Dear list member,
My question is related to input file format to an Anova from car package.
Here is an example of what I did:
My file format is like this (and I dislike the idea that I will need
to recode it):
Hormone day Block Treatment Plant Diameter High N.Leaves
SH 23 1 1 1 3.19 25.3 2
SH 23 1 1 2 3.42 5.5 1
SH 23 1 2 1 2.19 5.2 2
SH 23 1 2 2 2.17 7.6 2
CH 23 1 1 1 3.64 6.5 2
CH 23 1 1 2 2.8 3.7 2
CH 23 1 2 1 3.28 4 2
CH 23 1 2 2 2.82 5.2 2
SH 23 2 1 1 2.87 6.4 2
SH 23 2 1 2 2.8 6 2
SH 23 2 2 1 2.02 4.5 2
SH 23 2 2 2 3.15 5.5 2
CH 23 2 1 1 3.22 2.3 2
CH 23 2 1 2 2.45 3.8 2
CH 23 2 2 1 1.85 3.5 2
CH 23 2 2 2 3.13 4.4 2
CH 39 1 1 1 2.64 6 2
CH 39 1 1 2 4.33 10 2
CH 39 1 2 1 3.74 9 2
CH 39 1 2 2 3.23 8 2
SH 39 1 1 1 3.8 8 2
SH 39 1 1 2 2.35 9 2
SH 39 1 2 1 3.66 6 2
SH 39 1 2 2 3.92 7 2
CH 39 2 1 1 3.28 7 2
CH 39 2 1 2 4.99 7 2
CH 39 2 2 1 2.49 6 2
CH 39 2 2 2 4.75 7 2
SH 39 2 1 1 3.35 5 2
SH 39 2 1 2 4.38 7 2
SH 39 2 2 1 5.11 9 2
SH 39 2 2 2 2.71 5 2
idata <- data.frame(Idade=factor(c(23,39)))
a = read.table("clipboard", sep=" ", head=T)
mod.ok <- lm(Diameter ~ Treatment*Hormone, data=a)
av.ok <- Anova(mod.ok, idata=idata, idesign=~as.factor(day))
summary(av.ok)
Sum Sq Df F value Pr(>F)
Min. : 0.02153 Min. : 1.00 Min. :0.02828 Min. :0.5105
1st Qu.: 0.06169 1st Qu.: 1.00 1st Qu.:0.06346 1st Qu.:0.6331
Median : 0.20667 Median : 1.00 Median :0.09863 Median :0.7558
Mean : 5.43711 Mean : 7.75 Mean :0.19043 Mean :0.7113
3rd Qu.: 5.58208 3rd Qu.: 7.75 3rd Qu.:0.27150 3rd Qu.:0.8117
Max. :21.31356 Max. :28.00 Max. :0.44437 Max. :0.8677
NA's :1.00000 NA's :1.0000
This result is wrong, I believe.
Here, is a file format with repeated measures side-by-side:
Hormone Block Treatment Plant Diameter.23 Diameter.39 High.23 High.39
N.Leaves.23 N.Leaves.39
SH 1 1 1 3.19 2.64 25.3 6 2 2
SH 1 1 2 3.42 4.33 5.5 10 1 2
SH 1 2 1 2.19 3.74 5.2 9 2 2
SH 1 2 2 2.17 3.23 7.6 8 2 2
CH 1 1 1 3.64 3.8 6.5 8 2 2
CH 1 1 2 2.8 2.35 3.7 9 2 2
CH 1 2 1 3.28 3.66 4 6 2 2
CH 1 2 2 2.82 3.92 5.2 7 2 2
SH 2 1 1 2.87 3.28 6.4 7 2 2
SH 2 1 2 2.8 4.99 6 7 2 2
SH 2 2 1 2.02 2.49 4.5 6 2 2
SH 2 2 2 3.15 4.75 5.5 7 2 2
CH 2 1 1 3.22 3.35 2.3 5 2 2
CH 2 1 2 2.45 4.38 3.8 7 2 2
CH 2 2 1 1.85 5.11 3.5 9 2 2
CH 2 2 2 3.13 2.71 4.4 5 2 2
idata <- data.frame(day=factor(c(23,39)))
a = read.table("clipboard", sep=" ", head=T)
mod.ok <- lm(cbind(Diameter.23,Diameter.39) ~ Treatment*Hormone, data=a)
av.ok <- Anova(mod.ok, idata=idata, idesign= ~ as.factor(day))
summary(av.ok)
Type II Repeated Measures MANOVA Tests:
------------------------------------------
Term: Treatment
Response transformation matrix:
(Intercept)
Diameter.23 1
Diameter.39 1
Sum of squares and products for the hypothesis:
(Intercept)
(Intercept) 0.6765062
Sum of squares and products for error:
(Intercept)
(Intercept) 13.05917
Multivariate Tests: Treatment
Df test stat approx F num Df den Df Pr(>F)
Pillai 1 0.0492517 0.6216377 1 12 0.44574
Wilks 1 0.9507483 0.6216377 1 12 0.44574
Hotelling-Lawley 1 0.0518031 0.6216377 1 12 0.44574
Roy 1 0.0518031 0.6216377 1 12 0.44574
------------------------------------------
Term: Hormone
Response transformation matrix:
(Intercept)
Diameter.23 1
Diameter.39 1
Sum of squares and products for the hypothesis:
(Intercept)
(Intercept) 0.09150625
Sum of squares and products for error:
(Intercept)
(Intercept) 13.05917
Multivariate Tests: Hormone
Df test stat approx F num Df den Df Pr(>F)
Pillai 1 0.0069583 0.08408456 1 12 0.77679
Wilks 1 0.9930417 0.08408456 1 12 0.77679
Hotelling-Lawley 1 0.0070070 0.08408456 1 12 0.77679
Roy 1 0.0070070 0.08408456 1 12 0.77679
------------------------------------------
Term: Treatment:Hormone
Response transformation matrix:
(Intercept)
Diameter.23 1
Diameter.39 1
Sum of squares and products for the hypothesis:
(Intercept)
(Intercept) 1.139556
Sum of squares and products for error:
(Intercept)
(Intercept) 13.05917
Multivariate Tests: Treatment:Hormone
Df test stat approx F num Df den Df Pr(>F)
Pillai 1 0.0802576 1.047132 1 12 0.32636
Wilks 1 0.9197424 1.047132 1 12 0.32636
Hotelling-Lawley 1 0.0872610 1.047132 1 12 0.32636
Roy 1 0.0872610 1.047132 1 12 0.32636
------------------------------------------
Term: as.factor(day)
Response transformation matrix:
as.factor(day)1
Diameter.23 1
Diameter.39 -1
Sum of squares and products for the hypothesis:
as.factor(day)1
as.factor(day)1 11.78206
Sum of squares and products for error:
as.factor(day)1
as.factor(day)1 15.41527
Multivariate Tests: as.factor(day)
Df test stat approx F num Df den Df Pr(>F)
Pillai 1 0.4332063 9.171726 1 12 0.010496 *
Wilks 1 0.5667937 9.171726 1 12 0.010496 *
Hotelling-Lawley 1 0.7643105 9.171726 1 12 0.010496 *
Roy 1 0.7643105 9.171726 1 12 0.010496 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------
Term: Treatment:as.factor(day)
Response transformation matrix:
as.factor(day)1
Diameter.23 1
Diameter.39 -1
Sum of squares and products for the hypothesis:
as.factor(day)1
as.factor(day)1 1.139556
Sum of squares and products for error:
as.factor(day)1
as.factor(day)1 15.41527
Multivariate Tests: Treatment:as.factor(day)
Df test stat approx F num Df den Df Pr(>F)
Pillai 1 0.0688353 0.887086 1 12 0.36484
Wilks 1 0.9311647 0.887086 1 12 0.36484
Hotelling-Lawley 1 0.0739238 0.887086 1 12 0.36484
Roy 1 0.0739238 0.887086 1 12 0.36484
------------------------------------------
Term: Hormone:as.factor(day)
Response transformation matrix:
as.factor(day)1
Diameter.23 1
Diameter.39 -1
Sum of squares and products for the hypothesis:
as.factor(day)1
as.factor(day)1 0.1501563
Sum of squares and products for error:
as.factor(day)1
as.factor(day)1 15.41527
Multivariate Tests: Hormone:as.factor(day)
Df test stat approx F num Df den Df Pr(>F)
Pillai 1 0.0096468 0.1168889 1 12 0.73835
Wilks 1 0.9903532 0.1168889 1 12 0.73835
Hotelling-Lawley 1 0.0097407 0.1168889 1 12 0.73835
Roy 1 0.0097407 0.1168889 1 12 0.73835
------------------------------------------
Term: Treatment:Hormone:as.factor(day)
Response transformation matrix:
as.factor(day)1
Diameter.23 1
Diameter.39 -1
Sum of squares and products for the hypothesis:
as.factor(day)1
as.factor(day)1 0.04305625
Sum of squares and products for error:
as.factor(day)1
as.factor(day)1 15.41527
Multivariate Tests: Treatment:Hormone:as.factor(day)
Df test stat approx F num Df den Df Pr(>F)
Pillai 1 0.0027853 0.03351708 1 12 0.8578
Wilks 1 0.9972147 0.03351708 1 12 0.8578
Hotelling-Lawley 1 0.0027931 0.03351708 1 12 0.8578
Roy 1 0.0027931 0.03351708 1 12 0.8578
Univariate Type II Repeated-Measures ANOVA Assuming Sphericity
SS num Df Error SS den Df F Pr(>F)
Treatment 0.3383 1 6.5296 12 0.6216 0.44574
Hormone 0.0458 1 6.5296 12 0.0841 0.77679
Treatment:Hormone 0.5698 1 6.5296 12 1.0471 0.32636
as.factor(day) 5.8910 1 7.7076 12 9.1717 0.01050 *
Treatment:as.factor(day) 0.5698 1 7.7076 12 0.8871 0.36484
Hormone:as.factor(day) 0.0751 1 7.7076 12 0.1169 0.73835
Treatment:Hormone:as.factor(day) 0.0215 1 7.7076 12 0.0335 0.85779
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
How I could use Anova from the first file format? If not, could you
suggest me a way to recode my data file in R?
I ask because I don't know how I can recode my data file on R. Is ti possible?
Thank you very much!
--
Marcelo Luiz de Laia
Universidade do Estado de Santa Catarina
UDESC - www.cav.udesc.br
Lages - SC - Brazil
Linux user number 487797
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