I don't understand most of what you wrote, but when you say "matrix" you are
mistaken. A matrix is NOT the same thing as a data frame, which is what you get
when you call read.csv(). Read
RShowDoc("R-intro")
Sections 5 and 6... A data frame is a list of column vectors, while a matrix is
a vec
Hi
Well, I tried google and this
https://www.jessesadler.com/post/network-analysis-with-r/
seems to provide some insights.
If it is not appropriate to your situation, you should be more specific.
Cheers
Petr
BTW, what is factor in continuous scale? I am almost sure that factors are
discrete
Hi R support group
I have an experimental data of a network analysis problem. One group is
treated and another is controlled (untreated). The response of the
treatment of three factors are measured in ordinal scale and the other two
factors in continuous scale.
I am looking for R code which accou
Hi -
I'm trying to run Krippendorff's alpha for data consisting of 4 subjects
rated on 6 events each by three raters. The ratings are interval ratio
scale data.
I've rearranged my data into a 3 x 24 of ratersXevents. (per this
discussion on CrossValidated: (
https://stats.stackexchange.com/quest
Dear list users,
I am looking for a R package implementing a multinomial logistic
regression with fixed effects (Chamberlain 1980, Review of Economic
Studies 47: 225–238).
Over the years, a number of questions have been asked in the R help
and in stack-related websites in order to find how to use
Dear R users;
I am trying to extract the X and Y coordinates of a polylines along with
Elevation data. I have extracted the Elevations as Z, but I do not know how
to extract the X and Y of these Elevations. Is it possible to extract X and
Y of the Elevation and create a data frame with three variab
I haven't found much call to mess with this, but I think the built-in
"glm" function could do it. You might have to reformulate the inputs to
outcome/observation (ratio) and outcome+observation (weight) to get glm to
accept it [1]... but I am not sure. What I am somewhat more sure of is
that y
I have no idea what you mean when you say "select starting date and ending
date properly form [sic] datai$DATA". For one thing there is no column
called DATA, and for another I don't know what starting dates and ending
dates you might be interested in. If you need help to subset by time,
perhap
Dear Jeff, Dear Rui, Dear all,
I will try Rui's solution as soon as possible.
If I could ask:
As a first step, I would like to follow Jeff's suggestion. I will represent
the precipitation data with a cumulative distribution, one for each year.
This follow that I would like to select the starting
Dear Jim,
Thanks a lot for your stellar replies!
They address my questions perfectly.
Cheers
Lorenzo
On Fri, Jan 25, 2019 at 07:46:50AM -0800, jim holtman wrote:
Try this for the second question:
years <- map2(zz,
+ list(c(2000, 2001), c(2001, 2003)),
+ ~ filter(
Very succinct, Rui!
One warning to Diego automatic data recorders tend to use the local
standard timezone year-round. R by default assumes that timestamps converted
from character to POSIXct using the current timezone on your computer... which
may not be in the same zone that the logger was
Hello,
See if the following can get you started.
It uses package CRAN zoo, function as.yearmon.
dati$MES <- zoo::as.yearmon(dati$DATAORA)
PMES <- ave(dati$PREC, dati$MES, FUN = cumsum)
plot(dati$DATAORA, PMES)
Hope this helps,
Rui Barradas
Às 15:25 de 27/01/2019, Diego Avesani escreveu:
De
Are you looking for a plot where each point represents a month? Or a plot
where each point represents the accumulated precipitation so far that month?
The latter seems closer to your computations so far, but doesn't seem like a
typical way to present precipitation data...
On January 27, 2019 7
Dear all,
I have a set of data with has hourly value:
# ID
# Lo
# L
# Q
Time,T, RH,PSFC,DIR,VEL10, PREC, RAD, CC,FOG
-mm-dd hh:mm, °C, %, hPa, °N, m/s, mm/h,W/m², %,-
2012-01-01 06:00, -0.1,100, 815,313, 2.6, 0.0, 0, 0,0
2012-01-01 07:00, -1.2, 93, 814,314, 4.8, 0.0, 0, 0,0
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