Displaying 5 results from an estimated 5 matches for "treatb".
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2011 Jul 29
2
Multifactor boxplots
Dear All
I would like to produce interaction boxplots and this seems to work:
par(mfrow=c(2,2))
A=sample(rnorm(50,50,10))
B=sample(rnorm(50,100,10))
Test=merge(A,B,by=0)#by=0 where 0 is the row.names
TreatA=(gl(2,50,100,labels=c("High","Low")))
TreatB=rep(gl(2,25,50,labels=c("High","Low")),2)
Newdata=data.frame(TreatA,TreatB,Test)
bwplot(x~TreatA:TreatB,data=Newdata)
However, I would prefer the X axis labels to be different, such that there are two label rows (TreatA and TreatB) something like this:
TreatA High H...
2010 Sep 15
0
Computing effect sizes based on mixed models
...ts for the outcome (dv), thereby allowing for individual intercepts and slopes:
lmer(dv ~ dv_base+treat*time+(1+time|subject))
Fixed effects:
Estimate Std. Error DF t value
(Intercept) -1.080041 0.126665 58 -8.527
dv_base -0.888656 0.090617 53 -9.807
treatB 0.645455 0.190541 53 3.387
time -0.001726 0.163044 58 -0.011
treatB:time 0.377888 0.271972 58 1.389
I'm interested now in comparing estimated treatment means for let's say the last time point and I've centered 'time' accordingly....
2006 Jun 15
1
Repost: Estimation when interaction is present: How do I get get the parameters from nlme?
...change upper and ed50. We want
to know if treatment B blocks the effect of treatment A and if so to
what degree.
This is similar to the Ludbrook example in Venables and Ripley, however
they only had one treatment and I have two.
my approach
The dataframe is structured like this:
expt treatA treatB dose force.
1 - - 0.1 20
1 - - 0.2 40
...
4 + + 0.1 20
4 +
I used a groupedData object: mydata=groupedData(force ~ dose | expt)
I used an nlme obect to model the data as follows (pseudocode):
myfit.nlme <- nlme(force ~ ss_tpl(dose, upper, ed50,slope),
fixed=list(ed50~factor(treat...
2007 Feb 26
0
LD50 contrasts with lmer/lme4
...at]*gr$logdose+
animal$da[gr$animal] + rnorm(nrow(gr),0,2) >0)
gr.lmer = lmer(resp ~ treat*logdose+(1|animal),data=gr,family=binomial)
summary(gr.lmer)
------- Output
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.9553 0.3074 3.11 0.0019 **
treatB 0.8793 0.3313 2.65 0.0079 **
treatC 0.5516 0.3077 1.79 0.0730 .
logdose 0.3733 0.0774 4.82 1.4e-06 ***
treatB:logdose 0.3081 0.1323 2.33 0.0198 *
treatC:logdose 0.2666 0.1249 2.13 0.0328 *
----- Goal
Value...
2006 Jun 09
0
interaction terms in regression analysis
...is done for each combination of two factors (treatmentA and Treatment
B) each having two levels (- and +). Each set of measurements is
obtained on a muscle from a different animal (i.e. each dose response
curve represents an independent experiment).
The data are stored as follows:
expt treatA treatB dose force
I use a groupedData object mydata=groupedData(force ~ dose | expt)
I used an nlme obect to model the data as follows (pseudocode):
myfit <- nlme(force ~ ssThreeParLogistic(dose, upper, ed50,slope),
fixed=list(ed50~factor(treatmentA)*factor(treatmentC)))
The ThreeParLogistic is a...