> be suboptimal; in the case of replication weights, even wrong.
> Hence, standard errors and analysis of variance tables should be
> treated with care.
>
> OK?
>
>
> -pd
>
>
>> On 12 Oct 2017, at 13:48 , Arie ten Cate <arietenc...@gmail.com>
rambsch."
Let us repair the other bug also.
Arie
On Thu, Oct 12, 2017 at 1:48 PM, Arie ten Cate <arietenc...@gmail.com> wrote:
> OK. We have now three suggestions to repair the text:
> - remove the text
> - add "not" at the beginning of the text
> - add at t
, the terms() function is only following the cited behavior 1/3rd
> of the time.
>
> Best regards,
> Tyler
>
> On Mon, Nov 6, 2017 at 6:45 AM, Arie ten Cate <arietenc...@gmail.com> wrote:
>>
>> Hello Tyler,
>>
>> You write that you understa
t; does not do anything special for them, and it remains valid, in a trivial
> sense, whenever any of the F_j is numeric rather than categorical." Since
> F_j refers to both categorical and numeric variables, the behavior of
> model.matrix is not consistent with the heuristic.
>
ehavior of R does not match the heuristic that
> it's citing.
>
> Best regards,
> Tyler
>
> On Thu, Nov 2, 2017 at 2:51 AM, Arie ten Cate <arietenc...@gmail.com> wrote:
>>
>> Hello Tyler,
>>
>> Thank you for searching for, and finding, the basic description
ave demonstrated that this dummy
> variable encoding only occurs for the model where the missing term is the
> numeric-numeric interaction, "~(X1+X2+X3)^3-X1:X2". Otherwise, the
> interaction term X1:X2:X3 is encoded by contrasts, not dummy variables. This
> is inconsistent w
1 1 0 0 1 0
> 4 1 1 0 0 -1 0
> 5 1 0 1 0 0 1
> 6 1 0 1 0 0 -1
>
>> solve(t(mm) %*% mm)
> Error in solve.default(t(mm) %*% mm) : system is computationally singular:
>
I think it is not a bug. It is a general property of interactions.
This property is best observed if all variables are factors
(qualitative).
For example, you have three variables (factors). You ask for as many
interactions as possible, except an interaction term between two
particular variables.
s like
>
> y <- c(0,1)
> w <- c(49,51)
> glm(y~1, weights=w, family=binomial)
>
> -pd
>
>> On 9 Oct 2017, at 07:58 , Arie ten Cate <arietenc...@gmail.com> wrote:
>>
>> Yes. Thank you; I should have quoted it.
>> I suggest to re
the point you were
> trying to make.
>
> Best,
> Wolfgang
>
> -Original Message-
> From: R-devel [mailto:r-devel-boun...@r-project.org] On Behalf Of Arie ten
> Cate
> Sent: Sunday, 08 October, 2017 14:55
> To: r-devel@r-project.org
> Subject: [Rd] Disc
the first three observations have variance
sigma^2).
Best,
Wolfgang
-Original Message-
From: R-devel [mailto:r-devel-boun...@r-project.org] On Behalf Of Arie ten Cate
Sent: Saturday, 07 October, 2017 9:36
To: r-devel@r-project.org
Subject: [Rd] Discourage the weights= option of lm
In the Details section of lm (linear models) in the Reference manual,
it is suggested to use the weights= option for summarized data. This
must be discouraged rather than encouraged. The motivation for this is
as follows.
With summarized data the standard errors get smaller with increasing
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