag=[INLA.Data1]
family=[POISSON]
likelihood=[POISSON]
file->name=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1050f3a3fa]
file->name=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d104663253f]
file->name=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d10113c0086]
read n=[3256572] entries from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1050f3a3fa]
mdata.nattributes = 0
0/1085524 (idx,a,y,d) = (0, 5578, 26, 1)
1/1085524 (idx,a,y,d) = (1, 650, 70, 1)
2/1085524 (idx,a,y,d) = (2, 453, 72, 1)
3/1085524 (idx,a,y,d) = (3, 1091, 88, 1)
4/1085524 (idx,a,y,d) = (4, 1538, 88, 1)
5/1085524 (idx,a,y,d) = (5, 3100, 4, 1)
6/1085524 (idx,a,y,d) = (6, 4513, 40, 1)
7/1085524 (idx,a,y,d) = (7, 3651, 0, 1)
8/1085524 (idx,a,y,d) = (8, 7323, 3, 1)
9/1085524 (idx,a,y,d) = (9, 5891, 7, 1)
likelihood.variant=[0]
Link model [LOG]
Link order [-1]
Link variant [-1]
Link ntheta [0]
mix.use[0]
parse section=[7] name=[dist] type=[FFIELD]
inla_parse_ffield...
section=[dist]
dir=[random.effect00000001]
model=[bym]
PRIOR0->name=[loggamma]
hyperid=[10001|dist]
PRIOR0->from_theta=[function (x) <<NEWLINE>>exp(x)]
PRIOR0->to_theta = [function (x) <<NEWLINE>>log(x)]
PRIOR0->PARAMETERS0=[1, 0.0005]
PRIOR1->name=[loggamma]
hyperid=[10002|dist]
PRIOR1->from_theta=[function (x) <<NEWLINE>>exp(x)]
PRIOR1->to_theta = [function (x) <<NEWLINE>>log(x)]
PRIOR1->PARAMETERS1=[1, 0.0005]
correct=[-1]
constr=[0]
diagonal=[1.01511e-05]
id.names=<not present>
compute=[1]
nrep=[1]
ngroup=[1]
read covariates from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7]
read n=[2171048] entries from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7]
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7] 0/1085524 (idx,y) = (0, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7] 1/1085524 (idx,y) = (1, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7] 2/1085524 (idx,y) = (2, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7] 3/1085524 (idx,y) = (3, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7] 4/1085524 (idx,y) = (4, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7] 5/1085524 (idx,y) = (5, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7] 6/1085524 (idx,y) = (6, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7] 7/1085524 (idx,y) = (7, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7] 8/1085524 (idx,y) = (8, 0)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d106034d1d7] 9/1085524 (idx,y) = (9, 0)
read graph from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d10741ccf59]
file for locations=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1076c2d4b6]
nlocations=[1902]
locations[0]=[1]
locations[1]=[2]
locations[2]=[3]
locations[3]=[4]
locations[4]=[5]
locations[5]=[6]
locations[6]=[7]
locations[7]=[8]
locations[8]=[9]
locations[9]=[10]
initialise log_precision (iid component)[4]
fixed=[0]
initialise log_precision (spatial component)[4]
fixed=[0]
adjust.for.con.comp[1]
scale.model[0]
read extra constraint from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1033ed5f78]
Constraint[0]
A[1902] = 1.000000
A[1903] = 1.000000
A[1904] = 1.000000
A[1905] = 1.000000
A[1906] = 1.000000
A[1907] = 1.000000
A[1908] = 1.000000
A[1909] = 1.000000
A[1910] = 1.000000
A[1911] = 1.000000
A[1912] = 1.000000
e[0] = 0.000000
Constraint[1]
A[2029] = 1.000000
A[2030] = 1.000000
e[1] = 0.000000
Constraint[2]
A[2091] = 1.000000
A[2092] = 1.000000
e[2] = 0.000000
Constraint[3]
A[2093] = 1.000000
A[2094] = 1.000000
A[2095] = 1.000000
e[3] = 0.000000
Constraint[4]
A[2096] = 1.000000
A[2097] = 1.000000
A[2098] = 1.000000
A[2099] = 1.000000
A[2100] = 1.000000
A[2101] = 1.000000
A[2102] = 1.000000
A[2103] = 1.000000
A[2104] = 1.000000
A[2105] = 1.000000
A[2106] = 1.000000
e[4] = 0.000000
Constraint[5]
A[3197] = 1.000000
A[3215] = 1.000000
A[3217] = 1.000000
e[5] = 0.000000
Constraint[6]
A[3451] = 1.000000
A[3452] = 1.000000
e[6] = 0.000000
Constraint[7]
A[3514] = 1.000000
A[3515] = 1.000000
A[3516] = 1.000000
A[3517] = 1.000000
A[3518] = 1.000000
A[3519] = 1.000000
A[3520] = 1.000000
A[3521] = 1.000000
A[3522] = 1.000000
A[3523] = 1.000000
A[3524] = 1.000000
e[7] = 0.000000
Constraint[8]
A[3640] = 1.000000
A[3643] = 1.000000
A[3675] = 1.000000
e[8] = 0.000000
Constraint[9]
A[3726] = 1.000000
A[3749] = 1.000000
A[3750] = 1.000000
e[9] = 0.000000
Constraint[10]
A[3735] = 1.000000
A[3752] = 1.000000
A[3753] = 1.000000
A[3754] = 1.000000
A[3755] = 1.000000
e[10] = 0.000000
Constraint[11]
A[3757] = 1.000000
A[3758] = 1.000000
A[3759] = 1.000000
e[11] = 0.000000
Constraint[12]
A[3760] = 1.000000
A[3761] = 1.000000
e[12] = 0.000000
Constraint[13]
A[3763] = 1.000000
A[3764] = 1.000000
A[3766] = 1.000000
A[3767] = 1.000000
A[3768] = 1.000000
A[3769] = 1.000000
A[3770] = 1.000000
A[3771] = 1.000000
A[3773] = 1.000000
A[3774] = 1.000000
A[3775] = 1.000000
e[13] = 0.000000
rank-deficiency is *defined* [63]
output:
summary=[1]
return.marginals=[1]
nquantiles=[3] [ 0.025 0.5 0.975 ]
ncdf=[0] [ ]
section=[4] name=[(Intercept)] type=[LINEAR]
inla_parse_linear...
section[(Intercept)]
dir=[fixed.effect00000001]
file for covariates=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9]
read n=[2171048] entries from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9]
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9] 0/1085524 (idx,y) = (0, 1)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9] 1/1085524 (idx,y) = (1, 1)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9] 2/1085524 (idx,y) = (2, 1)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9] 3/1085524 (idx,y) = (3, 1)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9] 4/1085524 (idx,y) = (4, 1)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9] 5/1085524 (idx,y) = (5, 1)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9] 6/1085524 (idx,y) = (6, 1)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9] 7/1085524 (idx,y) = (7, 1)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9] 8/1085524 (idx,y) = (8, 1)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d101bb99ff9] 9/1085524 (idx,y) = (9, 1)
prior mean=[0]
prior precision=[0]
compute=[1]
output:
summary=[1]
return.marginals=[1]
nquantiles=[3] [ 0.025 0.5 0.975 ]
ncdf=[0] [ ]
section=[5] name=[age] type=[LINEAR]
inla_parse_linear...
section[age]
dir=[fixed.effect00000002]
file for covariates=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7]
read n=[2171048] entries from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7]
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7] 0/1085524 (idx,y) = (0, 10)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7] 1/1085524 (idx,y) = (1, 17)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7] 2/1085524 (idx,y) = (2, 18)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7] 3/1085524 (idx,y) = (3, 17)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7] 4/1085524 (idx,y) = (4, 16)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7] 5/1085524 (idx,y) = (5, 1)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7] 6/1085524 (idx,y) = (6, 12)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7] 7/1085524 (idx,y) = (7, 3)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7] 8/1085524 (idx,y) = (8, 5)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d105d3233a7] 9/1085524 (idx,y) = (9, 5)
prior mean=[0]
prior precision=[0.001]
compute=[1]
output:
summary=[1]
return.marginals=[1]
nquantiles=[3] [ 0.025 0.5 0.975 ]
ncdf=[0] [ ]
section=[6] name=[year] type=[LINEAR]
inla_parse_linear...
section[year]
dir=[fixed.effect00000003]
file for covariates=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2]
read n=[2171048] entries from file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2]
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2] 0/1085524 (idx,y) = (0, 12)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2] 1/1085524 (idx,y) = (1, 5)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2] 2/1085524 (idx,y) = (2, 6)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2] 3/1085524 (idx,y) = (3, 17)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2] 4/1085524 (idx,y) = (4, 12)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2] 5/1085524 (idx,y) = (5, 21)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2] 6/1085524 (idx,y) = (6, 10)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2] 7/1085524 (idx,y) = (7, 30)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2] 8/1085524 (idx,y) = (8, 17)
file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file11d1019ed45b2] 9/1085524 (idx,y) = (9, 21)
prior mean=[0]
prior precision=[0.001]
compute=[1]
output:
summary=[1]
return.marginals=[1]
nquantiles=[3] [ 0.025 0.5 0.975 ]
ncdf=[0] [ ]
Index table: number of entries[5], total length[1089331]
tag start-index length
Predictor 0 1085524
dist 1085524 3804
(Intercept) 1089328 1
age 1089329 1
year 1089330 1
parse section=[8] name=[INLA.Parameters] type=[INLA]
inla_parse_INLA...
section[INLA.Parameters]
lincomb.derived.only = [Yes]
lincomb.derived.correlation.matrix = [No]
global_node.factor = 2.000
global_node.degree = 2147483647
reordering = -1
Contents of ai_param 0x6d875f0
Optimiser: DEFAULT METHOD
Option for GSL-BFGS2: tol = 0.1
Option for GSL-BFGS2: step_size = 1
Option for GSL-BFGS2: epsx = 0.005
Option for GSL-BFGS2: epsf = 0.000353553
Option for GSL-BFGS2: epsg = 0.005
Restart: 0
Mode known: No
Gaussian approximation:
tolerance_func = 0.0005
tolerance_step = 0.0005
optpar_fp = 0
optpar_nr_step_factor = -0.1
Gaussian data: No
Strategy: Use a mean-skew corrected Gaussian by fitting a Skew-Normal
Fast mode: On
Use linear approximation to log(|Q +c|)? Yes
Method: Compute the derivative exact
Parameters for improved approximations
Number of points evaluate: 9
Step length to compute derivatives numerically: 0.000100002
Stencil to compute derivatives numerically: 5
Cutoff value to construct local neigborhood: 0.0001
Log calculations: On
Log calculated marginal for the hyperparameters: On
Integration strategy: Automatic (GRID for dim(theta)=1 and 2 and otherwise CCD)
f0 (CCD only): 1.100000
dz (GRID only): 0.750000
Adjust weights (GRID only): On
Difference in log-density limit (GRID only): 6.000000
Skip configurations with (presumed) small density (GRID only): On
Gradient is computed using Central difference with step-length 0.010000
Hessian is computed using Central difference with step-length 0.100000
Hessian matrix is forced to be a diagonal matrix? [No]
Compute effective number of parameters? [Yes]
Perform a Monte Carlo error-test? [No]
Interpolator [Auto]
CPO required diff in log-density [3]
Stupid search mode:
Status [On]
Max iter [1000]
Factor [1.05]
Numerical integration of hyperparameters:
Maximum number of function evaluations [100000]
Relative error ....................... [1e-05]
Absolute error ....................... [1e-06]
To stabilise the numerical optimisation:
Minimum value of the -Hessian [-inf]
Strategy for the linear term [Keep]
CPO manual calculation[No]
Laplace-correction is Disabled.
inla_build: check for unused entries in[/tmp/RtmpQBh7Yp/file11d107e022b2b/Model.ini]
inla_INLA...
Strategy = [DEFAULT]
Sparse-matrix library... = [taucs]
OpenMP strategy......... = [huge]
Density-strategy........ = [Low]
Size of graph........... = [1089331]
Number of constraints... = [14]
Error in inla.inlaprogram.has.crashed() :
The inla-program exited with an error. Unless you interupted it yourself, please rerun with verbose=TRUE and check the output carefully.
If this does not help, please contact the developers at <help@r-inla.org>.
Calls: inla -> inla.inlaprogram.has.crashed
Execution halted
Helpdesk
May 11, 2020, 7:15:40 AM5/11/20
to Yijing WANG, R-inla discussion group
with a graph of size 1 million, it seem like you'll run out of memory.
I would try with the PARDISO library, in parallel mode, see
inla.pardiso()
and follow the instructions to obtain the license.
run with
control.compute=list(openmp.strategy="pardiso.parallel"),
and set upfront
inla.setOption(mkl=TRUE)
and reduce the number of threads,
inla.setOption(num.threads=16)
let me know.
I can try to run it here on our server if you like
H
On Mon, 2020-05-11 at 03:01 -0700, Yijing WANG wrote:
> Hello,
>
> Recently, I use R-INLA to make a spatial-temporal model of 1902
> districts in Japan for 32 years, and population was divided into 18
> age groups. However, the console had the message showing "Error in
> inla.inlaprogram.has.crashed()" ?
>
> （But when I use part of the my data to run, such as 5 age groups and
> 20 years, it works.)
>
> Does you have any suggestions?
> Many thanks!
>
> ------here are part of my code and error imformation------
>
> > formula9<- y~ 1 + age+ year + f(dist,model = "bym",graph = jp.adj)
> >
> > result9<-inla(formula9, family = "poisson", data = data, E=n,
> + control.predictor=list(link=1, compute=TRUE),
> + control.compute =list(dic=TRUE,cpo=TRUE),
> + verbose=TRUE)
>
> hgid: 8b30e851fed2 date: Tue Mar 17 10:38:12 2020 +0300
> Report bugs to <he...@r-inla.org>
> 11d102f00ca84] 0/1085524 (idx,y) = (0, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 1/1085524 (idx,y) = (1, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 2/1085524 (idx,y) = (2, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 3/1085524 (idx,y) = (3, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 4/1085524 (idx,y) = (4, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 5/1085524 (idx,y) = (5, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 6/1085524 (idx,y) = (6, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 7/1085524 (idx,y) = (7, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 8/1085524 (idx,y) = (8, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 0/1085524 (idx,y) = (0, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 1/1085524 (idx,y) = (1, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 2/1085524 (idx,y) = (2, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 3/1085524 (idx,y) = (3, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 4/1085524 (idx,y) = (4, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 5/1085524 (idx,y) = (5, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 6/1085524 (idx,y) = (6, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 7/1085524 (idx,y) = (7, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 8/1085524 (idx,y) = (8, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d105d3233a7] 5/1085524 (idx,y) = (5, 1)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d1019ed45b2] 3/1085524 (idx,y) = (3, 17)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> he...@r-inla.org>.
> You received this message because you are subscribed to the Google
> Groups "R-inla discussion group" group.
> To unsubscribe from this group and stop receiving emails from it,
> send an email to r-inla-discussion...@googlegroups.com
> .
> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/r-inla-discussion-group/969b1a91-e9f8-4410-8cde-d94d647e9c23%40googlegroups.com
> .
--
Håvard Rue
Helpdesk
he...@r-inla.org
I would try with the PARDISO library, in parallel mode, see
inla.pardiso()
and follow the instructions to obtain the license.
run with
control.compute=list(openmp.strategy="pardiso.parallel"),
and set upfront
inla.setOption(mkl=TRUE)
and reduce the number of threads,
inla.setOption(num.threads=16)
let me know.
I can try to run it here on our server if you like
H
On Mon, 2020-05-11 at 03:01 -0700, Yijing WANG wrote:
> Hello,
>
> Recently, I use R-INLA to make a spatial-temporal model of 1902
> districts in Japan for 32 years, and population was divided into 18
> age groups. However, the console had the message showing "Error in
> inla.inlaprogram.has.crashed()" ?
>
> （But when I use part of the my data to run, such as 5 age groups and
> 20 years, it works.)
>
> Does you have any suggestions?
> Many thanks!
>
> ------here are part of my code and error imformation------
>
> > formula9<- y~ 1 + age+ year + f(dist,model = "bym",graph = jp.adj)
> >
> > result9<-inla(formula9, family = "poisson", data = data, E=n,
> + control.predictor=list(link=1, compute=TRUE),
> + control.compute =list(dic=TRUE,cpo=TRUE),
> + verbose=TRUE)
>
> hgid: 8b30e851fed2 date: Tue Mar 17 10:38:12 2020 +0300
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 1/1085524 (idx,y) = (1, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 2/1085524 (idx,y) = (2, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 3/1085524 (idx,y) = (3, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 4/1085524 (idx,y) = (4, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 5/1085524 (idx,y) = (5, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 6/1085524 (idx,y) = (6, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 7/1085524 (idx,y) = (7, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d102f00ca84] 8/1085524 (idx,y) = (8, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 1/1085524 (idx,y) = (1, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 2/1085524 (idx,y) = (2, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 3/1085524 (idx,y) = (3, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 4/1085524 (idx,y) = (4, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 5/1085524 (idx,y) = (5, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 6/1085524 (idx,y) = (6, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 7/1085524 (idx,y) = (7, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> 11d106034d1d7] 8/1085524 (idx,y) = (8, 0)
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> file=[/tmp/RtmpQBh7Yp/file11d107e022b2b/data.files/file
> Calls: inla -> inla.inlaprogram.has.crashed
> Execution halted
> --
> Execution halted
> You received this message because you are subscribed to the Google
> Groups "R-inla discussion group" group.
> To unsubscribe from this group and stop receiving emails from it,
> send an email to r-inla-discussion...@googlegroups.com
> .
> To view this discussion on the web, visit
> https://groups.google.com/d/msgid/r-inla-discussion-group/969b1a91-e9f8-4410-8cde-d94d647e9c23%40googlegroups.com
> .
--
Håvard Rue
Helpdesk
he...@r-inla.org
Yijing WANG
May 11, 2020, 8:57:55 AM5/11/20
to R-inla discussion group
Thank you very much for your good solution. I used the pardiso library and rerun my program, and it seems to work, I will let you know if there is progress.
Yijing
在 2020年5月11日星期一 UTC+9下午8:15:40，Helpdesk写道：
在 2020年5月11日星期一 UTC+9下午8:15:40，Helpdesk写道：
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