Closed Virginienael closed 3 years ago
jrmie
commented
4 years ago I am not sure to help because you got several errors but for the following error message
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
Virginienael
commented
4 years ago 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
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
— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/drizopoulos/JM/issues/15?email_source=notifications&email_token=ANQMGEO56UY5PSDHZ36GI6TQRKO7ZA5CNFSM4JBWETSKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOECXBHJY#issuecomment-548279207, or unsubscribe https://github.com/notifications/unsubscribe-auth/ANQMGEOYC3PCLNVXCTNJLQTQRKO7ZANCNFSM4JBWETSA .
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
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)