I am getting error in fitting Model based recursive partitioning - r

Question

library("partykit")
library(data.table) ## optional, but what I'll use to coerce the list into a DT
library(future.apply) ## for parallel stuff
library(glmnet)
plan(multisession) ## use all available cores
#sample size
N <- 200
#coefficients
betas <- list()
betas$b0 <- 1
betas$b1_up <- 2.4
betas$b1_down <- 2
betas$b2_up <- 2.4
betas$b2_down <- 2
ols_formula <- y ~a| x1 + x2+ x3+ x4+ x5+ x6 +x7 +x8+ x9 + x10
#lmtee # function to fit mob tree
#""0 +"" ---> supress the 'double' interecept
ols <- function(y, a, start = NULL, weights = NULL, offset = NULL,..){
lm(y ~ 0 + a)
}
reg_simulation_mob <- function(...){
#data
data <- data. Frame(
a= rbinom(N,1,0.5),
x1 = rnorm(N),
x2 = rnorm(N),
x3=rnorm(N),
x4=rnorm(N),
x5=rnorm(N),
x6=rnorm(N),
x7=rnorm(N),
x8=rnorm(N),
x9=rnorm(N),
x10=rnorm(N),
e = rnorm(N))
#dependent variable
data$y <- betas$b0 + with(data, ifelse(a>0, betas$b1_up * x1 + betas$b2_up * x2 , betas$b1_down * x1 + betas$b2_down * x2 )+ e )
#Estimate mob()-OLS
ols_mob <- lm(ols_formula, data = data, fit =ols)
# return(ols$coefficients)
return(ols_mob)
}
# N repetitions
nreps <- 4
## Parallel version
results <- future_lapply(1:nreps, reg_simulation_mob,future.seed =NULL)
results
The trees are not splitting, moreover I intend to regress y over a treatment with x1...x10 as covariates. The result I obtained is given below
Call:
lm(formula = ols_formula, data = data, fit = ols)
Coefficients:
(Intercept)
0.8658
a | x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10TRUE
NA