I need to fix my mistakes, from earlier this morning.
The sums should be over densities, so:
fh (X, Y) = [fh1 (X1, X1) + fh2 (X2, Y2) + ... + fhn (Xn, Yn)] / n
fh (X, Y) = w1*fh1 (X1, X1) + w2*fh2 (X2, Y2) + ... + wn*fhn (Xn, Yn)
assuming the weights sum to 1
If simulated data is used, then
Just realised the above notation may be a bit misleading.
Because I was thinking in terms of simulated data.
On Mon, Jun 22, 2020 at 10:00 AM Abby Spurdle wrote:
>
> Hi Frederick,
>
> I glanced at the webpage you've linked.
> (But only the top three snippets).
>
> This is what I would call the s
Hi Frederick,
I glanced at the webpage you've linked.
(But only the top three snippets).
This is what I would call the sum of random variables.
(X, Y) = (X1, X1) + (X2, Y2) + ... + (Xn, Yn)
The example makes the mistake of assuming that the Xs are normally
distributed, and each of the Ys are fro
On 21/06/2020 11:37 a.m., Benjamin Tyner wrote:
On 6/20/20 5:04 PM, Duncan Murdoch wrote:
I think you effectively did that in your original post (all but
encapsulating the expression in a function), so yes, it's possible.
However, it's a really bad idea. Why use non-standard evaluation when
sta
Hi
Do you mean you want to reduce *system requirements* ? I'm not sure you
have many options. Looking at the plantuml output format options, there
is ...
png via 'png', which requires libpng
svg via 'grImport2', which requires 'rsvg', which requires librsvg2
eps via 'grImport', which requir
Dear Peter,
Thanks for your reply, and pointing out my mistake of misspelling in
'Permanganate' variable.
However, it couldn't be detected because the same code, with misspelled
variable at factor declaration level, ran successful ly till I upgraded to
R-4.0.0 and later to R-0.1.
I am sure the b
On 6/20/20 5:04 PM, Duncan Murdoch wrote:
I think you effectively did that in your original post (all but
encapsulating the expression in a function), so yes, it's possible.
However, it's a really bad idea. Why use non-standard evaluation when
standard evaluation is fine? Standard evaluation
Hello everyone
At the moment I put a lot of attention in the uncertainty of my analyzes. I
want to do a spearman correlation that takes into account the uncertainty in my
observations and has weighting.
uncertainty of observations: I came across this excellent blog that proposes a
bootstrap fu
This is fallout from the stringsAsFactors changes. You have 'Permanganate' as a
character vector and it runs afoul of this code
aterms <- attributes(terms(x))
dcl <- aterms$dataClasses[-aterms$response]
facvars <- names(dcl)[dcl %in% c("factor", "ordered")]
which does not include 'Pe
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