Bill et. al.
If I understand correctly, your example does not answer my query. I have
already acknowledged that the data argument is required for nse formula
evaluation. The question is: can with() also be used to evaluate other
arguments, some of which also might be in the formula's environment?.
Dear All,
I am getting error in DESeq installation in R.
package ‘DESeq’ is not available (for R version 3.3.1)
> source("http://www.Bioconductor.org/biocLite.R";)
Bioconductor version 3.4 (BiocInstaller 1.24.0), ?biocLite for help
> biocLite("BiocUpgrade")
Error: Bioconductor version 3.4 cannot
Here is a concrete example where with(data, fit(formula)) differs from
fit(formula, data):
> z1 <- function(myFormula, myData) lm(myFormula, data=myData)
> z2 <- function(myFormula, myData) with(myData, lm(myFormula))
> coef(z1(hp ~ wt, datasets::mtcars))
(Intercept) wt
-1.820922 46.1
On 23/10/2016 3:43 PM, Bert Gunter wrote:
Yes, variables in the formula should be handled by nse with the data
argument. Got it -- thanks. But still ... can with() be used to handle
those and/or any other variables in foo that appear as arguments. I see no
problems in doing so, but ... ?
One po
Thank you for your help. I did try Anthony's recommendation of removing the
'na.action=na.exclude' ; I thought I needed that argument as the data set
includes NA values. I found it interesting that without the
'na.action=na.exclude' argument, the baseline level of two of my predictor
variabl
Yes, variables in the formula should be handled by nse with the data
argument. Got it -- thanks. But still ... can with() be used to handle
those and/or any other variables in foo that appear as arguments. I see no
problems in doing so, but ... ?
Bert
(But see inline below)
On Oct 23, 2016 7:24
The immediate problem could be solved by changing the following lines in
survey:::summary.svrepglm from
presid <- resid(object, "pearson")
dispersion <- sum(object$survey.design$pweights * presid^2,
na.rm = TRUE)/sum(object$survey.design$pweights)
to
presid <- resid(object, "pea
hi, great example. i am ccing survey package author/maintainer dr.
lumley. why do you have `na.action=na.exclude`? if you remove it, things
work as expected--
library(RCurl)
library(survey)
data <- getURL("
https://raw.githubusercontent.com/cbenjamin1821/careertech-ed/master/elsq1a
No. And I don't know why you are conflating the treatment of variables in the
formula with treatment of variables passed as other arguments. It is sort of
like thinking the x symbols in foo$x[ x < 0 ] refer to the same data.
foo$y ~ foo$x1 + foo$x2 + foo$x3 is not preferable, and given the avai
As has been noted oftimes on this list
f( y ~ x1 + x2 + x3 + ... , data = foo, ...)
is much preferable to
f( foo$y ~ foo$x1 + foo$x2 + foo$x3 + ..., ...)
(with no data argument), using nse = non-standard evaluation to set the
environment for formula evaluation. However, as queries here recently
Hello R Users,
I am using Lumley's Survey Package in R to analyze complex survey data that
involves 200 balanced repeated replicate (BRR) weight variables. I have
ensured that my svyrepdesign object that specifies the application of the BRR
weights to the data set is accurate and I have matche
I have informed the vendor that perhaps spyhunter 4 is too aggressive.
They suggested I could make an exclusion - but it turned out to be not
looking for Gator rather than not looking in gtools.dll. I shall inform
if anything appears
BW
Troels
Den 23-10-2016 kl. 11:57 skrev Mateusz Kopyt:
O
One of my student also noticed such report (about Gator) from Spyhunter.
We have checked that the file on the computer is identical to this from
repo.
So probably it was a false alarm. If you have some information from
enigmasoftware, please share on the list.
Best regards
Mateusz Kopyt
WNE UW
You could convert your data from a wide format to a long format using
the reshape function in base R:
DF2 <- reshape(DF, direction="long",
idvar=names(DF)[1:3],
varying=c("site1_elev", "site1_temp", "site2_elev", "site2_temp"),
v.names=c("elev", "temp"),
times=1:2
Please accept my apologies as I was in fact wrong.
It was not my intention to attack minpack.lm or criticize the maintainer.
I like minpack.lm and am fully aware of the effort involved in rewriting the
code. Next time I'll use more careful wording.
Thanks also to Professor Nash for his efforts. I
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