Dear R list:
I modelled changes in a variable (mconc) over time (d) for individuals
(replicate) given one of three treatments (treatment) using:
mconc.lme <- lme(mconc~treatment*poly(d,2), random=~poly(d,2)|replicate,
data=my.data)
summary(mconc.lme) shows that the linear coefficient of one of the
treatments is significantly different to zero, viz.
Value Std.Error DF t-value p-value
... ... ... ...
...
treatmentf:poly(d, 2)1 1.3058562 0.5072409 315 2.574430 0.0105
But anova(mconc.lme) gives a non-significant result for the treatment*time
interaction, viz.
numDF denDF F-value p-value
(Intercept) 1 315 159.17267 <.0001
treatment 2 39 0.51364 0.6023
poly(d, 2) 2 315 17.43810 <.0001
treatment:poly(d, 2) 4 315 2.01592 0.0920
Pinheiro & Bates (2000) only discusses anova() for single arguments briefly
on p.90.
I would like to know whether these results indicate that the significant
effect found in summary(mconc.lme) is spurious (perhaps due to
multiplicity).
Many thanks,
Dan Bebber
Department of Plant Sciences
University of Oxford
South Parks Road
Oxford OX1 3RB
UK
Tel. 01865 275000
Hi Dan,
check the `type' argument of `anova.lme()' which defaults to
"sequential". This is also discussed in Pinheiro and Bates but I
don't
have the book with me now to trace the page.
I hope it helps.
Best,
Dimitris
----
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/16/396887
Fax: +32/16/337015
Web: http://www.med.kuleuven.ac.be/biostat
http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm
----- Original Message -----
From: "Dan Bebber" <danbebber at forestecology.co.uk>
To: <r-help at stat.math.ethz.ch>
Sent: Wednesday, November 17, 2004 3:35 PM
Subject: [R] summary.lme() vs. anova.lme()
> Dear R list:
>
> I modelled changes in a variable (mconc) over time (d) for
> individuals
> (replicate) given one of three treatments (treatment) using:
> mconc.lme <- lme(mconc~treatment*poly(d,2),
> random=~poly(d,2)|replicate,
> data=my.data)
>
> summary(mconc.lme) shows that the linear coefficient of one of the
> treatments is significantly different to zero, viz.
> Value Std.Error DF t-value p-value
> ... ... ... ...
> ...
> treatmentf:poly(d, 2)1 1.3058562 0.5072409 315 2.574430 0.0105
>
> But anova(mconc.lme) gives a non-significant result for the
> treatment*time
> interaction, viz.
> numDF denDF F-value p-value
> (Intercept) 1 315 159.17267 <.0001
> treatment 2 39 0.51364 0.6023
> poly(d, 2) 2 315 17.43810 <.0001
> treatment:poly(d, 2) 4 315 2.01592 0.0920
>
> Pinheiro & Bates (2000) only discusses anova() for single arguments
> briefly
> on p.90.
> I would like to know whether these results indicate that the
> significant
> effect found in summary(mconc.lme) is spurious (perhaps due to
> multiplicity).
>
> Many thanks,
> Dan Bebber
>
> Department of Plant Sciences
> University of Oxford
> South Parks Road
> Oxford OX1 3RB
> UK
> Tel. 01865 275000
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>
Is there a way to calculate the number of months between dates?
StartDate <- strptime("01 March 1950", "%d %B %Y")
EventDates <- strptime(c("01 April 1955", "01 July
1980"), "%d %B %Y")
difftime(EventDates, StartDate)
So, there are 61 months between 01 March 1950 and 01 April 1955. There are
364 months between 01 March 1950 and 01 July 1980. What I want is for there
to be a "months" argument to units in difftime. Anybody have a bright
idea?
Is there a better way to approach this than difftime?
Thanks in advance, Andy
> version
_
platform i386-pc-mingw32
arch i386
os mingw32
system i386, mingw32
status
major 2
minor 0.0
year 2004
month 10
day 04
language R
On Wed, 17 Nov 2004, Dan Bebber wrote:> I modelled changes in a variable (mconc) over time (d) for individuals > (replicate) given one of three treatments (treatment) using: > mconc.lme <- lme(mconc~treatment*poly(d,2), random=~poly(d,2)|replicate, > data=my.data) > > summary(mconc.lme) shows that the linear coefficient of one of the > treatments is significantly different to zero, viz. > Value Std.Error DF t-value p-value > ... ... ... ... > ... > treatmentf:poly(d, 2)1 1.3058562 0.5072409 315 2.574430 0.0105 > > But anova(mconc.lme) gives a non-significant result for the treatment*time > interaction, viz. > numDF denDF F-value p-value > (Intercept) 1 315 159.17267 <.0001 > treatment 2 39 0.51364 0.6023 > poly(d, 2) 2 315 17.43810 <.0001 > treatment:poly(d, 2) 4 315 2.01592 0.0920 > > Pinheiro & Bates (2000) only discusses anova() for single arguments briefly > on p.90. > I would like to know whether these results indicate that the significant > effect found in summary(mconc.lme) is spurious (perhaps due to > multiplicity).Probably yes (but p values of 9% and 1% are not that different, and in both cases you are looking at a few p values). But since both summary.lme and anova.lme use Wald tests, I would use a LRT, using anova on two fits (and I would use ML fits to get a genuine LRT but that is perhaps being cautious). To Dimitris Rizopoulos: as this is the last term in the sequential anova, it is the correct Wald test. -- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595
Andy Bunn <abunn <at> whrc.org> writes:
:
: Is there a way to calculate the number of months between dates?
:
: StartDate <- strptime("01 March 1950", "%d %B %Y")
: EventDates <- strptime(c("01 April 1955", "01 July
1980"), "%d %B %Y")
: difftime(EventDates, StartDate)
:
: So, there are 61 months between 01 March 1950 and 01 April 1955. There are
: 364 months between 01 March 1950 and 01 July 1980. What I want is for there
: to be a "months" argument to units in difftime. Anybody have a
bright idea?
: Is there a better way to approach this than difftime?
1. There are an average of 365.25/12 days per month so the following
expression gives the number of months between d1 and d2:
# test data
d1 <- as.Date("01 March 1950", "%d %B %Y")
d2 <- as.Date(c("01 April 1955", "01 July 1980"),
"%d %B %Y")
# calculation
round((d2 - d1)/(365.25/12))
2. Another possibility is to get the length of seq.Dates like this:
as.Date.numeric <- function(x) structure(floor(x+.001), class =
"Date")
sapply(d2, function(d2) length(seq(d1, as.Date(d2), by =
"month")))-1