[R] Questions of t.test {stats}

[pcscan]

We are currently using the t-test in Package stats,

t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"),
       mu = 0, paired = FALSE, var.equal = FALSE,
       conf.level = 0.95, ...)

but have some troubles :
1. why does the t-test take so a long time to perform a single test on a row
of a data.frame ? Is there any alternative function to perform t-test on all
the rows of a data.frame ?
2. We got different results on the following data with the argument
var.equal  setting as TRUE and FALSE respectively.
We are curious why the  "Welch Two Sample t-test" couldn't distinguish these
two vectors well.

Any help is greatly appreciated.


Sincerely. Liu Yu Ting

============================================================================
===============

x <-
c(-0.299611385,-0.164028986,-0.225545128,-0.244473171,-0.276619985,-0.276362
81,-0.289633015,-0.298994167,-0.27908886,-0.265612916,-0.262321082,-0.295753
768,-0.235677803,-0.283872306,-0.282174954,-0.241592817,-0.274716893,-0.2886
55752,-0.262166777,-0.263298345,-0.252239841,-0.298274078,-0.28958158,-0.187
174691,-0.26628157,-0.252034102,-0.248793703,-0.267207398,-0.289838754,-0.28
4283785,-0.118097619,-0.27898599,-0.265818655,-0.295085114,-0.246839177,-0.2
76105636,-0.293336328,-0.294210721,-0.259543597,0.18181929,-0.276311375,-0.2
48948008,-0.212583533,-0.247147786,-0.269573403,-0.27636281,-0.295445158,-0.
281146256,-0.27636281,-0.255840285,-0.292513369,-0.21664689,-0.228014003,-0.
238249548,-0.238300983,-0.238506723,-0.242004296,-0.213869405,-0.272916672,-
0.293233458,-0.239483986,-0.147672687,-0.289941624,-0.233774712,-0.237940939
,-0.276517115,-0.22431069,-0.217469848,0.461573717,-0.218858591,-0.280271863
,-0.290867452,-0.177144886,-0.179150847,-0.258463465,-0.269470533,-0.2482279
19,-0.221327466,-0.217418413,-0.290044494,-0.290610278,-0.260006512,-0.22261
3338,-0.275951331,0.015118775,0.116959879,-0.24509039,-0.092894518,0.5274618
26)

y <-
c(0.963784092,-0.266641614,4.623274441,1.6857758,-0.251159709,-0.090631382,1
.380355357,-0.117840445,7.213998979,3.404935937,1.444648983)

> t.test(x,y)

        Welch Two Sample t-test

data:  x and y
t = -2.8369, df = 10.009, p-value = 0.01763
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.6338688 -0.4369566
sample estimates:
 mean of x  mean of y
-0.2180945  1.8173182

> t.test(x,y,var.equal=TRUE)

        Two Sample t-test

data:  x and y
t = -8.2473, df = 98, p-value = 7.507e-13
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -2.525175 -1.545650
sample estimates:
 mean of x  mean of y
-0.2180945  1.8173182