Hi,

I understand that to get the ordinary t statistic for independent two groups is using 

fit$coef / fit$stdev.unscaled / fit$sigma

I wonder how to get the ordinary t statistic for dependent groups? My simulation study tells me that the above code does not work because the result is different from the t.test(, paired = TRUE).

y = matrix(rnorm(2*10), nrow=2)
treat = factor(rep(c("T1","T2"),5))
subject_id = rep(c("A","B","C","D","E"), each = 2)
design <- model.matrix(~0+treat)
colnames(design) <- levels(treat)


design <- model.matrix(~subject_id+treat)
fit <- lmFit(y, design)
fit <- eBayes(fit)
topTable(fit, coef="treatT2") # p value from limma

       logFC    AveExpr          t   P.Value adj.P.Val         B
1  0.3365364 -0.5893109  0.3322012 0.7482740 0.9126306 -4.599436
2 -0.1147175 -0.5007840 -0.1132397 0.9126306 0.9126306 -4.599906

(1-pt(abs(fit$coef / fit$stdev.unscaled / fit$sigma),4))*2  # p value from ordinary t statistic

     (Intercept) subject_idB subject_idC subject_idD subject_idE   treatT2
[1,]   0.7485326   0.7275158   0.9835921   0.9614861   0.7787754 0.7892628
[2,]   0.7753628   0.2667974   0.3548992   0.4055479   0.7356786 0.8949580

t.test(y[1,]~treat, paired = TRUE) # p value from paired t test in R. The result does not match with the previous code.


t = -0.28574, df = 4, p-value = 0.7893