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July 20, 2018 19:30
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t-statistic
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# Write function | |
t.test2 <- function(m1,m2,s1,s2,n1,n2,m0=0,equal.variance=FALSE) | |
{ | |
if( equal.variance==FALSE ) | |
{ | |
se <- sqrt( (s1^2/n1) + (s2^2/n2) ) | |
# welch-satterthwaite df | |
df <- ( (s1^2/n1 + s2^2/n2)^2 )/( (s1^2/n1)^2/(n1-1) + (s2^2/n2)^2/(n2-1) ) | |
} else | |
{ | |
# pooled standard deviation, scaled by the sample sizes | |
se <- sqrt( (1/n1 + 1/n2) * ((n1-1)*s1^2 + (n2-1)*s2^2)/(n1+n2-2) ) | |
df <- n1+n2-2 | |
} | |
t <- (m1-m2-m0)/se | |
dat <- c(m1-m2, se, t, 2*pt(-abs(t),df)) | |
names(dat) <- c("Difference of means", "Std Error", "t", "p-value") | |
return(dat) | |
} | |
# Calculate t-statistic and p-value | |
t.test2(m1=37.33, m2=39.65, s1=13.40, s2=15.91, n1=23,n2=32, | |
equal.variance = FALSE) |
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