Displaying 15 results from an estimated 15 matches for "drugb".
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2018 Mar 05
2
data analysis for partial two-by-two factorial design
...2018, at 2:27 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote:
> >
> > David:
> >
> > I believe your response on SO is incorrect. This is a standard OFAT (one
> factor at a time) design, so that assuming additivity (no interactions),
> the effects of drugA and drugB can be determined via the model you rejected:
>
> >> three groups, no drugA/no drugB, yes drugA/no drugB, yes drugA/yes drug
> B, omitting the fourth group of no drugA/yes drugB.
>
> >
> > For example, if baseline control (no drugs) has a response of 0, drugA
> has...
2018 Mar 05
0
data analysis for partial two-by-two factorial design
> On Mar 5, 2018, at 2:27 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote:
>
> David:
>
> I believe your response on SO is incorrect. This is a standard OFAT (one factor at a time) design, so that assuming additivity (no interactions), the effects of drugA and drugB can be determined via the model you rejected:
>> three groups, no drugA/no drugB, yes drugA/no drugB, yes drugA/yes drug B, omitting the fourth group of no drugA/yes drugB.
>
> For example, if baseline control (no drugs) has a response of 0, drugA has an effect of 1, drugB has an eff...
2018 Mar 05
5
data analysis for partial two-by-two factorial design
David:
I believe your response on SO is incorrect. This is a standard OFAT (one
factor at a time) design, so that assuming additivity (no interactions),
the effects of drugA and drugB can be determined via the model you rejected:
For example, if baseline control (no drugs) has a response of 0, drugA has
an effect of 1, drugB has an effect of 2, and the effects are additive,
with no noise we would have:
> d <- data.frame(drugA = c("n","y","y"...
2018 Mar 05
0
data analysis for partial two-by-two factorial design
...On Mar 5, 2018, at 2:27 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote:
> >
> > David:
> >
> > I believe your response on SO is incorrect. This is a standard OFAT (one factor at a time) design, so that assuming additivity (no interactions), the effects of drugA and drugB can be determined via the model you rejected:
>
> >> three groups, no drugA/no drugB, yes drugA/no drugB, yes drugA/yes drug B, omitting the fourth group of no drugA/yes drugB.
>
> >
> > For example, if baseline control (no drugs) has a response of 0, drugA has an effec...
2018 Mar 05
2
data analysis for partial two-by-two factorial design
...B). is it different from running three separate T tests?
Thank you so much!!
Ding
I need to analyze data generated from a partial two-by-two factorial design: two levels for drug A (yes, no), two levels for drug B (yes, no); however, data points are available only for three groups, no drugA/no drugB, yes drugA/no drugB, yes drugA/yes drug B, omitting the fourth group of no drugA/yes drugB. I think we can not investigate interaction between drug A and drug B, can I still run model using R as usual: response variable = drug A + drug B? any suggestion is appreciated.
From: Bert Gunter [mail...
2018 Mar 05
0
data analysis for partial two-by-two factorial design
...ing three separate T tests?
>
> Thank you so much!!
>
> Ding
>
> I need to analyze data generated from a partial two-by-two factorial design: two levels for drug A (yes, no), two levels for drug B (yes, no); however, data points are available only for three groups, no drugA/no drugB, yes drugA/no drugB, yes drugA/yes drug B, omitting the fourth group of no drugA/yes drugB. I think we can not investigate interaction between drug A and drug B, can I still run model using R as usual: response variable = drug A + drug B? any suggestion is appreciated.
Replied on CrossValidate...
2018 Mar 05
0
data analysis for partial two-by-two factorial design
...mius
Cc: Ding, Yuan Chun; r-help at r-project.org
Subject: Re: [R] data analysis for partial two-by-two factorial design
David:
I believe your response on SO is incorrect. This is a standard OFAT (one factor at a time) design, so that assuming additivity (no interactions), the effects of drugA and drugB can be determined via the model you rejected:
For example, if baseline control (no drugs) has a response of 0, drugA has an effect of 1, drugB has an effect of 2, and the effects are additive, with no noise we would have:
> d <- data.frame(drugA = c("n","y","y")...
2018 Mar 02
3
data analysis for partial two-by-two factorial design
Dear R users,
I need to analyze data generated from a partial two-by-two factorial design: two levels for drug A (yes, no), two levels for drug B (yes, no); however, data points are available only for three groups, no drugA/no drugB, yes drugA/no drugB, yes drugA/yes drug B, omitting the fourth group of no drugA/yes drugB. I think we can not investigate interaction between drug A and drug B, can I still run model using R as usual: response variable = drug A + drug B? any suggestion is appreciated.
Thank you very much!
Yu...
2018 Mar 02
0
data analysis for partial two-by-two factorial design
...uan Chun <ycding at coh.org> wrote:
> Dear R users,
>
> I need to analyze data generated from a partial two-by-two factorial
> design: two levels for drug A (yes, no), two levels for drug B (yes, no);
> however, data points are available only for three groups, no drugA/no
> drugB, yes drugA/no drugB, yes drugA/yes drug B, omitting the fourth group
> of no drugA/yes drugB. I think we can not investigate interaction between
> drug A and drug B, can I still run model using R as usual: response
> variable = drug A + drug B? any suggestion is appreciated.
>
>...
2011 Oct 20
3
Survival analysis
Hello,
I need some results from the survival analysis of my data
that I do not know whether exist in Survival Package or how to obtain if
they do:
1. The Mean survival time
2. The standard error of the mean
3. Point and 95% Lower & Upper Confidence Intervals estimates
Any help will be greatly appreciated.
Cem
[[alternative HTML version
2006 May 15
1
anova statistics in lmer
...ev.
Patient (Intercept) 44.541 6.6739
Residual 29.780 5.4571
number of obs: 120, groups: Patient, 24
Fixed effects:
Estimate Std. Error t value
(Intercept) 33.96209 9.93059 3.4199
baseHR 0.58819 0.11846 4.9653
Time -10.69835 2.42079 -4.4194
Drugb 3.38013 3.78372 0.8933
Drugp -3.77824 3.80176 -0.9938
Time:Drugb 3.51189 3.42352 1.0258
Time:Drugp 7.50131 3.42352 2.1911
Correlation of Fixed Effects:
(Intr) baseHR Time Drugb Drugp Tm:Drgb
baseHR -0.963
Time -0.090 0.000
Drugb -0...
2011 Oct 17
3
Extracting results from a function output
...need "median" value for
the group DrugA which is 48. "Print" function does not reveal them either.
Thank you.
> fit
Call: survfit(formula = Surv(tT, dT) ~ gT, conf.type = "log-log")
records n.max n.start events median 0.95LCL 0.95UCL
gT=DrugA 9 9 9 6 48 32 NA
gT=DrugB 9 9 9 3 NA 42 NA
gT=DrugC 9 9 9 4 NA 42 NA
gT=Vehicle 9 9 9 8 40 37 45
> str(fit)
List of 14
$ n : int [1:4] 9 9 9 9
$ time : num [1:28] 32 43 45 46 48 54 55 60 62 42 ...
$ n.risk : num [1:28] 9 8 7 6 5 4 3 2 1 9 ...
$ n.event : num [1:28] 1 1 1 1 1 0 1 0 0 2 ...
$ n.censor : num [1:28] 0 0 0 0...
2012 Jul 20
1
Extracting standard errors for adjusted fixed effect sizes in lmer
...m 92 92 92 92 92 54 54 54 54 54 ...
$ HR : num 76 84 88 96 84 58 60 60 60 64 ...
$ Time : num 0.0167 0.0833 0.25 0.5 1 ...
> fm1 <- lmer(HR ~ baseHR + Time + Drug + (1 | Patient), HR)
> fixef(fm1) ##Extract estimates of fixed effects
(Intercept) baseHR Time Drugb Drugp
32.6037923 0.5881895 -7.0272873 4.6795262 -1.0027581
> se.fixef(fm1) ##Extract standard error of estimates of fixed effects
(Intercept) baseHR Time Drugb Drugp
9.9034008 0.1184529 1.4181457 3.5651679 3.5843026
##Because the estimate of t...
2009 Oct 10
2
easy way to find all extractor functions and the datatypes of what they return
...ions for querying an object so that I can quickly learn
the extractor functions to pull out the data and manipulate it?
Will the datatypes returned usually be named vectors and named
matrices, indiced by categorical values in the data
( "Male" "Female" "Placebo" "DrugB" etc )? If they are indexed by 1 ,
2 , 3 , 4 , it's easier to lose track.
thanks a bunch in advance
2009 Oct 27
0
syntax for estimable(gmodels package) and glht(multcomp package)
...dels with 2nd order
terms.
A modestly complex model:
2-way anova with one continuous covariate, no random effects(and no
repeated measures) to keep it modestly complex:
Y = treatmentgroup + sex + treatmentgroup*sex + weight
treatment has 3 levels : "Placebo" , "DrugA" , "DrugB"
sex has 2 levels
I want to do pairwise comparison(s) for one of the main effects, say
"DrugB" - "Placebo"
And a pairwise comparison at the cell-wise level, for example:
"Female:DrugA" - "Female:Placebo" or
"Female:DrugA" - "Male:DrugA&q...