Re: [R] re peated measures
Hi,
Thank you.
It was that.
Julien.
Tal Galili wrote:
>
> check for missing values.
> Tal
>
>
>
> On Wed, Sep 23, 2009 at 3:27 PM, pompon wrote:
>
>>
>> Hi,
>>
>> I am performing a repeated measures 2-way ANOVA to assess the influence
>> of
>> plant and leaf on aphid fecundity. Fecundity is measured for each aphid
>> on
>> a
>> single leaf.
>> Here is what I typed.
>>
>> wingless <- reshape(Wingless,
>>varying =
>>
>> list(c("d0","d1","d2","d3","d4","d5","d6","d7","d8","d9","d10","d11","d12","d13","d14","d15","d16")),
>>v.names = c("fecundity"), timevar = "time",
>>direction = "long")
>>
>> wingless.aov <- aov(fecundity ~ factor(time) * clip.cage * plant +
>> Error(factor(id)), data = wingless)
>>
>> summary(wingless.aov)
>>
>> and I obtained
>>
>> Error: factor(id)
>> Df Sum Sq Mean Sq F value Pr(>F)
>> factor(time)4 56.789 14.197 3.0613 0.05925 .
>> clip.cage 1 14.149 14.149 3.0509 0.10621
>> plant 1 3.251 3.251 0.7010 0.41880
>> factor(time):clip.cage 1 0.304 0.304 0.0655 0.80240
>> clip.cage:plant 1 17.114 17.114 3.6903 0.07880 .
>> Residuals 12 55.652 4.638
>> ---
>> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>>
>> Error: Within
>> Df Sum Sq Mean Sq F value Pr(>F)
>> factor(time) 16 340.83 21.30 11.5222 < 2e-16 ***
>> factor(time):clip.cage16 27.341.71 0.9242 0.54195
>> factor(time):plant16 46.362.90 1.5673 0.07783 .
>> factor(time):clip.cage:plant 16 24.501.53 0.8281 0.65304
>> Residuals255 471.441.85
>> ---
>> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>>
>> I don't understand why I have the factor(time) inmy between subject
>> results,
>> whereas with a similar set of data I don't.
>>
>> Thank you very much,
>> Julien Pompon.
>> --
>> View this message in context:
>> http://www.nabble.com/repeated-measures-tp25531110p25531110.html
>> Sent from the R help mailing list archive at Nabble.com.
>>
>> __
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>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
>
> --
> --
>
>
> My contact information:
> Tal Galili
> Phone number: 972-50-3373767
> FaceBook: Tal Galili
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> http://www.talgalili.com
> http://www.biostatistics.co.il
>
> [[alternative HTML version deleted]]
>
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[R] re peated measures
Hi,
I am performing a repeated measures 2-way ANOVA to assess the influence of
plant and leaf on aphid fecundity. Fecundity is measured for each aphid on a
single leaf.
Here is what I typed.
wingless <- reshape(Wingless,
varying =
list(c("d0","d1","d2","d3","d4","d5","d6","d7","d8","d9","d10","d11","d12","d13","d14","d15","d16")),
v.names = c("fecundity"), timevar = "time",
direction = "long")
wingless.aov <- aov(fecundity ~ factor(time) * clip.cage * plant +
Error(factor(id)), data = wingless)
summary(wingless.aov)
and I obtained
Error: factor(id)
Df Sum Sq Mean Sq F value Pr(>F)
factor(time)4 56.789 14.197 3.0613 0.05925 .
clip.cage 1 14.149 14.149 3.0509 0.10621
plant 1 3.251 3.251 0.7010 0.41880
factor(time):clip.cage 1 0.304 0.304 0.0655 0.80240
clip.cage:plant 1 17.114 17.114 3.6903 0.07880 .
Residuals 12 55.652 4.638
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Error: Within
Df Sum Sq Mean Sq F value Pr(>F)
factor(time) 16 340.83 21.30 11.5222 < 2e-16 ***
factor(time):clip.cage16 27.341.71 0.9242 0.54195
factor(time):plant16 46.362.90 1.5673 0.07783 .
factor(time):clip.cage:plant 16 24.501.53 0.8281 0.65304
Residuals 255 471.441.85
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
I don't understand why I have the factor(time) inmy between subject results,
whereas with a similar set of data I don't.
Thank you very much,
Julien Pompon.
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Re: [R] anova(cph(..) output
Hi,
Thank you very much for the answer.
However, I have still some misunderstandings.
from the output, can we say that plant and leaf age are significant but not
their interaction?
And the last question I promise, what would you advise me to write in the
paper to explain the different method and ackonwledge for the df?
Thank you again,
julien.
Frank E Harrell Jr wrote:
>
> pompon wrote:
>> Hello,
>>
>> I am a beginner in R and statistics, so my question may be trivial. Sorry
>> in
>> advance.
>> I performed a Cox proportion hazard regression with 2 categorical
>> variables
>> with cph{design}. Then an anova on the results.
>> the output is
>>
>>> anova(cph(surv(survival, censor) ~ plant + leaf.age + plant*leaf.age,
>>> Mpnymph)
>>
>> Wald Statistics Response: Surv(survival,
>> censored)
>>
>> FactorChi-Square
>> d.f. P
>> plant (Factor+Higher Order Factors) 96.96 12 <.0001
>> All Interactions 10.58
>> 6 0.1022
>> leaf.age (Factor+Higher Order Factors) 29.11 7 0.0001
>> All Interactions 10.58
>> 6 0.1022
>> plant * leaf.age (Factor+Higher Order Factors) 10.58 6 0.1022
>> TOTAL 106.63 13 <.0001
>>
>> What do "All interaction" stand for?
>> The real df of for plant is 6 and 1 for leaf.age. Then, which chi square
>> is
>> one for my main factors anf their interaction.
>>
>> thank you,
>> Julien.
>
> Julien,
>
> I know what you mean when you say 'real df' but that's not the whole
> story as plant has 6 more df by interacting with a single df variable.
> There is no such thing as 'the' main effect test for plant. The 12 df
> test is unique and tests whether plant is associated with Y for any
> level of leaf.age.
>
> You can see exactly what is being tested by using various print options
> for anova.Design, as described in the help file. The "dots" option is
> easy on the eyes.
>
> Frank
> --
> Frank E Harrell Jr Professor and Chair School of Medicine
> Department of Biostatistics Vanderbilt University
>
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[R] anova(cph(..) output
Hello,
I am a beginner in R and statistics, so my question may be trivial. Sorry in
advance.
I performed a Cox proportion hazard regression with 2 categorical variables
with cph{design}. Then an anova on the results.
the output is
> anova(cph(surv(survival, censor) ~ plant + leaf.age + plant*leaf.age,
> Mpnymph)
Wald Statistics Response: Surv(survival, censored)
FactorChi-Square d.f. P
plant (Factor+Higher Order Factors) 96.96 12 <.0001
All Interactions 10.58
6 0.1022
leaf.age (Factor+Higher Order Factors) 29.11 7 0.0001
All Interactions 10.58
6 0.1022
plant * leaf.age (Factor+Higher Order Factors) 10.58 6 0.1022
TOTAL 106.63 13 <.0001
What do "All interaction" stand for?
The real df of for plant is 6 and 1 for leaf.age. Then, which chi square is
one for my main factors anf their interaction.
thank you,
Julien.
--
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