Discrete choice model maximum likelihood estimation

Hello again,

I changed the name to tt. 
and for a and tt actually I was getting them from data, I didn't put them
here in the question. Now I restructured my code and below I copy the full
code, I tried many things but still getting the same error, I don't
understand where is the mistake.

I also defined one more variable to increase comprehension of the problem.
Instead of data, I define three representative vectors in the beginning. If
you run this code, you will see the error message.

# Variables, a: discrete choice variable-dependent, x and tt are independent
variables, tt is binary
a  <- c(1,1,2,3,2,3,1,2,2,3,1,1)
x  <- c(23,26,12,27,10,30,13,20,23,44,17,15)
tt <- c(1,0,0,1,1,1,0,0,1,0,1,1)

# First, just to see, the linear model

LM	<-lm(a ~ x+tt)
coefLM	<- coefficients(LM)
summary(LM)

# Probabilities for discrete choices for a=3, a=2 and a=1 respectively 
P3 <- function(bx,b3,b,tt) { 
P <- exp(bx*x+b3+b*(tt==1))/(1-exp(bx*x+b3+b*(tt==1))) 
return(P) 
} 
P2 <- function(bx,b2,b,tt) { 
P <- exp(bx*x+b2+b*(tt==1))/(1-exp(bx*x+b2+b*(tt==1))) 
return(P) 
} 
P1 <- function(bx,b1,b,tt) { 
P <- exp(bx*x+b1+b*(tt==1))/(1-exp(bx*x+b1+b*(tt==1))) 
return(P) 
}

# Likelihood functions for discrete choices for a=3, a=2 and a=1
respectively

L3 <- function(bx,b1,b2,b3,b,tt) { 
P11 <- P1(bx,b1,b,tt) 
P22 <- P2(bx,b2,b,tt) 
P33 <- P3(bx,b3,b,tt) 

L3l <- P11*P22*P33 
return(L3l) 
} 

L2 <- function(bx,b1,b2,b,tt) { 
P11 <- P1(bx,b1,b,tt) 
P22 <- P2(bx,b2,b,tt) 

L2l <- P11*P22 
return(L2l) 
} 

L1 <- function(bx,b1,b,tt) { 
P11 <- P1(bx,b1,b,tt) 

L1l <- P11 
return(L1l) 
}

# Log-likelihood function 

llfn <- function(param) { 

bx <- param[1] 
b1 <- param[2] 
b2 <- param[3] 
b3 <- param[4] 
b <- param[5] 

lL1 <- log(L1(bx,b1,b2,b,tt)) 
lL2 <- log(L2(bx,b1,b2,b3,b,tt)) 
lL3 <- log(L3(bx,b1,b2,b3,b,tt)) 

llfn <- (a==1)*lL1+(a==2)*lL2+(a==3)*lL3 
} 
start.par <- c(0,0,0,0,0) 
est <- optim(param=start.par,llfn,x=x, a=a, tt=tt, method =
c("CG"),control=list(trace=2,maxit=2000), hessian=TRUE)