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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