Displaying 20 results from an estimated 10000 matches similar to: "linear regression, exclude a datum"
2012 Aug 07
2
What is this called? lapply(datum,"[[","ColumnName")
Hello R users
I recently learned how to use this command:
lapply(datum,"[[","ColumnName")
Unfortunately, I don't know how exactly it works, what it's called (in
particular the "[[" part], and what other things you can do with it
(retrieve multiple columns?).
Given datum is a list of dataframes with the same column, but different
number of rows,
2009 Sep 25
2
synchronisation of time series data using interpolation
Readers,
I have data with different time stamps that I wish to plot (for example):
data set 1
time(hh:mm:ss),datum
01:00:00,500
01:00:15,600
01:00:30,750
01:00:45,720
01:01:00,700
01:01:15,725
01:01:30,640
01:01:45,710
data set 2
time,datum
01:00:12,20
01:01:01,55
01:01:55,22
The time interval in data set 1 does not change, but the time interval
in data set 2 does change, such that for a
2009 Jul 10
1
assessing data variation
I have data like so:
time datum
30 12
60 24
90 37
120 41
150 8
In addition to standard deviation, I want to measure the average of
the differences in data for each time interval, i.e. average of 24-12,
37-24, 41-37, 8-41. Is there a statistical term for this task? Which
package should I use please?
rhelp at conference.jabber.org
2008 Jun 11
2
model simplification using Crawley as a guide
Hello,
I have consciously avoided using step() for model simplification in favour
of manually updating the model by removing non-significant terms one at a
time. I'm using The R Book by M.J. Crawley as a guide. It comes as no
surprise that my analysis does proceed as smoothly as does Crawley's and
being a beginner, I'm struggling with what to do next.
I have a model:
lm(y~A * B *
2010 Jul 06
1
information reduction-database management question
If you redefine your NAs as below to be detected as some arbitrary large
number, then the code should work through. Any 5's left in your dataset can
be replaced just as easily by NAs again. Not elegant, but effective.
site <- c("s1", "s1", "s1", "s2","s2", "s2")
pref <- c(1, 2, 3, 1, 2, 3)
R1 <- c(NA, NA, 1, NA,NA,NA)
R2
2014 Dec 12
2
Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: 26.
Anna Crepes: Traubenzucker
+ Feldsalat spezielles Dressing (bringt selbst mit?)
-------- Weitergeleitete Nachricht --------
Betreff: Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: Fwd: 26.
Datum: Thu, 11 Dec 2014 15:34:39 +0100
Von: Markus <universe at truemetal.org>
An: universe at truemetal.org
Geschenke Moritz: dunkle Schokolade.
Geschenke Anna: normale Schokolade.
-------- Weitergeleitete
2008 Jul 15
1
code reduction (if anyone feels like it)
# I am sure that I could be more efficient than this but how? Thanks in
advance.
#GPS in Decimal Degrees in the form longitude latitude
RM215 <- matrix(c(-82.1461363, 33.5959109), nrow=1)
SC <- matrix(c(-82.025888, 33.606454), nrow=1)
RM202 <- matrix(c(-81.9906723, 33.5027653), nrow=1)
RM198 <- matrix(c(-81.926823, 33.4634678), nrow=1)
HC <- matrix(c(-81.920505, 33.46192), nrow=1)
2003 Feb 13
1
fixed and random effects in lme
Hi All,
I would like to ask a question on fixed and random effecti in lme. I am
fiddlying around Mick Crawley dataset "rats" :
http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/
The advantage is that most work is already done in Crawley's book (page 361
onwards) so I can check what I am doing.
I am tryg to reproduce the nested analysis on page 368:
2011 Mar 10
2
Selecting ranges of dates from a dataframe
Hello list!
I have a data.frame which looks like this:
> serv
datum op.read op.write read write
1 2011-01-29 10:00:00 0 0 0 0
2 2011-01-29 10:00:01 0 0 0 0
3 2011-01-29 10:00:02 0 0 0 0
4 2011-01-29 10:00:03 0 4 0 647168
5 2011-01-29 10:00:04 0 0 0 0
6 2011-01-29
2010 Oct 19
1
Doubt on using lattice
Hi all,
I suppose this is a very simple question, but as I've lost already a bit of
time with it, without being able to get what I wanted, I'm addressing the
question to the group in the hope someone can help me.
I pretend to plot the richness of herbaceous species (RichHN) as a function
of time since remnant isolation (Isol) conditioned to the area of the
remnant (fArea - this is a
2011 Nov 30
2
nls help
Hello,
I have data like the following:
datum <- structure(list(Y = c(415.5, 3847.83333325, 1942.833333325,
1215.22222233333,
950.142857325, 2399.5833335, 804.75, 579.5, 841.708333325, 494.053571425
), X = c(1.081818182, 0.492727273, 0.756363636, 0.896363636,
1.518181818, 0.499166667, 1.354545455, 1.61, 1.706363636, 1.063636364
)), .Names = c("Y", "X"), row.names = c(NA,
2005 Aug 02
3
how to print a data.frame without row.names
Dear All,
is there a simple way to print a data.frame without its row.names?
example:
datum <- as.Date(c("2004-01-01", "2004-01-06", "2004-04-12"))
content <- c('Neujahr', 'Hl 3 K.', 'Ostern')
df1 <- data.frame(datum, content)
print(df1)
datum content
1 2004-01-01 Neujahr
2 2004-01-06 Hl 3 K.
3 2004-04-12 Ostern
Can I get
2007 Dec 12
1
how to shorten elements in a data frame?
I have a data frame ("res$data" in the example below), and I'd like to walk
through it, doing an approx() to interpolate each element in turn based on
the value of the item named "pressure" that is in the data.frame.
I've put some code below. I had hoped that the commented-out "length() <-"
line would let me adjust the lengths of the items, but that has
2012 Oct 18
1
spTransform longlat to utm
Dear all,
I am trying to project my LongLat-maps to a plane.
The ultimate purpose is to do a search of points in vicinity of other points using overlay-commands (sp) with radius in km.
I am applying spTransform (package rgdal) and it gives my some curious results.
An example.
Let's take a point lying somewhere in Germany, zone=32U
x <- 8.968735
y <- 49.454735
After conversion I
2009 May 27
1
R Books listing on R-Project
I was wondering what the criteria were for including books on the Books
Related to R page <http://www.r-project.org/doc/bib/R-books.html>. (There is
no maintainer listed on this page.)
In particular, I was wondering why the following two books are not listed:
* Andrew Gelman, Jennifer Hill, *Data Analysis Using Regression and
Multilevel/Hierarchical Models*. (CRAN package 'arm')
*
2008 Dec 20
2
NA, where no NA should (could!) be!
Hello,
again I'm on my weblog-script... having problems...
This code:
===========================
weblog <- read_weblog("web.log")
weblog_by_date <- split(weblog, weblog$date)
#for ( i in names(weblog_by_day) ) { print(i); print(weblog_by_day$i) }
for ( datum in names(weblog_by_date) )
{
print(datum)
selected <- weblog_by_date[[datum]]
res_size_by_host <-
2007 Aug 28
1
subcripts on data frames (PR#9885)
I'm not sure if this is a bug, or if I'm doing something wrong.
=20
=46rom the worms dataframe, which is at in a file called worms.txt at
=20
http://www.imperial.ac.uk/bio/research/crawley/therbook
<http://www.imperial.ac.uk/bio/research/mjcraw/therbook/index.htm>=20
=20
the idea is to extract a subset of the rows, sorted in declining order
of worm density, with only the maximum
2006 Nov 14
2
Repeated measures by lme and aov give different results
I am analyzing data from an experiment with two factors: Carbon (+/-)
and O3 (+/-), with 4 replicates of each treatment, and 4 harvests over a
year. The treatments are assigned in a block design to individual
Rings.
I have approaches this as a repeated measures design. Fixed factors
are Carbon, O3 and Harvest, with Ring assigned as a random variable. I
have performed repeated measures analysis
2004 Nov 03
3
cut POSIX results in NA - bug?
Dear all
I try to make hourly average by cut() function, which almost works
as *I* expected. What puzled me is that if there is only one item at
the end of your data it results in NA.
Example will explain what I mean
datum<-seq(ISOdate(2004,8,31), ISOdate(2004,9,1), "min")
cut(datum[1370:1381],"hour", labels=F)
[1] 1 1 1 1 1 1 1 1 1 1 1 NA
2006 Nov 23
1
nonlinear regression-getting the explained variation
Hi,
I'm trying to teach myself R, and by the way, re-learning statistics using
Crawley's "Statistics: an introduction using R".
I've reached the regression chapter, and when it deals with non-linear
regresion using the nls library I face the following problem:
I follow the steps---
>deer<-read.table("c:\\temp\\jaws.txt",header=T)
---data available at