Displaying 4 results from an estimated 4 matches for "druga_on".
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druga_only
2018 Mar 05
2
data analysis for partial two-by-two factorial design
...has an effect of 2, and the effects are additive,
> with no noise we would have:
> >
> > > d <- data.frame(drugA = c("n","y","y"),drugB = c("n","n","y"))
>
> d2 <- data.frame(trt = c("Baseline","DrugA_only","DrugA_drugB")
> >
> > > y <- c(0,1,3)
> >
> > And a straighforward inear model recovers the effects:
> >
> > > lm(y ~ drugA + drugB, data=d)
> >
> > Call:
> > lm(formula = y ~ drugA + drugB, data = d)
> >
> &...
2018 Mar 05
0
data analysis for partial two-by-two factorial design
...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"),drugB = c("n","n","y"))
d2 <- data.frame(trt = c("Baseline","DrugA_only","DrugA_drugB")
>
> > y <- c(0,1,3)
>
> And a straighforward inear model recovers the effects:
>
> > lm(y ~ drugA + drugB, data=d)
>
> Call:
> lm(formula = y ~ drugA + drugB, data = d)
>
> Coefficients:
> (Intercept) drugAy...
2018 Mar 05
0
data analysis for partial two-by-two factorial design
...ugB has an effect of 2, and the effects are additive, with no noise we would have:
> >
> > > d <- data.frame(drugA = c("n","y","y"),drugB = c("n","n","y"))
>
> d2 <- data.frame(trt = c("Baseline","DrugA_only","DrugA_drugB")
> >
> > > y <- c(0,1,3)
> >
> > And a straighforward inear model recovers the effects:
> >
> > > lm(y ~ drugA + drugB, data=d)
> >
> > Call:
> > lm(formula = y ~ drugA + drugB, data = d)
> >
> &...
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