Simulating likelihood ratio test (LRT) pvalue using Monte Carlo method I'm trying to figure out my assignment to simulate lrt test p-value output using the Monte Carlo method. As far as I understand, the lrt test is supposed to test for "better", more accurate model.
I know how to perform such a test:
nested <- glm(finalgrade~absences,data=grades)
complex <- glm(finalgrade~absences+age,data=grades)
lrtest(nested, complex)

From there I can return my p-value and perform some calculations like type I and type II errors or power of a test and see how it changes depending of number of simulations.
My question is how am I supposed to simulate the random data. It doesn't have to be grades or school related stuff this was just a showcase of my understanding.
I was thinking about making data frame with 3 to 4 columns with 1 column being a dependent value (0,1) and the rest being random numbers generated from the normal distribution or some different distribution.
But I don't know if this approach will create understandable results, or if this even makes sense.
I looked at this function function but it didn't really help me to understand anything.
I came up with something like this:
library(lmtest)
n <- 1000
depentend = sample(c(0,1), replace=TRUE, size=n)

pvalue <- c()
for(i in 1:1000) {
  independend_x = rnorm(n, mean = 2,sd = 0.2)
  independend_y = rnorm(n, mean = 7,sd = 0.5)
  
  nested <- lm(depentend~independend_x)
  complex <- lm(depentend~independend_x + independend_y)
  lrtest(nested, complex)
  
  pvalue <- c(pvalue, as.numeric(lrtest(nested, complex)[5][2,1]))
}

but I don't know if this is the right direction.
I would be really thankful if someone could help me to understand how to simulate data for the Monte Carlo sampling method.

Monte Carlo simulations are performed to compute a distribution of something that is difficult to compute or for which one is too lazy to perform the exact computation.
The likelihood ratio test computes a p-value based on the distribution of the likelihood ratio $\Lambda$, and that distribution is the value that you want to simulate instead of compute or estimate with formula's. The trick is to use simulation instead of computations.
Your problem does not seem to be so much how to perform the simulations, but more like what is the distribution that you are interested in and want to simulate and what are the boundary conditions that you need to fix. Which computation or estimation is it that you want to replace/estimate with simulation?

*

*For your likelihood ratio test you probably want to test the hypothesis $H_0: \theta_{age} = 0$ against the alternative hypothesis $H_a: \theta_{age} \neq 0$. In this case you compute the ratio of the likelihood $\mathcal{L}$ where one of the hypotheses is a composite hypothesis and you select the highest likelihood among them.
$$\Lambda = \frac{\mathcal{L}(\theta_{age} = 0|  \text{some data})}{\text{sup}_{\theta_{age} \neq 0}\mathcal{L}( \theta_{age} | \text{some data})} = \frac{\mathcal{L}(\theta_{age} = 0|  \text{some data})}{\mathcal{L}( \hat\theta_{age} | \text{some data})} $$ where the supremum is found by using the likelihood for the maximum likelihood estimator $ \hat\theta_{age} $
To compute these likelihood functions you need assumptions about the distributions. In your case you do this with glm (where you need to decide on some distribution and link function) or more simple lm (which assumes Gaussian conditional distribution for the data).


*The simulations are then computed for a given null hypothesis. For instance, given some data, you assume that $\theta_{age} = 0$ and you want to compute what the distribution of the outcomes of $\Lambda$ is. You need some more data and parameters

*

*The independent variables. These you probably want to fix at some values that relate to your practical problem. You want to know the distribution given some independent variables. Potentially you may wish to study what happens when there is an error in these independent variables, in that case you may also simulate these variables.

*The variance/dispersion/noise-level of the conditional distribution. This you may vary to see how this influences the statistic. Or you have some value of interest, for instance if you have data for which you estimated the noise.

*The other coefficients. These you may likewise vary or keep fixed depending on the situation, whether you want to model a particular situation or a more range of situations.



Example
The code below computes a simulation for a given regressor matrix (the independent variables) and given other coefficients. For large sample size the distribution will approach a chi-squared distribution. The simulation shows that using that limit as an estimate for the distribution underestimates the p-value by a lot.
(I ran the code with only 5000 simulations because I am using an online r-editor an compiler, on a computer you can get more precise results)

n_sim = 5*10^3

### simulate likelihood ratio test 
### given coefficient and independent variables
### we assume a logistic model with binomial distribution 
sim = function(theta1, X) {
   ### compute model
   Z = X %*% theta1
   p = 1/(1+exp(-Z))
   ### simulate dependent variable
   Y = rbinom(length(p), 1, p)

   ### compute (log)likelihood ratio
   mod1 = glm(Y ~ 1 + X[,2] + X[,3], family = binomial)
   mod0 = glm(Y ~ 1 + X[,2], family = binomial)
   logratio = -2*(logLik(mod0)-logLik(mod1))
   return(as.numeric(logratio))
} 

set.seed(1)
n = 10
### coefficients with the last one zero
theta1 = c(1,1,0) 
### some regressor matrix, independent variables
X = cbind(rep(1,n), matrix(rnorm(n*2),n)) ### first column is intercept 


### simulate
Lsim = replicate(n_sim,sim(theta1,X))

### ordering for empirical distribution 
Lsim = Lsim[order(Lsim)]
perc = c(1:length(Lsim))/length(Lsim)

plot(Lsim,1-perc, main = "emperical distribution", ylab = "P(likelihood > L)", xlab = "L", type = "l")
lines(qchisq(perc,1),1-perc, lty = 2)

legend(8,1, c("n=10","n=40", "chi-squared estimate"), lty = c(1,1,2), col = c(1,2,1))


#### repeat with larger n
set.seed(1)
n = 40
theta1 = c(1,1,0) 
X = cbind(rep(1,n), matrix(rnorm(n*2),n)) 
Lsim2 = replicate(n_sim,sim(theta1,X))
Lsim2 = Lsim2[order(Lsim2)]
lines(Lsim2, 1-perc, col = 2)

Note that there are many variants and this is just an example what simulation does. Here we simulate data based on a given distribution. (And it replaces a computation that we could not perform. We had an estimate with a chi-squared distribution, but that is not accurate for small $n$.)
Other times this distribution is not know and one uses the data and some resampling method to simulate/estimate the distribution of the statistic.
For your situation you need to figure out what exact computation (for which information/conditions are given) it is that you want to replace by using simulations.
