Displaying 7 results from an estimated 7 matches for "pompon".
2009 Sep 23
1
re peated measures
...1.44 1.85
---
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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2013 Apr 12
2
model frame and formula mismatch in model.matrix()
Hello everyone,
I am trying to fit the following model
All X. variables are continuous, while the conditions are categoricals.
model <- lm(X2
2006 Oct 25
1
Network card problem
Hi all,
I''m trying to install Xen on a dell E520 computer. I''ve a network problem
while rebooting on kernel vmlinuz-2.6.16.29-xen, all do work well, dom0
(ubuntu 6.06) start but I have no network at all.
When booting on ubuntu, the NIC which is a Intel Gigabit Ethernet card
drived by the e1000 modules does work correctly, but when the kernel xen is
booted, trying to use the
2009 May 15
1
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)
2003 Aug 12
0
excellently
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> pleasantness examination teared terminology exclusion boats alfred
> scoops exegesis tensed telecommunicate bonus hotbed theirs tattooed
> hot hotelman exposes horrid scheme evens
>
> [1][ra.gif]
> pompon expectancy householders crazes middles hypodermics hysterectomy
> sawfly pouted mealy bonanza alvin bond bonder melted poked evicts crow
> hospitalizing bellini aitken testability hostesses boundless
> microcomputer bombards illuminate boiler brass boggled melts
> accomplish...
2009 Sep 26
1
Multiple comparisons for coxph survival analysis model
Hello, all R-users!
I am working on fitting a survival analysis model using the coxph
function for Cox proportional hazards regression model. Data look like
usual:
==========================
group block death censor
Group1 1 4 1
Group1 1 12 1
...
Group2 30 4 1
Group2 30 4 1
...
Group3 57 16
2009 Dec 30
0
factorial analysis influenced by data skewness
Hello
I run a factorial analysis on 20 variables describing the behaviour of
insects on 9 different resistant plants. I then biologically interpreted
each of the 5 factors obtained, with respect to the variables with the
highest loading value for each factor. And I run a GLM on each factor with
plant as independent variable. Eventually, I inferred the resistance
mechanism by comparing (through