我是Rstudio的新手,所以我希望有人可以帮助我。 所以我有这个代码:
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)$x
boxplot(slopes~grp)
t.test(slopes~grp, var.equal=TRUE)
fit0 = lmer(y~ x +(x|subject), data=dataset, REML=FALSE)
fit1 = lmer(y~ group*x +(x|subject), data=dataset, REML=FALSE)
anova(fit0, fit1)
当我运行它时,它会生成:
Two Sample t-test
data: slopes by grp
t = -2.2495, df = 18, p-value = 0.03723
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.66690111 -0.02277686
sample estimates:
mean in group Control mean in group Treatment
1.362975 1.707814
和此:
Data: dataset
Models:
fit0: y ~ x + (x | subject)
fit1: y ~ group * x + (x | subject)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
fit0 6 326.65 342.28 -157.32 314.65
fit1 8 324.34 345.18 -154.17 308.34 6.3072 2 0.0427 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
基本上我想要做的是在代码中重复一下,这样当我点击运行时,它会生成这个但是我指定了很多次。然后我希望它将它生成的p值分为两组,一组p值高于0.05,另一组低于0.05
正如我所说,我对此很陌生,所以如果有人能够向我解释,那将非常感激。
答案 0 :(得分:4)
为简单起见,我从t.test
获取了p值,它可能不是你所说的p值。但是,它适合演示目的。
只需将代码包装在一个函数中,并根据需要多次使用replicate
:
do_once <- function()
{
x = 1:5
alpha = 1
beta = 1.5
betaD = 0.1
s = 1
sa = 0.2
sb = 0.2
N = 10
grp = factor(rep(c("Control", "Treatment"), c(N,N)))
for(i in 1:(2*N)) {
ai = rnorm(1, 0, sa)
bi = rnorm(1, 0, sb)
intercept = alpha+ai
slope = beta + bi + ifelse(grp[i]=="Treatment", betaD, 0.0)
y = intercept+ slope*x + rnorm(length(x), 0, s)
tmp = data.frame(subject=i, x=x, y=y, a=ai, b=bi, group=grp[i])
if(i==1) dataset = tmp
else dataset = rbind(dataset, tmp)
}
require(lme4)
fitAll= lmList(y~x|subject, data=dataset)
slopes = coef(fitAll)$x
boxplot(slopes~grp)
t.test(slopes~grp, var.equal=TRUE)$p.value
}
p_vals <- replicate(10, do_once())
要使p值低于0.05,只需
p_vals[p_vals < 0.05]
是的,这与Rstudio无关,R代码可以在任何IDE和普通R控制台中使用。
答案 1 :(得分:3)
要多次运行代码,请使用replicate
。像
replicate(
100,
{
# Your code that creates the random dataset and runs ANOVA
}
)