Displaying 20 results from an estimated 31 matches for "treatment2".
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treatment
2010 Oct 28
1
xyplot and panel.curve
...el curve but I have run into error messages.
I want an 3 panel conditioned plot of 2 curves of Treatment 2 in each panel
conditioned by Treatment1, the example curve expression is x+value*x^2
A rough toy example to give an idea of what I want is:
Data:
data = expand.grid(Treatment1 = LETTERS[1:3],Treatment2 = letters[1:2])
data$value =seq(1.1,1.6,0.1)
data
Treatment1 Treatment2 value
1 A a 1.1
2 B a 1.2
3 C a 1.3
4 A b 1.4
5 B b 1.5
6 C b 1.6
xyplot(value|Treatment1, data = data,...
2011 May 26
1
dataframe - column value calculation in R
...E$2)*(E6-$E$2)+(F6-$F$2)*(F6-$F$2))
Treatment
1
2
27
3
5
=SQRT((D7-$D$3)*(D7-$D$3)+(E7-$E$3)*(E7-$E$3)+(F7-$F$3)*(F7-$F$3))
Treatment
2
1
29
2
2
=SQRT((D8-$D$4)*(D8-$D$4)+(E8-$E$4)*(E8-$E$4)+(F8-$F$4)*(F8-$F$4))
Treatment
2
2
30
3
2
=SQRT((D9-$D$5)*(D9-$D$5)+(E9-$E$5)*(E9-$E$5)+(F9-$F$5)*(F9-$F$5))
Treatment2
1
1
32
2
3
=SQRT((D10-$D$2)*(D10-$D$2)+(E10-$E$2)*(E10-$E$2)+(F10-$F$2)*(F10-$F$2))
Treatment2
1
2
35
1
3
=SQRT((D11-$D$3)*(D11-$D$3)+(E11-$E$3)*(E11-$E$3)+(F11-$F$3)*(F11-$F$3))
Treatment2
2
1
34
2
3
=SQRT((D12-$D$4)*(D12-$D$4)+(E12-$E$4)*(E12-$E$4)+(F12-$F$4)*(F12-$F$4))
Treatment2
2
2
28
2
1
=SQ...
2008 Apr 04
1
lme4: How to specify nested factors, meaning of : and %in%
...36.0843 6.0070
Treatment (Intercept) 4.7039 2.1689
Residual 21.1678 4.6008
number of obs: 36, groups: Liver:(Rat:Treatment), 18; Rat:Treatment, 6;
Treatment, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 140.500 5.184 27.104
Treatment2 10.500 7.331 1.432
Treatment3 -5.333 7.331 -0.728
Correlation of Fixed Effects:
(Intr) Trtmn2
Treatment2 -0.707
Treatment3 -0.707 0.500
> (m1a<-lmer(Glycogen~Treatment+(1|Treatment)+(1|Treatment:Rat)+(1|
Treatment:Rat:Liver)))
Linear mixed-effects model fit b...
2002 Sep 11
0
Contrasts with interactions
...(1,1,1,1,1,1,-8/3,1,-8/3,1,-8/3)
summary.lm(model);
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.30411 0.22355 5.834 2.11e-08 ***
dryweight 0.05445 0.02556 2.130 0.034358 *
treatment1 -0.01390 0.10283 -0.135 0.892575
treatment2 -0.25015 0.50841 -0.492 0.623230
treatment3 -0.65174 0.50580 -1.289 0.199026
treatment4 0.35105 0.42871 0.819 0.413838
treatment5 -0.15977 0.46738 -0.342 0.732827
treatment6 -0.06789 0.85831 -0.079 0.937036
treat...
2008 Feb 03
1
Effect size of comparison of two levels of a factor in multiple linear regression
...tcome[treatment==0])
[1] 3.984774
cohens.d(outcome[treatment==2], outcome[treatment==0])
[1] 6.167798
# Sometimes standardized regression coefficients are recommended
# for determining effect size but that clearly doesn't work here:
coef(lm(scale(outcome) ~ treatment))
(Intercept) treatment1 treatment2
-1.233366 1.453152 2.246946
# The reason it doesn't work is that the difference of outcome
# means is divided by the sd of *all* outcomes:
(mean(outcome[treatment==1])-mean(outcome[treatment==0]))/sd(outcome)
[1] 1.453152
(mean(outcome[treatment==2])-mean(outcome[treatment==0]))/sd(outc...
2005 Oct 26
1
Post Hoc Groupings
...e.g., if I have treatments 1, 2,
and 3, with 1 and 2 being statistically the same and 3 being different
from both
Group Treatment
A 1
A 2
B 3
2) I've been stumbling over the proper syntax for simple effects for a
tukeyHSD test. Is it
TukeyHSD(model.aov, "Treatment1", "Treatment2")
or
TukeyHSD(model, c("Treatment1", "Treatment2"))
or something else, as neither of those seem to really work.
2011 Apr 13
0
ordinal predictor in anova
...quot;AB"), each = 10))
length <- c(75, 67, 70, 75, 65, 71, 67, 67, 76, 68,
57, 58, 60, 59, 62, 60, 60, 57, 59, 61,
58, 61, 56, 58, 57, 56, 61, 60, 57, 58,
58, 59, 58, 61, 57, 56, 58, 57, 57, 59,
62, 66, 65, 63, 64, 62, 65, 65, 62, 67)
treatment2 <- c("BA", "BA", "BB", "BB", "BC", "BC", "BD", "BD", "BE", "BE",
"BA", "BA", "BB", "BB", "BC", "BC", "BD", "...
2010 May 18
1
proportion of treatment effect by a surrogate (fitting multivariate survival model)
...S-plus.
Is this the way to fit such a model in R?
Suppose I have variables: time, delta, treatment, and surrogate.
Should I repeat the dataset (2x) and stack, creating the variables:
time1 (time repeated 2x), delta1 (delta repeated 2x), treatment1 (same
as treatment, but 0's for the 2nd set), treatment2 (0's in first set,
then same as treatment), and surrogate2 (0's in first set, then same
as treatment), and id (label the subject, so each id should have 2
observations).
Thus, a dataset with n observations will become 2n observations. To fit, do
fit <- coxph(Surv(time1,delta1) ~ treatm...
2008 Oct 10
1
Correlation among correlation matrices cor() - Interpretation
...1.1, 2.2, 3.3, 4.4, .55)
> var3 <-c(22.2, 66.7, 99.9, 1000008, 123123, .1, .2, .3, .4, .5)
> var4<- c(.000001,.00001,.0001, .001, .1, .12345, .56789, .67890, .78901,
.89012)
> dat <- cbind(var1,var2,var3,var4)
> dat.d <- data.frame(dat)
> treatment1 <- dat.d[1:5,]
> treatment2 <-dat.d[6:10,]
> t1.d.cor <- cor(treatment1)
> t2.d.cor <- cor(treatment2)
> I <-lower.tri(t1.d.cor)
> t1.t2 <- cor(cbind(T1 = t1.d.cor[I], T2 = t2.d.cor[I]))
> t1.t2
T1 T2
T1 1.0000000 0.2802750
T2 0.2802750 1.0000000
My code may be unpolished,
Thank...
2011 Feb 08
1
Error in example Glm rms package
...8,17,15,20,10,20,25,13,12)
Glm> outcome <- gl(3,1,9)
Glm> treatment <- gl(3,3)
Glm> f <- glm(counts ~ outcome + treatment, family=poisson())
Glm> f
Call: glm(formula = counts ~ outcome + treatment, family = poisson())
Coefficients:
(Intercept) outcome2 outcome3 treatment2 treatment3
3.045e+00 -4.543e-01 -2.930e-01 -4.210e-16 -3.997e-16
Degrees of Freedom: 8 Total (i.e. Null); 4 Residual
Null Deviance: 10.58
Residual Deviance: 5.129 AIC: 56.76
Glm> anova(f)
Analysis of Deviance Table
Model: poisson, link: log
Response: counts
Terms added...
2009 May 18
2
Overdispersion using repeated measures lmer
...Variance Std.Dev. Corr
Block (Intercept) 0.06882396 0.262343
Month 0.00011693 0.010813 1.000
Number of obs: 160, groups: Block, 6
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.624030 0.175827 9.237 < 2e-16 ***
Treatment2.Radiata 0.150957 0.207435 0.728 0.466777
Treatment3.Aldabra -0.005458 0.207435 -0.026 0.979009
Month -0.079955 0.022903 -3.491 0.000481 ***
Treatment2.Radiata:Month 0.048868 0.033340 1.466 0.142717
Treatment3.Aldabra:Month 0.077697 0.033340 2.330...
2005 Sep 07
1
FW: Re: Doubt about nested aov output
...(Intercept) 2.1238e-08 0.00014573
Rat (Intercept) 2.0609e+01 4.53976242
Residual 4.2476e+01 6.51733769
# of obs: 36, groups: Rat:Liver, 6; Rat, 2
Fixed effects:
Estimate Std. Error DF t value Pr(>|t|)
(Intercept) 140.5000 3.7208 33 37.7607 < 2.2e-16 ***
Treatment2 10.5000 2.6607 33 3.9463 0.0003917 ***
Treatment3 -5.3333 2.6607 33 -2.0045 0.0532798 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Trtmn2
Treatment2 -0.358
Treatment3 -0.358...
2006 Jan 29
1
extracting 'Z' value from a glm result
Hello R users
I like to extract z values for x1 and x2. I know how to extract coefficents
using model$coef
but I don't know how to extract z values for each of independent variable. I
looked around
using names(model) but I couldn't find how to extract z values.
Any help would be appreciated.
Thanks
TM
#########################################################
>summary(model)
Call:
2009 Aug 19
3
Sweave output from print.summary.glm is too wide
...rmula = counts ~ outcome + treatment, family = poisson())
Deviance Residuals:
1 2 3 4
-0.67125 0.96272 -0.16965 -0.21999
5 6 7 8
-0.95552 1.04939 0.84715 -0.09167
9
-0.96656
Coefficients:
Estimate Std. Error
(Intercept) 3.045e+00 1.709e-01
outcome2 -4.543e-01 2.022e-01
outcome3 -2.930e-01 1.927e-01
treatment2 8.717e-16 2.000e-01
treatment3 4.557e-16 2.000e-01
z value Pr(>|z|)
(Intercept) 17.815 <2e-16 ***
outcome2 -2.247 0.0246 *
outcome3 -1.520 0.1285
treatment2 4.36e-15 1.0000
treatment3 2.28e-15 1.0000
The final pdf output file is mostly fine: but not all of the output of print.summary.glm...
2006 Sep 05
1
help: advice on the structuring of ReML models for analysing growth curves
...is should be a
random factor? Growth is not linear exactly (more quadratic), so I thought
rather than put time in the fixed model I want to control for the effects
of time as a random factor....
The resulting model is this
where id=chick identity and brood=nest box
model1<-lmer(weight~treatment1*treatment2*brood
size*sex+(id|brood)+(1|brood)+(1|age), data=H)
Is this the "right" approach or am I barking up the wrong tree?
Any suggestions much appreciated,
Simon
Simon Pickett
PhD student
Centre For Ecology and Conservation
Tremough Campus
University of Exeter in Cornwall
TR109EZ
Tel 013263...
2009 Jun 05
2
p-values from VGAM function vglm
Anyone know how to get p-values for the t-values from the coefficients
produced in vglm?
Attached is the code and output ? see comment added to output to show
where I need p-values
+ print(paste("********** Using VGAM function gamma2 **********"))
+ modl2<-
vglm(MidPoint~Count,gamma2,data=modl.subset,trace=TRUE,crit="c")
+ print(coef(modl2,matrix=TRUE))
2005 May 23
0
using lme in csimtest
...type = "Tukey")
#
# Tukey contrasts for factor treatment, covariables: site +time
+treatment:site + treatment:time
#
#Coefficients:
# Estimate t value Std.Err. p raw p Bonf p adj
#treatment3-treatment1 -0.655 -2.004 0.327 0.050 0.149 0.120
#treatment3-treatment2 -0.581 -1.777 0.327 0.081 0.162 0.143
#treatment2-treatment1 -0.074 -0.227 0.327 0.821 0.821 0.821
___
drs. René Eschen
CABI Bioscience Switzerland Centre
1 Rue des Grillons
CH-2800 Delémont
Switzerland
+41 32 421 48 87 (Direct)
+41 32 421 48 70 (Secretary)
+41 32 421 48 71 (Fax)
&l...
2008 Oct 09
1
Interpretation in cor()
Hello,
I am performing cor() of some of my data. For example, I'll do 3 corr()
(many variables) operations, one for each of the three treatments.
I then do the following:
i <-lower.tri(treatment1.cor)
cor(cbind(one = treatment1.corr[i], two = treatment2.corr[i], three =
treatment3.corr[i]))
Does this operation above tell me how correlated each of the three
treatments is? Because this how I am interpreting it.
Thanks,
Michael Just
[[alternative HTML version deleted]]
2009 Jan 20
1
Poisson GLM
This is a basics beginner question.
I attempted fitting a a Poisson GLM to data that is non-integer ( I believe
Poisson is suitable in this case, because it is modelling counts of
infections, but the data collected are all non-negative numbers with 2
decimal places).
My question is, since R doesn't return an error with this glm fitting, is it
important that the data is non-integer. How does
2009 Nov 22
0
Repeated measures unbalanced in a split-split design
...set)
mod.Cana
Call:
lm(formula = cbind(Diameter.38, Diameter.53, Diameter.73, Diameter.85)
~ Treatment * Hormone, data = marcelo.subset)
Coefficients:
Diameter.38 Diameter.53 Diameter.73 Diameter.85
(Intercept) 1.24000 1.35750 1.99375 2.31000
Treatment2 -0.31625 -0.14250 0.07500 -0.13875
Treatment3 -0.19250 -0.01500 -0.20875 -0.36875
Treatment4 -0.35375 -0.08500 -0.22750 -0.27125
Treatment5 -0.29125 0.04875 -0.14375 -0.26375
Treatment6 -0.00125...