Peter,
Thank you very much for your help.
On Fri, Oct 4, 2024 at 9:19 PM Petr Pikal wrote:
> Hallo
>
> you can extract POSIX object
>
> tv <- as.POSIXct(index(dt_train))
>
> and use cut together with aggregate
> cut(tv, "hour")
>
> aggregate(dt_train, list(cut(tv, "hour")), mean)
>
> 2014-10-06
Hallo
you can extract POSIX object
tv <- as.POSIXct(index(dt_train))
and use cut together with aggregate
cut(tv, "hour")
aggregate(dt_train, list(cut(tv, "hour")), mean)
2014-10-06 21:00:00 9.807692
2014-10-06 22:00:00 8.67
Cheers.
Petr
čt 3. 10. 2024 v 17:25 odesílatel roslinazairimah
Hi
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Kapil Shukla
> Sent: Saturday, March 01, 2014 6:23 PM
> To: r-help@r-project.org
> Subject: [R] Time Series Data Analysis
>
> Hi All
>
>
Hi All
I am totally new to R so this question may sound basic to many of you. I am
trying to use R for time series analysis of some financial instruments.
Currently i have hourly data of a stock which has OPEN/HIGH/LOW/CLOSE in a
CSV file. I used read.table to import the data in R in to a datafram
For a set of data showing seasonality (related to the 4th quarter), ncv
test in R shows p-value of 0.008 which rejects the null hypothesis of
constant-variance. How to apply White's standard error in R?
thanks
__
The i
For a set of data showing seasonality (related to the 4th quarter),
ncv
test shows p-value of 0.008 which rejects the null hypothesis of
constant-variance. Currently a linear LM relationship is being
applied to
the data.
Should white's error be used to correct the non-cons
On 26.08.2011 17:43, Simmons, Ryan wrote:
I am working with data from the USGS with data every 30 minutes from
4/27/2011 to 8/25/2011.
I am having trouble with setting the frequency.
My R script is below:
shavers=read.csv("shavers.csv")
names(shavers)
[1] "agency_cd""site_no"
I am working with data from the USGS with data every 30 minutes from
4/27/2011 to 8/25/2011.
I am having trouble with setting the frequency.
My R script is below:
> shavers=read.csv("shavers.csv")
> names(shavers)
[1] "agency_cd""site_no" "datetime" "tz_cd""Temp"
Hi:
Perhaps something like this, assuming DATE is a Date object (try str(plcm60)
to check) - if not, you need to use as.Date() to convert.
jandays <- data.frame(DATE = seq(as.Date('2003-01-01'), by = 'days', length
= 23))
merge(jandays, plcm60, by = 'DATE', all.x = TRUE)
HTH,
Dennis
On Wed, Nov
I have a stock price dataset a snippet of which is:
> plcm60[1:15, c(1,3,4,5,6,7)]
DATE BIDLO ASKHIPRC VOL RET
1 1/2/03 9.450 9.79 9.700 1531819 0.018907
2 1/3/03 9.670 9.94 9.940 1582192 0.024742
3 1/6/03 9.830 10.05 9.960 1843298 0.002012
4 1/7/03 9.835
> From: ggrothendi...@gmail.com
> Date: Tue, 26 Oct 2010 00:37:05 -0400
> To: flym...@gmail.com
> CC: r-help@r-project.org
> Subject: Re: [R] Time series data with dropouts/gaps
>
> On Tue, Oct 26, 2010 at 12:28 AM, Bob Cunningh
On 10/25/2010 09:37 PM, Gabor Grothendieck wrote:
On Tue, Oct 26, 2010 at 12:28 AM, Bob Cunningham wrote:
I have time-series data from a pair of inexpensive self-logging 3-axis
accelerometers (http://www.gcdataconcepts.com/xlr8r-1.html). Since I'm not
sure of the vibration/shock spectrum I
On Tue, Oct 26, 2010 at 12:28 AM, Bob Cunningham wrote:
> I have time-series data from a pair of inexpensive self-logging 3-axis
> accelerometers (http://www.gcdataconcepts.com/xlr8r-1.html). Since I'm not
> sure of the vibration/shock spectrum I'm measuring, for my initial sensor
> characterizat
I have time-series data from a pair of inexpensive self-logging 3-axis
accelerometers (http://www.gcdataconcepts.com/xlr8r-1.html). Since I'm not
sure of the vibration/shock spectrum I'm measuring, for my initial sensor
characterization run the units were mounted together with the sample rate
On Mar 20, 2010, at 1:17 PM, Gaathier Mahed wrote:
Dear Sir/madam
I am a new user to R. I have no background in coding or even
scripting. It seems as if R would be the best tool in order to analyse
large sets of data. I need to sum hourly readings for the day and then
sum daily into monthly re
Dear Sir/madam
I am a new user to R. I have no background in coding or even
scripting. It seems as if R would be the best tool in order to analyse
large sets of data. I need to sum hourly readings for the day and then
sum daily into monthly readings. i also need to do the same for
another set of d
ciated,
Chris
> CC: r-help@r-project.org
> From: dwinsem...@comcast.net
> To: w_chris_carle...@hotmail.com
> Subject: Re: [R] Time Series Data
> Date: Fri, 27 Nov 2009 10:10:36 -0500
>
>
> On Nov 27, 2009, at 9:55 AM, chris carleton wrote:
>
> >
> > Hi Al
On Nov 27, 2009, at 9:55 AM, chris carleton wrote:
Hi All,
I'm trying to analyze some time series data and I have run into
difficulty. I have decadal sun spot data and I want to separate the
very regular periodic function from the trend and noise. I looked
into using stl(), but the freq
Hi All,
I'm trying to analyze some time series data and I have run into difficulty. I
have decadal sun spot data and I want to separate the very regular periodic
function from the trend and noise. I looked into using stl(), but the frequency
of the time series data must be greater than 1 for s
If you convert your dates to an object d of Date class then
as.numeric(d) will be the number of days since the Epoch. See R News
4/1.
On Mon, Oct 19, 2009 at 2:20 PM, Marlin Keith Cox wrote:
> Hi all and thanks in advance.
>
> I am regressing Time and Weight, and then predicting Weight at
> diff
jullian day?
On Mon, Oct 19, 2009 at 1:20 PM, Marlin Keith Cox wrote:
> Hi all and thanks in advance.
>
> I am regressing Time and Weight, and then predicting Weight at
> different Time. The format of the Time data is day/month/year. How
> can I get R to use time series data such as this?
>
> K
Hi all and thanks in advance.
I am regressing Time and Weight, and then predicting Weight at
different Time. The format of the Time data is day/month/year. How
can I get R to use time series data such as this?
Keith
--
M. Keith Cox, Ph.D.
Alaska NOAA Fisheries, National Marine Fisheries Servi
Check out na.locf in the zoo package. Here we fill in
NAs going forward and just in case there were NAs
right at the beginning we fill them in backward as well.
library(zoo)
x <- as.Date(c(NA, "2000-01-01", NA))
x2 <- na.locf(x, na.rm = FALSE)
x2 <- na.locf(x2, fromLast = TRUE, na.rm = FALSE)
gi
Dear list,
I have some problems with time-series data and missing values of time-invariant
informations like sex or the birth-date.
Assume a data (d) structure like
id birth sex year of observation
1 NA NA 2006
1 1976-01-01 male2007
1
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