# Made up data
x1 = c(0.051,0.049,0.046,0.042,0.042,0.041,0.038,0.037,0.043,0.031)
x2 = c(0.11,0.12,0.09,0.21,0.18,0.11,0.13,0.11,0.08,0.10)
y = c(0.97,0.87,0.77,0.65,0.77,0.84,0.76,0.73,0.82,0.90)

data = data.frame(x1,x2,y)

# run beta regression on data using loglog link
regression.beta = betareg(y ~ x1 + x2, link = "loglog")

# summarise result:
summary(regression.beta)

Call:
betareg(formula = y ~ x1 + x2, link = "loglog")

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max
-1.4901 -0.8370 -0.2718  0.2740  2.6258

Coefficients (mean model with loglog link):
            Estimate Std. Error z value Pr(>|z|)
(Intercept)    1.234      1.162   1.062   0.2882
x1            31.814     26.715   1.191   0.2337
x2            -7.776      3.276  -2.373   0.0176 *

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)
(phi)    24.39      10.83   2.252   0.0243 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Type of estimator: ML (maximum likelihood)
Log-likelihood: 12.06 on 4 Df
Pseudo R-squared: 0.2956
Number of iterations: 232 (BFGS) + 12 (Fisher scoring)

# predict result of first row
predict = predict(regression.beta, newdata = data[1,])

outcome = 1.234 + (0.051*31.814) - (0.11*-7.746)
outcome = 3.708