On 16-Nov-09 19:22:10, Jack Luo wrote:
> Hi,
> I am trying to fit a logistic regression using glm, but my
> explanatory variables are of mixed type: some are numeric,
> some are ordinal, some are categorical, say
>
> If x1 is numeric, x2 is ordinal, x3 is categorical, is the
> following formula OK
On Nov 16, 2009, at 2:53 PM, Jack Luo wrote:
> David,
>
> Thanks for your reply. Since I am kinda new to this forum, could you
> please advise me on where to read those questions in R-help?
http://search.r-project.org/nmz.html
http://search.r-project.org/cgi-bin/namazu.cgi?query=%22ordered+fa
David,
Thanks for your reply. Since I am kinda new to this forum, could you please
advise me on where to read those questions in R-help? In addition, I did not
pay much attention to the na.action, probably I should use na.action =
na.omit instead of na.pass.
-Jack
On Mon, Nov 16, 2009 at 2:32 PM
On Nov 16, 2009, at 2:22 PM, Jack Luo wrote:
Hi,
I am trying to fit a logistic regression using glm, but my explanatory
variables are of mixed type: some are numeric, some are ordinal,
some are
categorical, say
If x1 is numeric, x2 is ordinal, x3 is categorical, is the following
formula
Hi,
I am trying to fit a logistic regression using glm, but my explanatory
variables are of mixed type: some are numeric, some are ordinal, some are
categorical, say
If x1 is numeric, x2 is ordinal, x3 is categorical, is the following formula
OK?
*model <- glm(y~x1+x2+x3, family=binomial(link="l
Thank you very much, Jun. This is what I was looking for.
Best!
Dani
On Wed, 2009-08-19 at 09:52 -0500, Jun Shen wrote:
> I would suggest a model with a baseline level, something like
>
> nls(AMP~E0+(Emax-E0)*Time**gamma/(EC50**gamma+Time**gamma),data=your
> data,
> start=list(EC50=50,gamma=2,E
I would suggest a model with a baseline level, something like
nls(AMP~E0+(Emax-E0)*Time**gamma/(EC50**gamma+Time**gamma),data=your data,
start=list(EC50=50,gamma=2,E0=0.2,Emax=1.2))->mod.test
AIC(mod.test) does improve. Hope this helps.
Jun
On Wed, Aug 19, 2009 at 5:04 AM, Dani Valverde wrote:
with my limited understanding, I am not surprised to see this data fitting
nicely at the end just by eyeballing at it. the reaction at the early time
point is not completed as the time passes which is close to 20 units the
reaction generates more metabolite to be measured reliably your t=0 and t=1
Hello,
I have this data:
Time AMP
0 0.200
10 0.1958350
20 0.2914560
40 0.6763628
60 0.8494534
90 0.9874526
120 1.0477692
where AMP is the concentration of this metabolite with time. If you plot
the data, you can see that it could be fitted using a logistic
regres
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