On 9/18/21 5:28 AM, Leonard Mada via R-help wrote:
Hello Andrew,
I add this info as a completion (so other users can get a better
understanding):
If we want to perform a survival analysis, than the interval should be
closed to the right, but we should include also the first time point (as
pe
Hello Andrew,
I add this info as a completion (so other users can get a better
understanding):
If we want to perform a survival analysis, than the interval should be
closed to the right, but we should include also the first time point (as
per Intention-to-Treat):
[0, 4](4, 8](8, 12](12, 16]
Perhaps you and Andrew should take this discussion off list...
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Fri, Sep 17, 2021 at 3:45 PM Leonard Mada via R-he
The warn should be in cut() => .bincode().
It should be generated whenever a real value (excludes NA or NAN or +/-
Inf) is not included in any of the bins.
If the user writes a script and doesn't want any warnings: he can select
warn = FALSE. But otherwise it would be very helpful to catch
Why would you want to merge different factors?
It makes no sense on real data. Even if some names are the same, the
factors are not the same!
The only real-data application that springs to mind is censoring (right
or left, depending on the choice): but here we have both open and closed
interv
Re your objection that "the user has to suspect that some values were not
included" applies equally to your proposed warn option. There are a lot of ways
to introduce NAs... in real projects all analysts should be suspecting this
problem.
On September 17, 2021 3:01:35 PM PDT, Leonard Mada via R
Hello Andrew,
But "cut" generates factors. In most cases with real data one expects to
have also the ends of the interval: the argument "include.lowest" is
both ugly and too long.
[The test-code on the ftable thread contains this error! I have run
through this error a couple of times.]
The
I disagree, I don't really think it's too long or ugly, but if you think it
is, you could abbreviate it as 'i'.
x <- 0:20
breaks1 <- seq.int(0, 16, 4)
breaks2 <- seq.int(0, 20, 4)
data.frame(
cut(x, breaks1, right = FALSE, i = TRUE),
cut(x, breaks2, right = FALSE, i = TRUE),
check.nam
While it is not explicitly mentioned anywhere in the documentation for
.bincode, I suspect 'include.lowest = FALSE' is the default to keep the
definitions of the bins consistent. For example:
x <- 0:20
breaks1 <- seq.int(0, 16, 4)
breaks2 <- seq.int(0, 20, 4)
cbind(
.bincode(x, breaks1, right
Thank you Andrew.
Is there any reason not to make: include.lowest = TRUE the default?
Regarding the NA:
The user still has to suspect that some values were not included and run
that test.
Leonard
On 9/18/2021 12:53 AM, Andrew Simmons wrote:
> Regarding your first point, argument 'include.
Regarding your first point, argument 'include.lowest' already handles this
specific case, see ?.bincode
Your second point, maybe it could be helpful, but since both 'cut.default'
and '.bincode' return NA if a value isn't within a bin, you could make
something like this on your own.
Might be worth
Hello List members,
the following improvements would be useful for function cut (and .bincode):
1.) Argument: Include extremes
extremes = TRUE
if(right == FALSE) {
# include also right for last interval;
} else {
# include also left for first interval;
}
2.) Argument: warn = TRUE
Warn
> On Aug 28, 2017, at 9:26 AM, Elie Canonici Merle
> wrote:
>
> Chuck (Is it fine to call you Chuck?)
In this forum, yes please.
> I don't know much about pmin and factor but it might worth looking into if
> you want to manipulate states by names (I assume this is why one might want
> to use
Chuck (Is it fine to call you Chuck?) has far more R jutsu than I do
obviously.
I don't know much about pmin and factor but it might worth looking into if
you want to manipulate states by names (I assume this is why one might want
to use it?)
generate_transition_matrix <- function(data, states)
Ok, I assumed you wanted to compute a matrix M for all states such that
M[i][j]= transition from state i to state j / number of transition from
state i
but from what you just answered it looks like you want to compute a matrix
M for a set of states S such that:
M[S_i][S_j]= transition from stat
All of this can be done without for loops.
Use head(..., -1), tail(..., -1) to get the pre and post states.
Use factor or pmin to recode them as necessary
Use table(pre, post) to get the transition counts.
Use prop.table(table_of_counts,1) to get the probabilities.
HTH,
Chuck
> On Aug 28,
Hi,
I think you overthought this one a little bit, I don't know if this is the
kind of code you are expecting but I came up with something like that:
generate_transition_matrix <- function(data, n_states) {
#To be sure I imagine you should check n_states is right at this point
transiti
Hello,
I am trying to implement a formula
aij= transition from state S_i to S_j/no of transition at state S_i
Code I have written is working with three state {1,2,3 }, but if the number
of states become={1,2,3,4,..n} then the code will not work, so can some
help me with this.
For and s
project.org] On Behalf Of Amelia
> Marsh via R-help
> Sent: Tuesday, February 02, 2016 1:03 PM
> To: R Help R
> Subject: [R] Improvement in Process time
>
> Dear R forum,
>
> I am running a Particular process 1000 times for different rates. Each
> time the result
Dear R forum,
I am running a Particular process 1000 times for different rates. Each time the
result of the process is getting stored (appended) in a data.frame. However,
the process is taking unsual time at times more than 2 hours. When I had tried
to find out the reason for such a long proces
a) This sounds like homework. This is not a homework support forum.
b) If it is not homework, you should take one or more classes on statistics.
Your questions are more about theory than R and this is not a statistics theory
mailing list.
c) You ask questions about the use of your data, but you
These questions are off topic for this list. Try a statistical list
like stats.stackexchange.com.
Probably better yet, as your statistical skills sound like they are
somewhat limited, consult a local statistician for help.
-- Bert
On Wed, Sep 5, 2012 at 7:54 AM, Vignesh Prajapati wrote:
>
> Hel
Hello folks,
I am on learning phase of R. I have developed Regression Model over six
predictor variables. while development, i found my all data are not very
linear. So, may because of this the prediction of my model is not exact.
Here is the summary of model :
Call:
lm(formula = y ~ x_1 +
For example if nombreC <- nombreC <- c("Juan", "Carlos", "Ana", "María","Mario")
I do not want as a result:
name index
1 Juan 1
2 Juan 5
3 Carlos 2
4Ana 3
5 María 4
6 Mario 0
I want:
name index
1 Juan 1
2 Juan 5
3 Carlos 2
4Ana 3
Hi, if i just want a vector filled with names which has length(index) > 0.
For example if
nombreC <- c("Juan", "Carlos", "Ana", "María")
nombreL <- c("Juan Campo", "Carlos Gallardo", "Ana Iglesias", "María
Bacaldi", "Juan Grondona", "Dario Grandineti", "Jaime Acosta",
"Lourdes Serrano")
I would
On Sat, May 3, 2008 at 9:00 PM, Tobias Erik Reiners
<[EMAIL PROTECTED]> wrote:
> Dear Helpers,
>
> I just started working with R and I'm a bit overloaded with information.
>
> My data is from marsupials reindroduced in a area. I have weight(wt), hind
> foot
> lenghts(pes) as continues variables
Hi Tobias,
If you want to do inferential statistics with groups differing
systematically on the covariate, you will need to be extra careful in
your interpretation. See, e.g., Miller, G. A. & Chapman, J. P.
Misunderstanding Analysis of Covariance, Journal of Abnormal Psychology,
2001, 110, 40
For points 4 and 5, you could use a robust linear fit. One way to do that
is to use rlm() from package MASS, which is used in several examples in
the book that package MASS supports.
On Sun, 4 May 2008, Tobias Erik Reiners wrote:
Dear Helpers,
I just started working with R and I'm a bit ove
Tobi,
I think that it would be easier to provide advice if you were more
explicit on what the model will be used for, and what is the structure
of the data. Is there only one measurement for each marsupial? Is
the goal to
a) produce a model to predict marsupial weight given other variables,
and
Dear Helpers,
I just started working with R and I'm a bit overloaded with information.
My data is from marsupials reindroduced in a area. I have weight(wt),
hind foot
lenghts(pes) as continues variables and origin and gender as categorial.
condition is just the residuals i took from the model
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