I am using lme function in R to run a model:
f0 <- lme(value ~ -1 + cs_d0 + va_d0 + cs_d0:visitmse + va_d0:visitmse + cs_d0:ageb + va_d0:ageb +
+ cs_d0:lesionse + va_d0:lesionse, random =
+ ~ -1 + (cs_d0 + va_d0) + visitmse:( cs_d0 + va_d0)|pat , weights =
+ varIdent(form=~1| cs_d0), control=lmeControl(opt="optim"), data =D0Train)
but I faced an error
Error in MEEM(object, conLin, control$niterEM) :
Singularity in backsolve at level 0, block 1
When I searched about that and tried to look at model matrix I found this problem :
> m <- model.matrix(value ~ -1 + cs_d0 + va_d0 + cs_d0:visitmse + va_d0:visitmse + cs_d0:ageb + va_d0:ageb +
+ cs_d0:lesionse + va_d0:lesionse + va_d0:gender ,data =D0Train)
> m[1:10,]
cs_d0 va_d0 cs_d0:visitmse va_d0:visitmse cs_d0:ageb va_d0:ageb cs_d0:lesionse0 cs_d0:lesionse1 va_d0:lesionse1 va_d0:gender2
1 1 0 0.0 0 70 0 0 1 0 0
2 1 0 0.5 0 70 0 0 1 0 0
3 1 0 1.0 0 70 0 0 1 0 0
4 1 0 9.0 0 70 0 0 1 0 0
5 1 0 0.0 0 80 0 1 0 0 0
6 1 0 3.0 0 80 0 1 0 0 0
7 1 0 6.0 0 80 0 1 0 0 0
8 1 0 0.0 0 77 0 1 0 0 0
9 1 0 3.0 0 77 0 1 0 0 0
10 1 0 6.0 0 77 0 1 0 0 0
The lesionse is factor that has two levels (0,1),
str(D0Train$lesionse)
Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 1 1 1 ...
but in the model (cs_d0:lesionse0 , cs_d0:lesionse1 ) it seems has more than two levels.
Here an example :
# lesion type
lesion = factor(c(rep(0,150),rep(1,50)))
# gender
sex = factor(c(rep(0,150),rep(1,50)), label=c("Female","Male"))
# visiting time by months
time = rep(c(0,3,6,9),time = 4, 200)
# subjects
subject = rep(1:50, each = 4)
# first response variable "identity"
x_1 = c(rep(0, 100), rep(1,100))
# second response variable "identity"
x_2 = c(rep(1, 100), rep(0,100))
# values of both reponses variables (x_1, x_2)
value = c(rnorm(100,20,1),rnorm(100,48,1))
# variables refer to reponses variables (x_1, x_2)
variable = factor(c(rep(0,150),rep(1,50)), label=c("X2","X1"))
df = data.frame(subject , time, sex, lesion, x_1,x_2,value, variable)
library(nlme)
# fit the model that each response variable has intercept and slope (time) for each random and fixed effects
# as well as fixed effects slopes for sex and lesion, and each response has different variance
f= lme(value ~ -1 + x_1 + x_2 + x_1:time + x_2:time + x_1:lesion + x_2:lesion +
x_1:sex + x_2:sex, random = ~ -1 + (x_1 + x_2) + time:( x_1 + x_2)|subject ,
weights = varIdent(form=~1| x_1), control=lmeControl(opt="optim"), data =df)
fm = model.matrix(value ~ -1 + x_1 + x_2 + x_1:time + x_2:time + x_1:lesion + x_2:lesion +
x_1:sex + x_2:sex,data=df)
May you help me or give some suggestions?
