Dear R package community,
I am uncertain whether this is appropriate for this mailing list. Please
let me know. If not, would you be so kind as to point me in a better
direction?
I am a mathematics major with a well-developed R experience. I have
graduated two years ago and have been working in b
Run this code
tree<-graph_from_literal(1-+2:3,3-+5,1-+4);
graph.bfs(tree,root=1, neimode="out",father=TRUE,order=TRUE,unreachable =
FALSE)
I do not understand why the father values will give NA 1 1 3 1 rather than NA
1 1 1 3
The reason I am doing this is to obtain the values(by vertex names) or
In https://en.wikipedia.org/wiki/Pareto_distribution, it is clear what the
parameters are for the pareto distribution: *xmin *the scale parameter and
*a* the shape parameter.
I am using rmutil to generate random deviates from a pareto distribution.
It says in the documentation that the probabilty
Below are samples from a kernel density estimated "data" with gaussian
kernel.
I really like this solution of estimation of a kernel because it is nice
and elegant.
fit<-density(data)
rnorm(N, sample(data, size = N, replace = TRUE), fit$bw) #samples from
kernel density estimation
I am however in
data.frame(A=factor(c(1,1,1,1,1,1,2,2,2,2,2,2)),
> > + B=factor(c(1,1,2,2,3,3,1,1,2,2,3,3)),
> > + y=rnorm(12))
> >> mod <- aov(y ~ A+B, data=tmp)
> >> summary(mod)
> > Df Sum Sq Mean Sq F value Pr(>F)
> > A1 1.553 1.553 1.
Something does not make sense in R. It has to do with the question of
balance and unbalance.
*A<-factor(c(1,1,1,1,1,1,2,2,2,2,2,2))*
*B<-factor(c(1,1,2,2,3,3,1,1,2,2,3,3))*
*y<-rnorm(12)*
*mod<-aov(y~A+B)*
I was under the impression that the design is balanced ie order does not
effect the sums of
I have something which has been bugging me and I have even asked this on
cross validated but I did not get a response. Let's construct a simple
example. Below is the code.
A<-gl(2,4) #factor of 2 levels
B<-gl(4,2) #factor of 4 levels
df<-data.frame(y,A,B)
As you can see, B is nested within A.
Th
Hi again I need help
*R-code*
debug(model.matrix)
model.matrix(~S)
*model.matrix code*
ans <- .External2(C_modelmatrix, t, data) #t =terms(object) , data="data
frame of object"
*modelframe C-code*
SEXP modelframe(SEXP call, SEXP op, SEXP args, SEXP rho)
{
SEXP terms, data, names, variables,
Hi
I need some advice. Note: I do not know anything from C apart from my 2
days of research.
I am currently trying to make meaning of the modelmatrix function (written
in C) and called from R function model.matrix() via .External2.
In trying to view the source code (in R) for model.matrix(), I h
o
> error strata becomes nonsensical otherwise. If you do have a balanced
> design, the error strata reduce to simple combinations of means and
> observation, so the aov() algorithm is quite inefficient, but to my
> knowledge nobody has bothered to try and do better.
>
> -pd
>
> &
Below is the covariates for a model ~x1+x2+x3+x4+x5+x6. I noticed that when
fitting this model that the coefficient x6 is unestimable.*Is this merely a
case that adding more columns to my model matrix will eventually lead to
linear dependance so the more terms I have in the model formulae the more
I notice a lot of r documentation refer to this reference below. I can't
seem to find it anywhere.
Does anyone have a link to point to where I can either view it or buy it?
*Chambers, J. M., Freeny, A and Heiberger, R. M. (1992) Analysis of
variance; designed experiments*
--
Yours sincerely,
Ju
.
Does this makes sense?
On 19 July 2016 at 16:19, Michael Dewey wrote:
> Presumably it disappears because there is a unique value of ID for eac
> combination of S*x1 so they are indistinguishable.
>
>
> On 19/07/2016 12:53, Justin Thong wrote:
>
>> Why does the S:
Why does the S:x1 column disappear (presumably S:x1 goes into ID but I dont
know why)? S is a factor, x1 is a covariate and ID is a factor.
rich.side<-aov(y~S*x1+ID)
summary(rich.side)
Below is the model frame
model.frame(~S*x1+ID)
S x1 ID
1 1 12 A
2 1 12 A
Hi
*I have been looking for a reference to explain how R uses the aov
command(at a deeper level)*. More specifically, how R reads the formulae
and R computes the sums of squares. I am not interested in understanding
what the difference of Type 1,2,3 sum of squares are. I am more interested
in find
15 matches
Mail list logo
