I'm really new to Rstudio so I hope someone can help me. So I have this code:
x = 1:5
alpha = 1
beta = 1.5
betaD = 0.1
s = 1
sa = 0.2
sb = 0.2
N = 10
grp = factor(rep(c("Control", "Treatment"), c(N,N)))
for(i in 1:(2*N)) {
  ai = rnorm(1, 0, sa)
  bi = rnorm(1, 0, sb)
  intercept = alpha+ai
  slope = beta + bi + ifelse(grp[i]=="Treatment", betaD, 0.0)
  y = intercept+ slope*x + rnorm(length(x), 0, s)
  tmp = data.frame(subject=i, x=x, y=y, a=ai, b=bi, group=grp[i])
  if(i==1) dataset = tmp
  else dataset = rbind(dataset, tmp)
}
require(lme4)
fitAll= lmList(y~x|subject, data=dataset)
slopes = coef(fitAll)$x
boxplot(slopes~grp)
t.test(slopes~grp, var.equal=TRUE)
fit0 = lmer(y~ x +(x|subject), data=dataset, REML=FALSE)
fit1 = lmer(y~ group*x +(x|subject), data=dataset, REML=FALSE)
anova(fit0, fit1)
When I run this, it generates this:
Two Sample t-test
data:  slopes by grp
t = -2.2495, df = 18, p-value = 0.03723
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.66690111 -0.02277686
sample estimates:
  mean in group Control mean in group Treatment 
               1.362975                1.707814
and this:
Data: dataset
Models:
fit0: y ~ x + (x | subject)
fit1: y ~ group * x + (x | subject)
     Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)  
fit0  6 326.65 342.28 -157.32   314.65                           
fit1  8 324.34 345.18 -154.17   308.34 6.3072      2     0.0427 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Basically what I want to do is put a repeat in the code so that when I click run, it will generate this however many times I specify. Then I want it to sort the p-value it generates into two groups, one group where the p-value is above 0.05 and another where it is below 0.05
As I said I'm really new to this so if someone could explain it to me simply, that would be much appreciated.
To run code several times, use replicate. Something like
replicate(
  100,
  {
     # Your code that creates the random dataset and runs ANOVA
  }
)
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