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Dear,
Thank you again for this software. Unfortunately I am having trouble getting it to run on my own data. When I run the code below , I get the following error message from JointModel(): Error in solve.default(VC) : system is computationally singular: reciprocal condition number = 3.40196e-17 , Error: cannot allocate vector of size 86.8 Gb, Error in lme.formula(obs ~ IND_SEVVP0 + AUDI0_C + SEXE + CENTRE + DIPNIV0C + :
nlminb issue, code d'convergence error = 1 message = false convergence (8)
Error in optim(thetas, opt.survWB, gr.survWB, method = "BFGS", control = list(maxit = if (it < : unfinished value provided by optim
Warning message:
In jointModel(lme_Isa_30_M4VP2, Cox_Isa_30_M4VPquad, timeVar = "T", :
infinite or missing values in Hessian at convergence.
### Please, if someone could help me;; i'm really stuck
I am not sure to help because you got several errors but for the following error message
Error in solve.default(VC) : system is computationally singular: reciprocal condition number = 3.40196e-17
I fixed it by scaling the values of the outcome with a standard transformation ~N(0, 1)
You should also check if you have non-finite values (Inf) in your dataset and remove it
Bonjour
Je crois que nous travaillons au même endroit (Campus de médecine à
Bordeaux)
Pour répondre à votre proposition, j'ai transformé les tests cognitifs avec
une fonction spline, pour qu'ils suivent une loi normale.. mais ça ne
fonctionne pas
Pour les valeurs manquantes ou infinies je n'ai pas compris, il faut les
enlever ? vous pensez que c'est la cause de l'erreur que j'ai
* " Warning message: In jointModel(lme_Isa_30_M4VP2, Cox_Isa_30_M4VPquad,
timeVar = "T", : infinite or missing values in Hessian at convergence."*
Merci
Bien cordialement
Le jeu. 31 oct. 2019 à 10:16, Jérémie Lespinasse <notifications@github.com>
a écrit :
I am not sure to help because you got several errors but for the following
error message
- *Error in solve.default(VC) : system is computationally singular:
reciprocal condition number = 3.40196e-17*
I fixed it by scaling the values of the outcome with a standard
transformation ~N(0, 1)
You should also check if you have missing values or non-finite values (Inf)
in your dataset and remove it
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Dear,
Thank you again for this software. Unfortunately I am having trouble getting it to run on my own data. When I run the code below , I get the following error message from JointModel(): Error in solve.default(VC) : system is computationally singular: reciprocal condition number = 3.40196e-17 , Error: cannot allocate vector of size 86.8 Gb,
Error in lme.formula(obs ~ IND_SEVVP0 + AUDI0_C + SEXE + CENTRE + DIPNIV0C + :
nlminb issue, code d'convergence error = 1 message = false convergence (8)
Error in optim(thetas, opt.survWB, gr.survWB, method = "BFGS", control = list(maxit = if (it < : unfinished value provided by optim
Warning message:
In jointModel(lme_Isa_30_M4VP2, Cox_Isa_30_M4VPquad, timeVar = "T", :
infinite or missing values in Hessian at convergence.
### Please, if someone could help me;; i'm really stuck
Linear mixed model
MixteISAVP4 <- Base_ISA[(!is.na(Base_ISA$IND_SEVVP0) & (!is.na(Base_ISA$AUDI0_C)) & (!is.na(Base_ISA$DIPNIV0C))
& (!is.na(Base_ISA$VIVRE_SEUL0)) & (!is.na(Base_ISA$REVENU0C)) & (!is.na(Base_ISA$FUME0))
& (!is.na(Base_ISA$BMI0C)) & (!is.na(Base_ISA$ATCDCAR)) & (!is.na(Base_ISA$ATCDAVC))
& (!is.na(Base_ISA$HTA0_1)) & (!is.na(Base_ISA$DEPRES0C)) & (!is.na(Base_ISA$DIABBIS0C))
& (!is.na(Base_ISA$TRIGLY0C)) & (!is.na(Base_ISA$APOE4C)) & (!is.na(Base_ISA$HYPCT024C))), ]
CoxISA4$DELAIS<- CoxISA4$DELAIS/365
MixteISAVP4$T <- MixteISAVP4$T/365
ctrl <- lmeControl(opt='optim');
lme_Isa_30_M4VP2 <- lme(obs ~ IND_SEVVP0 + AUDI0_C + SEXE + CENTRE + DIPNIV0C + Prem + Age65 + VIVRE_SEUL0 + REVENU0C +
FUME0 + BMI0C + ATCDAVC + ATCDCAR + HTA0_1 + DEPRES0C + DIABBIS0C + TRIGLY0C +
APOE4C + HYPCT024C + T + I(T^2) +
IND_SEVVP0T + AUDI0_CT + SEXET + CENTRET + DIPNIV0CT + Age65T + DIABBIS0CT + APOE4CT +
IND_SEVVP0I(T^2) + AUDI0_CI(T^2) + SEXEI(T^2) + CENTREI(T^2) + DIPNIV0CI(T^2) + Age65I(T^2) + DIABBIS0CI(T^2) + APOE4CI(T^2),
random = ~ T + I(T^2) | NUM, method="ML", control=ctrl, na.action=na.omit, data = MixteISAVP4)
#summary(lme_Isa_30_M4VP2)
Cox
CoxISA4 <- MixteISAVP4[!duplicated(MixteISAVP4$NUM), ] # passe en 1 ligne par sujet
Cox_Isa_30_M4VPquad <- coxph(Surv(DELAIS, INDICDP) ~ IND_SEVVP0 + AUDI0_C + SEXE + CENTRE + DIPNIV0C + Prem + Age65
+ VIVRE_SEUL0 + REVENU0C + FUME0 + BMI0C + ATCDAVC + ATCDCAR + HTA0_1 + DEPRES0C
+ DIABBIS0C + TRIGLY0C + APOE4C + HYPCT024C, data = CoxISA4, x = TRUE, model=true)
#Cox_Isa_30_M4VPquad
JOINT MODEL
ctrljm<-list(iter.EM=500)
fitJOINTvalue_ISA30_M4VPquad <- jointModel(lme_Isa_30_M4VP2,
Cox_Isa_30_M4VPquad,
timeVar = "T",
method="Cox-PH-GH",
verbose=TRUE,
control=ctrljm)
#control = list(GHk = 3, lng.in.kn=1))
fitJOINTvalue_ISA30_M4VPquad <- jointModel(lme_Isa_30_M4VP2,
Cox_Isa_30_M4VPquad,
timeVar = "T",
method = "spline-PH-aGH")
fitJOINTvalue_ISA30_M4VPquad <- jointModel(lme_Isa_30_M4VP2,
Cox_Isa_30_M4VPquad,
timeVar = "T",
method = "piecewise-PH-aGH")
summary(fitJOINTvalue_ISA30_M4VPquad)
jointFit.aids3 <- jointModel(lme_Isa_30_M4VP2, Cox_Isa_30_M4VPquad,
timeVar = "T", method = "piecewise-PH-aGH", GHk = 3)
jointFit.aids6 <- jointModel(lme_Isa_30_M4VP2, Cox_Isa_30_M4VPquad,
timeVar = "T", method = "piecewise-PH-aGH", GHk = 6)
jointFit.aids9 <- jointModel(lme_Isa_30_M4VP2, Cox_Isa_30_M4VPquad,
timeVar = "T", method = "piecewise-PH-aGH", GHk = 9)
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