I want to restrict my beta, How can I do that?

> # Load necessary packages
> library(urca)
> library(vars)
> library(tseries)
> library(MASS)
> library(tidyverse)
> 
> # Assuming log_data is already loaded in the environment
> # Correct the order of variables
> Y <- log_data[, c("log_Spot", "log_Fut", "log_USO", "log_USL", "log_OIL")]
> 
> # Step 1: Unit root tests (code remains the same)
> 
> # Step 2: Determine the optimal lag length using VARselect
> VARselect_result <- VARselect(Y, lag.max=15, type="const")
> print("Optimal lag length selection:")
[1] "Optimal lag length selection:"
> print(VARselect_result$selection)
AIC(n)  HQ(n)  SC(n) FPE(n) 
    15      9      5     15 
> 
> # Choose the number of lags based on SC
> p <- VARselect_result$selection["HQ(n)"]
> 
> K <- p - 1  # Lagged differences in Johansen test
> # Step 3: Johansen cointegration test with corrected settings
> johansen_test <- ca.jo(Y, type="trace", ecdet="const", K=K, spec="longrun")
> summary(johansen_test)

###################### 
# Johansen-Procedure # 
###################### 

Test type: trace statistic , without linear trend and constant in cointegration 

Eigenvalues (lambda):
[1] 9.194013e-02 6.137328e-02 1.082481e-02 5.234867e-03 7.584923e-04
[6] 9.339895e-17

Values of teststatistic and critical values of test:

           test 10pct  5pct  1pct
r <= 4 |   2.29  7.52  9.24 12.97
r <= 3 |  18.16 17.85 19.96 24.60
r <= 2 |  51.06 32.00 34.91 41.07
r <= 1 | 242.53 49.65 53.12 60.16
r = 0  | 534.08 71.86 76.07 84.45

Eigenvectors, normalised to first column:
(These are the cointegration relations)

             log_Spot.l8 log_Fut.l8 log_USO.l8 log_USL.l8 log_OIL.l8
log_Spot.l8  1.000000000  1.0000000   1.000000  1.0000000   1.000000
log_Fut.l8  -1.003644123 -1.5256307  -3.331999  1.4487665  -5.903652
log_USO.l8  -0.006998315 -0.9992708   8.252360 13.6547508   8.825436
log_USL.l8  -0.002521254  0.7607663   4.716197 -8.9283064  -1.004589
log_OIL.l8   0.008398995  0.6448770  -8.722794 -8.0800039  -5.309850
constant     0.008065544  0.7922468   5.954291  0.1266867  10.436638
             constant
log_Spot.l8  1.000000
log_Fut.l8  -1.197880
log_USO.l8  -2.604882
log_USL.l8   1.305870
log_OIL.l8   1.247534
constant     2.071152

Weights W:
(This is the loading matrix)

            log_Spot.l8  log_Fut.l8   log_USO.l8    log_USL.l8   log_OIL.l8
log_Spot.d -0.061910397 0.004041937 -0.003984373 -0.0027565091 0.0003042509
log_Fut.d   0.338164478 0.016601908 -0.003969795 -0.0024759995 0.0002939173
log_USO.d  -0.022184653 0.037273171 -0.004320462 -0.0016669625 0.0002652621
log_USL.d   0.001740984 0.018450168 -0.005337932 -0.0004077368 0.0002501097
log_OIL.d  -0.060171749 0.039992249 -0.002479446 -0.0012813480 0.0003394962
               constant
log_Spot.d 6.558352e-15
log_Fut.d  2.942492e-14
log_USO.d  1.311205e-14
log_USL.d  1.067847e-14
log_OIL.d  8.429320e-15

> 
> # Step 4: Estimate VECM and transform to VAR representation
> vecm_var <- vec2var(johansen_test, r=4)
> r = 4
> # Step 5: Extract beta and alpha matrices
> beta <- johansen_test@V[1:ncol(Y), 1:r]
> alpha <- johansen_test@W[, 1:r]
> 
> print(beta)
             log_Spot.l8 log_Fut.l8 log_USO.l8 log_USL.l8
log_Spot.l8  1.000000000  1.0000000   1.000000   1.000000
log_Fut.l8  -1.003644123 -1.5256307  -3.331999   1.448766
log_USO.l8  -0.006998315 -0.9992708   8.252360  13.654751
log_USL.l8  -0.002521254  0.7607663   4.716197  -8.928306
log_OIL.l8   0.008398995  0.6448770  -8.722794  -8.080004