В Thu, 14 Jul 2022 14:58:17 +0200
Uwe Brauer пишет:
> What turns me crazy is that the way R, matlab and the JCR calculate
> the quartiles gives different results.
R by itself can give up to 9 slightly different results:
sapply(1:9, function(type) quantile(1:267, 1:3/4, type = type))
# [,1]
Tim,
Your reply is reasonable if you want to read in EVERYTHING and use various
nice features of the select() function in the dplyr package of the tidyverse
that let you exclude a bunch of columns based on names starting or ending or
containing various characters or not being of type integer and s
Read ?quantile carefully, please (and any references therein that you
may wish to consult).
You are estimating a continuous function by a discrete finite step
function, and as the Help page (and further references) explains,
there are many ways to do this.
Bert
On Thu, Jul 14, 2022 at 2:33 PM U
Hi
I am very acquainted with R. I use it occasionally via the org-babel library of
GNU emacs.
I wanted to check the first, second and third quartiles of the scientific
science index JCR
https://support.clarivate.com/ScientificandAcademicResearch/s/article/Journal-Citation-Reports-Quartile-ra
Hello,
You can use mice() argument predictorMatrix to tell mice() which
variables/blocks are used when imputing which column. If the column
vector is set to zeros, no column or block will used in its imputation.
library(mice)
predmat <- matrix(1L, ncol(nhanes2), ncol(nhanes2),
Dear members,
please feel free to ignore this mail if you feel that it is not about Base R.
I have the following web scraping code ( i have 500
stocks to iterate over):
getFirmsDates <- function() {
rD <- RsDriver(browser="chrome")
Maybe this is too simple but could you use the select() function from dplyr?
Tim
-Original Message-
From: R-help On Behalf Of Bert Gunter
Sent: Thursday, July 14, 2022 2:10 PM
To: Ian McPhail
Cc: R-help
Subject: Re: [R] mice: selecting small subset of variables to impute from
dataset w
If I understand your query correctly, you can use negative indexing to
omit variables. See ?'[' for details.
> dat <- data.frame (a = 1:3, b = letters[1:3], c = 4:6, d = letters[5:7])
> dat
a b c d
1 1 a 4 e
2 2 b 5 f
3 3 c 6 g
> dat[,-c(2,4)]
a c
1 1 4
2 2 5
3 3 6
Of course you have to know
Hello,
I am looking for some advice on how to select subsets of variables for
imputing when using the mice package.
>From Van Buuren's original mice paper, I see that selecting variables to be
'skipped' in an imputation can be written as:
ini <- mice(nhanes2, maxit = 0, print = FALSE)
pred <- in
To be clear, I take no credit for the rather extraordinary function cll shown
below:
mutate(Date = lubridate::dmy_hm(Date))
I would pretty much never have constructed such an interesting and rather
unnecessary line of code.
ALL the work is done within the parentheses:
Date = lubridate::dmy_hm
Dear Bill,
Many thanks ..
Yours sincrely,
AKSHAY M KULKARNI
From: Bill Dunlap
Sent: Thursday, July 14, 2022 1:28 AM
To: akshay kulkarni
Cc: R help Mailing list
Subject: Re: [R] aborting the execution of a function...
You could write a functi
Dear Avi,
THanks a lot...
Yours sincerely,
AKSHAY M KULKARNI
From: R-help on behalf of avi.e.gr...@gmail.com
Sent: Thursday, July 14, 2022 1:39 AM
To: 'R help Mailing list'
Subject: Re: [R] aborting the execution of a function...
Jeff & Akshay
I set out to appeal to this list for help with disentangling
a bewildering anomaly that was produced by some dynamically loaded
Fortran code.
In composing an email to explain the nature of the anomaly, I *FINALLY*
spotted the loony! I had an expression in a nested do loop:
j = npro + (r-1)
13 matches
Mail list logo
