Displaying 20 results from an estimated 1000 matches similar to: "Type III sum of squares and appropriate contrasts"
2008 Sep 26
1
Type I and Type III SS in anova
Hi all,
I have been trying to calculate Type III SS in R for an unbalanced two-way
anova. However, the Type III SS are lower for the first factor compared to
type I but higher for the second factor (see below). I have the impression
that Type III are always lower than Type I - is that right?
And a clarification about how to fit Type III SS. Fitting model<-aov(y~a*b)
in the base package and
2009 Mar 02
2
Unrealistic dispersion parameter for quasibinomial
I am running a binomial glm with response variable the no of mites of two
species y->cbind(mitea,miteb) against two continuous variables (temperature
and predatory mites) - see below. My model shows overdispersion as the
residual deviance is 48.81 on 5 degrees of freedom. If I use quasibinomial
to account for overdispersion the dispersion parameter estimate is 2501139,
which seems
2009 Mar 09
1
lme anova() and model simplification
I am running an lme model with the main effects of four fixed variables (3
continuous and one categorical – see below) and one random variable. The
data describe the densities of a mite species – awsm – in relation to four
variables: adh31 (temperature related), apsm (another plant feeding mite)
awpm (a predatory mite), and orien (sampling location within plant – north
or south).
I have read
2001 Sep 08
1
t.test (PR#1086)
Full_Name: Menelaos Stavrinides
Version: 1.3. 1
OS: Windows 98
Submission from: (NULL) (193.129.76.90)
When model simplification is used in glm (binomial errors) and anova is used two
compare two competitive models one can use either an "F" or a "Chi" test.
R always performs an F test (Although when test="Chi" the test is labeled as
Chi, there isn't any
2011 Jul 25
2
Wide confidence intervals or Error message in a mixed effects model (nlme)
I am analyzing a dataset on the effects of six pesticides on population
growth rate of a predatory mite. The response variable is the population
growth rate of the mite (ranges from negative to positive) and the
exploratory variable is a categorical variable (treatment). The
experiment was blocked in time (3 blocks / replicates per block) and it
is unbalanced - at least 1 replicate per block. I am
2011 May 21
2
unbalanced anova with subsampling (Type III SS)
Hello R-users,
I am trying to obtain Type III SS for an ANOVA with subsampling. My design
is slightly unbalanced with either 3 or 4 subsamples per replicate.
The basic aov model would be:
fit <- aov(y~x+Error(subsample))
But this gives Type I SS and not Type III.
But, using the drop() option:
drop1(fit, test="F")
I get an error message:
"Error in
2009 Dec 15
1
Type III sum of square in ANOVA
Dear all,
Does some body have idea on how to extract Type III sum of Square from "lm" or "aov" function in R ? I could not figure out it.
If this is minor and irrelevant to post in this mail, I am sorry for that.
Thanks.
Sincerely,
Ram Kumar Basnet
Wageningen University,
Netherlands
[[alternative HTML version deleted]]
2010 Mar 01
5
Type-I v/s Type-III Sum-Of-Squares in ANOVA
Hello,
I believe the aov() function in R uses a "Type-I sum-of-squares" by
default as against "Type-III".
This is relevant for me because I am trying to understand ANOVA in R using
my knowledge of ANOVA in SPSS. I can only reproduce the results of an ANOVA
done using R through SPSS if I specify that SPSS uses a Type-I
sum-of-squares. (And yes, I know that when the sample
2002 Mar 01
4
Type III Sum of Squares
Hi,
When doing a two-ways anova in R and comparing my same results with an SPSS
output, I noticed that R calculated type I Sum of Squares. Is it possible to
use Type III Sum of Squares?
Thanks,
S?bastien Plante
Institut des Sciences de la Mer de Rimouski (ISMER)
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r-help mailing list -- Read
2006 Aug 26
5
Type II and III sum of square in Anova (R, car package)
Hello everybody,
I have some questions on ANOVA in general and on ANOVA in R particularly.
I am not Statistician, therefore I would be very appreciated if you answer
it in a simple way.
1. First of all, more general question. Standard anova() function for lm()
or aov() models in R implements Type I sum of squares (sequential), which
is not well suited for unbalanced ANOVA. Therefore it is better
2004 Aug 11
2
type III sum of squares
R-help
What are the strengths and weakness of 'aov' in 'car' package?
My model looks something like this :
library(car)
aov(lm(fish.length~zone*area,data=my.data))
Thank you
Luis Ridao Cruz
Fiskiranns??knarstovan
N??at??n 1
P.O. Box 3051
FR-110 T??rshavn
Faroe Islands
Phone: +298 353900
Phone(direct): +298 353912
Mobile: +298 580800
Fax:
2008 Sep 14
3
Nonlinear regression question&In-Reply-To=6rya22mljx.fsf@franz.stat.wisc.edu
I was unable to open this file Bill Venables' excellent "Exegeses on
Linear Models" posted at
http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.ps.gz I'd be very
interested in reading it?
Thanks
Esther Meenken
Biometrician
Crop & Food Research
Private Bag 4704
Christchurch
TEL: (03) 325 9639
FAX: (03) 325 2074
EMAIL:MeenkenE at crop.cri.nz
Visit our website at
2005 Nov 24
2
type III sums of squares in R
Hi everyone,
Can someone explain me how to calculate SAS type III sums of squares in
R? Not that I would like to use them, I know they are problematic. I
would like to know how to calculate them in order to demonstrate that
strange things happen when you use them (for a course for example). I
know you can use drop1(lm(), test="F") but for an lm(y~A+B+A:B), type
III SSQs are only
2000 Aug 12
1
Nonlinear regression question
Dear R users
I recently migrated from Statistica/SigmaPlot (Windows) to R (Linux), so
please excuse if this may sound 'basic'.
When running a nonlinear regression (V = Vmax * conc / (Ks + conc), i.e.
Michaelis-Menten) on SigmaPlot, I get the output listed below:
>>>Begin SigmaPlot Output<<<
R = 0.94860969 Rsqr = 0.89986035 Adj Rsqr = 0.89458984
Standard Error of
2009 Oct 27
0
anova interaction contrasts: crossing helmert and linear contrasts
I am new to statistics, R, and this list, so apologies in advance for
the errors etiquette I am certain to make (in spite of reading the
posting guide, help on
various commands, etc.). ?Any help is greatly appreciated.
Here is my data:
fatigue = c(3,2,2,3,2,3,4,3,2,4,5,3,3,2,4,5,4,5,5,6,4,6,9,8,4,3,5,5,6,6,6,7,9,10,12,9)
n <- 3
train <- gl(3, 4*n, labels=c("6wks",
2004 Mar 03
1
Confusion about coxph and Helmert contrasts
Hi,
perhaps this is a stupid question, but i need some help about
Helmert contrasts in the Cox model.
I have a survival data frame with an unordered factor `group'
with levels 0 ... 5.
Calculating the Cox model with Helmert contrasts, i expected that
the first coefficient would be the same as if i had used treatment
contrasts, but this is not true.
I this a error in reasoning, or is it
2006 Sep 29
1
Helmert contrasts for repeated measures and split-plot expts
Dear R-help
I have two separate experiments, one a repeated-measures design, the other
a split-plot. In a standard ANOVA I have usually undertaken a
multiple-comparison test on a significant factor with e.g TukeyHSD, but as
I understand it such a test is inappropriate for repeated measures or
split-plot designs.
Is it therefore sensible to use Helmert contrasts for either of these
designs?
2011 Jul 20
0
Analysis of unbalanced data in nlme or car
I am analyzing a dataset on the effects of six pesticides on population
growth rate of a predatory mite. The response variable is the population
growth rate of the mite expressed as ln(Nfinal/Nstarting) of the mite,
where N final the population of the mite at the end of the experiment
and N starting the population of the mite at the beginning of the
experiment. Each of the six treatments was ran
2005 Apr 13
2
multinom and contrasts
Hi,
I found that using different contrasts (e.g.
contr.helmert vs. contr.treatment) will generate
different fitted probabilities from multinomial
logistic regression using multinom(); while the fitted
probabilities from binary logistic regression seem to
be the same. Why is that? and for multinomial logisitc
regression, what contrast should be used? I guess it's
helmert?
here is an example
2001 Jun 15
1
contrasts in lm and lme
I am using RW 1.2.3. on an IBM PC 300GL.
Using the data bp.dat which accompanies
Helen Brown and Robin Prescott
1999 Applied Mixed Models in Medicine. Statistics in Practice.
John Wiley & Sons, Inc., New York, NY, USA
which is also found at www.med.ed.ac.uk/phs/mixed. The data file was opened
and initialized with
> dat <- read.table("bp.dat")
>