Shouldn't this be similar (if not equivalent ) to examining the
leverage or influence of z1 and z{n+1} in the full model of Y ~
beta*Z[1:(n+1)] ?
--
David
On Jan 27, 2012, at 7:46 PM, Michael wrote:
> Yes, these observations are measured at equal-spaces...
>
> And the "n"-axis is the time
Yes, these observations are measured at equal-spaces...
And the "n"-axis is the time axis...
Thank you!
On Fri, Jan 27, 2012 at 3:54 PM, David Winsemius wrote:
>
> On Jan 27, 2012, at 4:10 PM, Michael wrote:
>
> I changed the notation for data from x to z...
>>
>> That's it. Should be very clear
On Jan 27, 2012, at 4:10 PM, Michael wrote:
I changed the notation for data from x to z...
That's it. Should be very clear now... Thanks!
Data: z1, z2, ..., z_{n+1}
y1 = z_1,z_2,. z_n
y2 = z_2, z_3,. z_{n+1}
x1 = 1, ..., n
x2 = 1, ..., n
y1 = A1+ x1 * B1 + epsilon_1
y2 = A
Hi: I don't think my paper applies because it needs the same y. Also, I
don't think I follow
what you're doing now ( now you have different y and different x's ? ) so
I'd rather not comment but hopefully someone else does understand Good luck.
On Fri, Jan 27, 2012 at 4:10 PM, Michael wrote:
> I
I changed the notation for data from x to z...
That's it. Should be very clear now... Thanks!
Data: z1, z2, ..., z_{n+1}
y1 = z_1,z_2,. z_n
y2 = z_2, z_3,. z_{n+1}
x1 = 1, ..., n
x2 = 1, ..., n
y1 = A1+ x1 * B1 + epsilon_1
y2 = A2 + x2 * B2 + epsilon_2
H0: B1 and B2 are stati
now i'm confused because you first use y_1, y_2 and then use y later. I
would take
a look at that earlier paper i mentioned. I think it's along the lines of
what you want. Unfortunately. I don't have a computer copy of it. I got it
from a library service where I once worked.
mark
On Fri, Jan 27,
Thanks all.
Here are a more clear statement of my question:
Data: z1, z2, ..., z_{n+1}
y1 = z_1,z_2,. z_n
y2 = z_2, z_3,. z_{n+1}
x1 = 1, ..., n
x2 = 1, ..., n
y = A1+ x1 * B1 + epsilon_1
y = A2 + x2 * B2 + epsilon_2
H0: B1 and B2 are statistically significally different...
Hi Richard: I read michael's question as meaning that he says two
univariate no intercept
regression model where the predictor data is different in each model so that
x1 = x_11,x_12,. x_1n
x2 = x_21, x_22,. x_2n
y = y_1, .y_n
y = x1 * B1 + epsilon_1
y = x2 * B2 + epsilon_2
a
It looks like you might be asking for the anova() on two models.
M1 <- lm(y ~ x1 + x2 + x3, data=something)
M2 <- lm(y ~ x2 + x3, data=something)
anova(M1, M2)
Please send a reproducible example to the list if more detail is needed.
Rich
On Thu, Jan 26, 2012 at 11:59 PM, Michael wrote:
I don't know what is available in R but a relevant paper that provides a
test is by
Hotelling, H ( September, 1940 )
"The Selection of Variates For Use in Prediction With Some Comments On The
General Problem of Nuisance Parameters".
Annals of Mathematical Statistics, 11, 271-283.
On Thu, Ja
Hi al,
I am looking for a R command to test the difference of two linear
regressoon betas.
Lets say I have data x1, x2...x(nï¼1).
beta1 is obtained from regressing x1 to xn onto 1 to n.
beta2 is obtained from regressing x2 to x(nï¼1) onto 1 to n.
Is there a way in R to test whether beta1 and
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