Subject var1 var2 var3 var4 var5
1 0.2 0.78 7.21 0.5 0.47
1 0.52 1.8 11.77 -0.27 -0.22
1 0.22 0.84 7.32 0.35 0.36
2 0.38 1.38 10.05 -0.25 -0.2
2 0.56 1.99 13.76 -0.44 -0.38
3 0.35 1.19 7.23 -0.16 -0.06
4 0.09 0.36 4.01 0.55 0.51
4 0.29 1.08 9.48 -0.57 -0.54
4 0.27 1.03 9.42 -0.19 -0.21
4 0.25 0.9 7.06 0.12 0.12
5 0.18 0.65 5.22 0.41 0.42
5 0.15 0.57 5.72 0.01 0.01
6 0.26 0.94 7.38 -0.17 -0.13
6 0.14 0.54 5.13 0.16 0.17
6 0.22 0.84 6.97 -0.66 -0.58
6 0.18 0.66 5.79 0.23 0.25
# the above is sample data matrix (dat11)
# The following lines of function is to calculate the p-value (P.z) for a
# variable pair var2 and var3 using lmer().
fit1 <- lmer(var2 ~ var3 + (1|Subject), data = dat11)
summary(fit1)
coefs <- data.frame(coef(summary(fit1)))
# use normal distribution to approximate p-value
coefs$p.z <- 2 * (1 - pnorm(abs(coefs$t.value)))
round(coefs,6)
# the following is the result
Estimate Std.Error t.value p.z
(Intercept) -0.280424 0.110277 -2.542913 0.010993
var3 0.163764 0.013189 12.417034 0.000000
实际数据包含65个变量(var1,var2 .... var65)。我想使用上述代码为所有可能的65个变量对找到上述结果,例如var1〜var2,var1〜var3,... var1〜var65; var2〜var3,var2〜var4,... var2〜var65; var3〜var4,...等等。大约有2000对。有人可以帮我提供循环代码并将结果保存到.csv文件吗?谢谢。