Hi Winod,
Your first error message seems to be saying that you are passing a
function that returns an "ohic" object rather than the object. Maybe:
ohic<-get.ohlc.yahoo("GOOG",start="2020-12-18",end="2021-12-17")
candlestickChart(ohic,..)
would get you a bit further. Also it's obvious that the
Note also that there is an r-sig-finance list that would be a better
place to post such queries.
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 Sat, Dec 18,
Please read and follow the Posting Guide linked below, which says,
among other things:
"For questions about functions in standard packages distributed with R
(see the FAQ Add-on packages in R), ask questions on R-help.
[The link is:
Hello,
R users community,
I want to plot candlestickCharts of any stock prices of listed companies
on any stock exchange (Indian or worldwide) into r by using
candlestickchart command in 'FinCal' r package. But I could not plot
candlestic charts. R showed me errors.
Modifying the first example in help('lme', pac='nlme'):
library(nlme)
fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age
fm1$sigma
fm1.2 <- lme(2*distance ~ age, data = Orthodont) # random is ~ age
fm1.2$sigma
Conclusion: Standard deviation, as
dear members,
I am using RSelenium. I have downloaded the java
binary standalone server. I am running it in my windows powershell with the
following command: java -jar selenium-server-standalone-4.0.0-alpha-2.jar
(note that the command doesn't get finished in the
You can run a test. Multiply all your data by a scalar, say 2.
If this changes the result lme_mod$sigma by a factor of 2, then it is
a std deviation.
If it changes the result by a factor of 4, then it is a variance.
HTH,
Eric
On Sat, Dec 18, 2021 at 11:26 AM Courtney Van Den elzen
wrote:
>
> Hi
Hi R-help,
I am a researcher fitting linear mixed models using the package nlme. I am
wondering whether the sigma value that is outputted as part of the model
object is standard deviation or variance? for example, I might fit a model
lme_mod <- nlme::lme(response ~ predictor1 + predictor2,
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