So, you are looking for confidence intervals for each factor level?
You can use the predict() function to do that.
fit <- aov(values ~ ind, data=otestme)
newdat <- data.frame(ind=factor(levels(otestme$ind)))
cbind(newdat, predict(fit, newdata=newdat, interval="confidence"))
Jean
Anna Dunietz wrote on 08/23/2011 03:53:51 AM:
>
> Hi All!
>
> I am interested in testing whether the means for the data I am
investigating
> are equal to a specific value - let's say 0.01. I have already run a
> one-way ANOVA and know that the differences in the means are not
> significant, so now I want to know what values the means take on.
"otestme"
> is the data I am working with (it would be hard for me to get into a
form
> that would be easy for you to manipulate, so I have pasted it below -
values
> is numeric, while ind consists of factors(equities)). I have also
pasted
> the results of my ANOVA below, although I do not think you need to see
them
> in order to answer my question.
>
> I understand that I should have more observations per each equity, but I
do
> not want to overflow this e-mail with data. I have, therefore, taken a
> small sample of all my data. I hope I have provided enough information
in
> order for you to understand what I would like to do. I have researched
for
> a couple of hours regarding this problem but to no avail!
>
> Thanks!
> Anna
>
>
> > otestme values ind
> 1 0.001008012 AAPL.UW.Equity
> 2 0.015518087 AAPL.UW.Equity
> 3 0.013221459 AAPL.UW.Equity
> 4 0.012195734 AAPL.UW.Equity
> 5 -0.026750298 AAPL.UW.Equity
> 6 -0.001910487 AAPL.UW.Equity
> 7 -0.003419938 AAPL.UW.Equity
> 8 0.009316770 BHI.UN.Equity
> 9 -0.007564103 BHI.UN.Equity
> 10 -0.040175688 BHI.UN.Equity
> 11 -0.017900404 BHI.UN.Equity
> 12 -0.010278197 BHI.UN.Equity
> 13 -0.034339518 BHI.UN.Equity
> 14 -0.006739317 BHI.UN.Equity
> 15 -0.013637913 BMY.UN.Equity
> 16 -0.015900449 BMY.UN.Equity
> 17 0.004566210 BMY.UN.Equity
> 18 -0.002097902 BMY.UN.Equity
> 19 0.014716188 BMY.UN.Equity
> 20 0.006560773 BMY.UN.Equity
> 21 0.000343053 BMY.UN.Equity
> 22 0.010712869 COP.UN.Equity
> 23 -0.012823868 COP.UN.Equity
> 24 -0.000132556 COP.UN.Equity
> 25 -0.004242344 COP.UN.Equity
> 26 0.009319664 COP.UN.Equity
> 27 -0.007254980 COP.UN.Equity
> 28 -0.009433962 COP.UN.Equity
> 29 -0.014138817 DELL.UW.Equity
> 30 0.005867014 DELL.UW.Equity
> 31 -0.018146468 DELL.UW.Equity
> 32 -0.002640264 DELL.UW.Equity
> 33 -0.003970880 DELL.UW.Equity
> 34 0.005315615 DELL.UW.Equity
> 35 0.046265697 DELL.UW.Equity
> 36 -0.024477612 DELL.UW.Equity.1
> 37 -0.019583843 DELL.UW.Equity.1
> 38 -0.033083645 DELL.UW.Equity.1
> 39 -0.002582311 DELL.UW.Equity.1
> 40 0.003883495 DELL.UW.Equity.1
> 41 0.018697614 DELL.UW.Equity.1
> 42 -0.000632911 DELL.UW.Equity.1
> 43 0.028893058 FCX.UN.Equity
> 44 -0.000911743 FCX.UN.Equity
> 45 0.020076656 FCX.UN.Equity
> 46 0.005009841 FCX.UN.Equity
> 47 -0.022431903 FCX.UN.Equity
> 48 0.002185394 FCX.UN.Equity
> 49 -0.012538615 FCX.UN.Equity
> 50 0.015815224 FDX.UN.Equity
> 51 0.006496416 FDX.UN.Equity
> 52 -0.017471623 FDX.UN.Equity
> 53 0.007588628 FDX.UN.Equity
> 54 0.020571043 FDX.UN.Equity
> 55 -0.005617359 FDX.UN.Equity
> 56 0.030350022 FDX.UN.Equity
> 57 -0.004484455 GOOG.UW.Equity
> 58 0.012791206 GOOG.UW.Equity
> 59 -0.011949216 GOOG.UW.Equity
> 60 0.019551524 GOOG.UW.Equity
> 61 0.018517603 GOOG.UW.Equity
> 62 0.001213141 GOOG.UW.Equity
> 63 0.005622153 GOOG.UW.Equity
> 64 0.003272557 INTC.UW.Equity
> 65 0.021901212 INTC.UW.Equity
> 66 0.025079799 INTC.UW.Equity
> 67 0.007117438 INTC.UW.Equity
> 68 0.007950530 INTC.UW.Equity
> 69 0.016213848 INTC.UW.Equity
> 70 -0.012074170 INTC.UW.Equity
> 71 -0.012396694 MS.UN.Equity
> 72 -0.025104603 MS.UN.Equity
> 73 0.009442060 MS.UN.Equity
> 74 -0.041666667 MS.UN.Equity
> 75 -0.007985803 MS.UN.Equity
> 76 -0.004919499 MS.UN.Equity
> 77 0.001797753 MS.UN.Equity
> 78 0.010965209 NSC.UN.Equity
> 79 -0.000937333 NSC.UN.Equity
> 80 0.000536121 NSC.UN.Equity
> 81 -0.031346283 NSC.UN.Equity
> 82 -0.001244641 NSC.UN.Equity
> 83 0.010108003 NSC.UN.Equity
> 84 0.002604524 NSC.UN.Equity
> 85 -0.010257403 TGT.UN.Equity
> 86 -0.008799374 TGT.UN.Equity
> 87 0.004931939 TGT.UN.Equity
> 88 -0.002159403 TGT.UN.Equity
> 89 -0.000786937 TGT.UN.Equity
> 90 0.005906675 TGT.UN.Equity
> 91 -0.009786651 TGT.UN.Equity
> 92 -0.002091613 UNH.UN.Equity
> 93 -0.007545588 UNH.UN.Equity
> 94 0.018162619 UNH.UN.Equity
> 95 0.018460900 UNH.UN.Equity
> 96 0.002647658 UNH.UN.Equity
> 97 0.013203331 UNH.UN.Equity
> 98 -0.004009623 UNH.UN.Equity
> 99 0.009640957 WMB.UN.Equity
> 100 -0.016134343 WMB.UN.Equity
> 101 0.000669344 WMB.UN.Equity
> 102 -0.005685619 WMB.UN.Equity
> 103 0.017827111 WMB.UN.Equity
> 104 0.003304693 WMB.UN.Equity
> 105 -0.011198946 WMB.UN.Equity
>
>
>
> > aov(values~ind,data=otestme)Call:
> aov(formula = values ~ ind, data = otestme)
>
> Terms:
> ind Residuals
> Sum of Squares 0.004903384 0.018953011
> Deg. of Freedom 14 90
>
> Residual standard error: 0.01451169
> Estimated effects may be unbalanced>
> summary(aov(values~ind,data=otestme)) Df Sum Sq Mean
> Sq F value Pr(>F)
> ind 14 0.0049034 0.00035024 1.6632 0.07774 .
> Residuals 90 0.0189530 0.00021059
> ---
> Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
>
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