How can I repeat the simulation multiple times?
I'm really new to Rstudio so 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 repeat in code so that when the button is clicked it will generate this, but many times I point out. Then I want it to sort the p value it generates into two groups, one group where the p value is higher than 0.05 and another where it is less than 0.05
As I said, I am really new to this, so if someone can explain this to me simply, it would be much appreciated.
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I took the p value from t.test
for simplicity, it may not be the p value you mean. However, this is fine for demonstration purposes.
Just wrap your code in a function and use replicate
as many times as you like:
do_once <- function()
{
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)$p.value
}
p_vals <- replicate(10, do_once())
To get p values below 0.05, simply
p_vals[p_vals < 0.05]
And yes, this has nothing to do with Rstudio, R code will work in any IDE and in a simple R console.
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