On 12/21/21 6:00 PM, Kai Yang via R-help wrote:
Hi team,I'm trying to pass macro variable into R script in Proc iml. I want to do change
variable in color= and export the result with different file name.If I don't use macro,
the code work well. But when I try to use macro below, I got error
Hi team,I'm trying to pass macro variable into R script in Proc iml. I want to
do change variable in color= and export the result with different file name.If
I don't use macro, the code work well. But when I try to use macro below, I got
error message: "Submit block cannot be directly placed in
Jim,
there are indeed many mathematical areas where data are not quite fixed.
Consider inequalities such as a value that can be higher than some number but
lower than another. A grade of A can often mean a score between 90 and 100 (no
extra credit). An event deemed to be "significant at the
Hello all,
My two cents. We use the term "below the detection limit" for any
physical measurement that is cannot be distinguished from noise in the
measurement system. This may either be instance specific (determine the
detection limit for each instance) or "below the reporting limit" which
Hi Bert,
What troubles me about this is that something like detectable level(s)
is determined at a particular time and may change. Censoring in
survival tells us that the case lasted "at least this long". While a
less than detectable value doesn't give any useful information apart
from perhaps
But you appear to be missing something, Jim -- see inline below (and
the original post):
Bert
On Tue, Dec 21, 2021 at 2:00 PM Jim Lemon wrote:
>
> Please pardon a comment that may be off-target as well as off-topic.
> This appears similar to a number of things like fuzzy logic, where an
>
Please pardon a comment that may be off-target as well as off-topic.
This appears similar to a number of things like fuzzy logic, where an
instance can take incompatible truth values.
It is known that an instance may have an attribute with a numeric
value, but that value cannot be determined.
It
Stephen:
You seem confused about data frames. sort(unique(...)) has no problem
sorting individual columns in a data frame (mod the issues about
mixing numerics and non-numerics that have already been discussed).
But the problem is that the results can *not* be put back in a data
frame because,
It is not about outlawing matrix notation... to the contrary, it is about
consistency. For tibbles, [] always returns another tibble. If you wanted a
column vector, you should have asked for a column vector. Does the fact that
DF[ 1, ] yields a different type than DF[ , 1 ] and DF[ 1:2, ]
I wonder if the package Adrian Dușa created might be helpful or point you along
the way.
It was eventually named "declared"
https://cran.r-project.org/web/packages/declared/index.html
With a vignette here:
https://cran.r-project.org/web/packages/declared/vignettes/declared.pdf
I do not know
Dear Jeff,
I haven't investigated your claim systematically, but out of curiosity, I did
try extending my previous example, admittedly arbitrarily. In doing so, I
assumed that you intended col in the first case to be the column subscript, not
the row subscript. Here's what I got (on a newish
Duncan,
Let's not go there discussing the trouble with tibbles when the topic asked how
to do things in more native R.
The reality is that tibbles when used in the tidyverse often use somewhat
different ways to select what columns you want including some very quite
sophisticated ones like:
Stephen,
Languages have their own philosophies and are often focused initially on doing
specific things well. Later, they tend to accumulate additional functionality
both in the base language and extensions.
I am wondering if you have explained your need precisely enough to get the
answers
On 21/12/2021 12:53 p.m., Duncan Murdoch wrote:
On 21/12/2021 12:29 p.m., Jeff Newmiller wrote:
It is a very rational choice, not a design flaw. I don't like every choice they
have made for that class, but this one is very solid, and treating data frames
as lists of columns consistently helps
When your brain is wired to treat a data frame like a matrix, then you think
things like
for ( col in colnames( col ) ) {
idx <- expr
D[ col, idx ] <- otherexpr
}
are reasonable, when
for ( col in colnames( col ) ) {
idx <- expr
D[[ col ]][ idx ] <- otherexpr
}
does actually run
On 21/12/2021 12:29 p.m., Jeff Newmiller wrote:
It is a very rational choice, not a design flaw. I don't like every choice they
have made for that class, but this one is very solid, and treating data frames
as lists of columns consistently helps all of us.
I think outlawing matrix notation is
It is a very rational choice, not a design flaw. I don't like every choice they
have made for that class, but this one is very solid, and treating data frames
as lists of columns consistently helps all of us.
On December 21, 2021 9:02:56 AM PST, Duncan Murdoch
wrote:
>On 21/12/2021 11:59
Dear Jeff,
On 2021-12-21, 11:59 AM, "R-help on behalf of Jeff Newmiller"
wrote:
Intuitive, perhaps, but noticably slower.
I think that in most applications, one wouldn't notice the difference; for
example:
> D <- data.frame(matrix(rnorm(1000*1e6), 1e6, 1000))
> microbenchmark(D[, 1])
On 21/12/2021 11:59 a.m., Jeff Newmiller wrote:
Intuitive, perhaps, but noticably slower. And it doesn't work on tibbles by
design. Data frames are lists of columns.
That's just one of the design flaws in tibbles, but not the worst one.
Duncan Murdoch
On December 21, 2021 8:38:35 AM PST,
Intuitive, perhaps, but noticably slower. And it doesn't work on tibbles by
design. Data frames are lists of columns.
On December 21, 2021 8:38:35 AM PST, Duncan Murdoch
wrote:
>On 21/12/2021 11:31 a.m., Duncan Murdoch wrote:
>> On 21/12/2021 11:20 a.m., Stephen H. Dawson, DSL wrote:
>>>
On 21/12/2021 11:31 a.m., Duncan Murdoch wrote:
On 21/12/2021 11:20 a.m., Stephen H. Dawson, DSL wrote:
Thanks for the reply.
sort(unique(Data[1]))
Error in `[.data.frame`(x, order(x, na.last = na.last, decreasing =
decreasing)) :
undefined columns selected
That's the wrong syntax:
On 21/12/2021 11:20 a.m., Stephen H. Dawson, DSL wrote:
Thanks for the reply.
sort(unique(Data[1]))
Error in `[.data.frame`(x, order(x, na.last = na.last, decreasing =
decreasing)) :
undefined columns selected
That's the wrong syntax: Data[1] is not "column one of Data". Use
Data[[1]]
Thanks for the reply.
sort(unique(Data[1]))
Error in `[.data.frame`(x, order(x, na.last = na.last, decreasing =
decreasing)) :
undefined columns selected
The recommended syntax did not work, as listed above.
What I want is the sort of distinct column output. Again, the column may
be text
On 21/12/2021 10:16 a.m., Stephen H. Dawson, DSL via R-help wrote:
Thanks everyone for the replies.
It is clear one either needs to write a function or put the unique
entries into another dataframe.
It seems odd R cannot sort a list of unique column entries with ease.
Python and SQL can do it
Thanks everyone for the replies.
It is clear one either needs to write a function or put the unique
entries into another dataframe.
It seems odd R cannot sort a list of unique column entries with ease.
Python and SQL can do it with ease.
QUESTION
Is there a simpler means than other than
Say 'yi' is left censored. Then:
# naive regression model
res1 <- lm(yi ~ xi, data=dat)
# tobit model via survreg()
res2a <- survreg(Surv(yi, yi > censval, type="left") ~ xi, dist="gaussian",
data=dat)
# tobit model via tobit() from AER package
res2b <- tobit(yi ~ xi, left=censval, data=dat)
Many thanks Wolfgang,
I guess I can see that survival analyses don't have to be time based but
clearly I need to read up on that. I can't see an example in the survival
package. And it proves to be hard to search for one. Can anyone point me
to useful resources on that, in {survival} or not?
Hi Chris,
The survival package provides machinery for handling censored observations.
Whether time is censored or some other type of variable (e.g., viral load due
to some lower detection limit) does not make a fundamental difference. In fact,
the type of model you are thinking of with 2) is a
I am neither a programmer nor a professional statistician but this topic
interests me because:
1) I remember from long, long ago that S had a way to create labels that could
denote multiple ways
in which a value could be missing that was sometimes useful to me as my
field sometimes has
On 20/12/2021 11:41 p.m., Marc Girondot via R-help wrote:
Dear members,
I work about dosage and some values are bellow the detection limit. I
would like create new "numbers" like LDL (to represent lower than
detection limit) and UDL (upper the detection limit) that behave like
NA, with the
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