Input file format to Anova from car package
Marcelo Laia wrote:
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.
It's wrong because your use of Anova is inappropriate here. mod.ok should be an object of class "mlm" for this use of Anova. class(mod.ok) or str(mod.ok) would be useful. See below for further comments.
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
This works because you now have mod.ok as an "mlm" object.
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?
Let's call your first data.frame dat.long. Then you can use:
dat.wide <- reshape(dat, timevar="day",
idvar = c("Hormone", "Block", "Treatment", "Plant"),
direction = "wide")
Note that the two data frames you give are not consistent.
You could also investigate the reshape package which makes a
lot of reshaping easier.
-Peter Ehlers
Thank you very much!