Coeficients estimation in a repeated measures linear model

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Coeficients estimation in a repeated measures linear model

Sergio.pv
Dear Users,

I am trying to understand the inner workings of a repeated measures linear
model. Take for example a situation with 6 individuals sampled twice for
two conditions (control and treated).

set.seed(12)
ctrl <- rnorm(n = 6, mean = 2)
ttd <- rnorm(n = 6, mean = 10)
dat <- data.frame(vals = c(ctrl, ttd),
                  group = c(rep("ctrl", 6), rep("ttd", 6)),
                  ind = factor(rep(1:6, 2)))

fit <- lm(vals ~ ind + group, data = dat)
model.matrix(~ ind + group, data = dat)

I am puzzled on how the coeficients are calculated. For example, according
to the model matrix, I thought the intercept would be individual 1 control.
But that is clearly not the case.
For the last coeficient, I understand it as the mean of all differences
between treated vs control at each individual.

I would greatly appreciate if someone could clarify to me how the
coefficients in this situation are estimated.

Thanks

        [[alternative HTML version deleted]]

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Re: Coeficients estimation in a repeated measures linear model

Bert Gunter-2
Sergio:

1. You do not have a "repeated measures linear model" .

2. This list is not designed to replace your own efforts to learn the
necessary R background, in this case, factor coding and contrasts in linear
models. I would suggest you spend some time with any of the many fine R
linear model tutorials that can be found on the web. Here is one place to
look for suggestions: https://www.rstudio.com/online-learning/#R  . But
just googling around you'll probably find something that may suit even
better.

3. This list is primarily for R programming help, not statistics help
(although they do sometimes intersect). For the latter, try a statistics
site like stats.stackexchange.com  .

4. Finally, as always, consulting with a local statistical resource, if
available, is always worth considering.

HTH.

Cheers,
Bert



Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Wed, Dec 6, 2017 at 6:17 AM, Sergio PV <[hidden email]> wrote:

> Dear Users,
>
> I am trying to understand the inner workings of a repeated measures linear
> model. Take for example a situation with 6 individuals sampled twice for
> two conditions (control and treated).
>
> set.seed(12)
> ctrl <- rnorm(n = 6, mean = 2)
> ttd <- rnorm(n = 6, mean = 10)
> dat <- data.frame(vals = c(ctrl, ttd),
>                   group = c(rep("ctrl", 6), rep("ttd", 6)),
>                   ind = factor(rep(1:6, 2)))
>
> fit <- lm(vals ~ ind + group, data = dat)
> model.matrix(~ ind + group, data = dat)
>
> I am puzzled on how the coeficients are calculated. For example, according
> to the model matrix, I thought the intercept would be individual 1 control.
> But that is clearly not the case.
> For the last coeficient, I understand it as the mean of all differences
> between treated vs control at each individual.
>
> I would greatly appreciate if someone could clarify to me how the
> coefficients in this situation are estimated.
>
> Thanks
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: Coeficients estimation in a repeated measures linear model

Jim Lemon-4
In reply to this post by Sergio.pv
Hi Sergio,
You seem to be aiming for a univariate repeated measures analysis.
Maybe this will help:

subno<-rep(1:6,2)
dat <- data.frame(subno=rep(1:6,2),,vals = c(ctrl, ttd),
   cond = c(rep("ctrl", 6), rep("ttd", 6)), ind = factor(rep(1:6, 2)))
fit<-aov(vals~ind+cond+Error(subno),data=dat)
fit
summary(fit)

Note that the assumptions of this model are easy to violate.

Jim


On Thu, Dec 7, 2017 at 1:17 AM, Sergio PV <[hidden email]> wrote:

> Dear Users,
>
> I am trying to understand the inner workings of a repeated measures linear
> model. Take for example a situation with 6 individuals sampled twice for
> two conditions (control and treated).
>
> set.seed(12)
> ctrl <- rnorm(n = 6, mean = 2)
> ttd <- rnorm(n = 6, mean = 10)
> dat <- data.frame(vals = c(ctrl, ttd),
>                   group = c(rep("ctrl", 6), rep("ttd", 6)),
>                   ind = factor(rep(1:6, 2)))
>
> fit <- lm(vals ~ ind + group, data = dat)
> model.matrix(~ ind + group, data = dat)
>
> I am puzzled on how the coeficients are calculated. For example, according
> to the model matrix, I thought the intercept would be individual 1 control.
> But that is clearly not the case.
> For the last coeficient, I understand it as the mean of all differences
> between treated vs control at each individual.
>
> I would greatly appreciate if someone could clarify to me how the
> coefficients in this situation are estimated.
>
> Thanks
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.