[R] read.alignment() crashes
Hi, I am trying to read a fasta file with >15K alignedd sequences. However it
crashes showing a runtime error. I have tried both R.3.2.3 and R.3.2.1 under
windows 7. A smaller fasta file works perfectly fine in either case.
library(seqinr)
myseqs <- read.alignment("aligned_seqs.fasta",format="fasta")
Thanks,
Eric
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Non-uniformly distributed plot
Hi Dennise, David and Jorge,
I hope you all had a great holiday season. Thank you so much for the good
comments. I think all your suggestions are sufficient for my current needs. I
guess it is safe to say that there is no easy way to plot the data according to
the x-axis range. By the way, spotfire can achieve this beautifully.
Eric
From: Dennis Murphy [mailto:djmu...@gmail.com]
Sent: Friday, December 24, 2010 12:43 PM
To: Eric Hu
Cc: r-help@r-project.org
Subject: Re: [R] Non-uniformly distributed plot
Hi:
One way to combine Jorge's and David's solutions is to visualize the data in
ggplot2 and/or lattice:
library(ggplot2)
x - c(0.349763, 3.39489, 1.52249, 0.269066, 0.107872, 0.0451689,
0.590268, 0.275755, 0.751845, 1.00599, 0.652409, 2.80664, 0.0269933,
0.137307, 0.282939, 1.23008, 0.436429, 0.0555626, 1.10624, 53,
1.30411, 1.29749, 53, 3.2552, 1.189, 2.23616, 1.13259, 0.505039,
1.05812, 1.18238, 0.500926, 1.0314, 0.733468, 3.13292, 1.26685,
3.10882, 1.01719, 0.13096, 0.0529692, 0.418408, 0.213299, 0.536631,
1.82336, 1.15287, 0.192519, 0.961295, 51, 0.470511, 4.05688,
1.78098, 0.364686, 1.24533)
y - c(0.423279, 0.473681, 0.629478, 1.09712, 0.396239, 0.273577,
0.303214, 0.628386, 0.465841, 0.687251, 0.544569, 0.635805, 0.358983,
0.16519, 0.366217, 1.08421, 0.668939, 0.181861, 0.782656, 13.3816,
1.15256, 0.965943, 20, 2.86051, 0.304939, 1.94654, 0.967576,
0.647599, 0.520811, 1.27434, 0.363666, 0.93621, 0.544573, 0.696733,
1.0031, 3.78895, 0.694053, 0.289111, 0.178439, 0.746576, 0.391725,
0.363901, 1.20297, 0.461934, 0.364011, 0.691368, 20, 0.81947,
1.69594, 1.56381, 0.900398, 0.960948)
d - data.frame(x, y)
g - ggplot(d, aes(log(x), log(y))
g + geom_point() + geom_smooth(colour = 'red', size = 1) +
geom_smooth(method = 'lm', colour = 'blue', size = 1)
The default smooth is a loess curve, which shows the curvature present in the
residual vs. fitted plot from Jorge's solution. The predicted values from the
linear model in the log-log scale lie along the blue line. (To get rid of the
confidence curves, add se = FALSE to both geom_smooth() calls above.) If you
were to fit a model to these data in the log-log scale, the plot indicates that
a quadratic polynomial would be a reasonable next step.
This is pretty easy to do in lattice as well (sans the confidence curves):
library(lattice)
xyplot(log(y) ~ log(x), data = d, type = c('p', 'r', 'smooth'),
pch = 16, col = 'black',
panel = function(x, y, ...) {
panel.xyplot(x, y, ..., col.line = 'blue')
panel.loess(x, y, col.line = 'red')
}
)
I needed to write a small panel function to get separate colors for the least
squares line and loess curves, but maybe there's an easier way (col.line =
c('blue', 'red') by itself doesn't work - I tried that - and it makes sense to
me why it doesn't).
Dennis
On Thu, Dec 23, 2010 at 3:50 PM, David Winsemius
dwinsem...@comcast.netmailto:dwinsem...@comcast.net wrote:
On Dec 23, 2010, at 6:41 PM, David Winsemius wrote:
On Dec 23, 2010, at 5:55 PM, Eric Hu wrote:
Thanks David. I am reposting the data here.
Jorge has already responded masterfully. He's apparently less lazy that I and
did all the editing. A log transformation as he illustrated can be very useful
with bivariate skewed distributions. The only variation I would have suggested
would be to record the default par settings and restore them at the end.
You could also repeat the plot an use abline to look at the predicted values
plot(x,y, log=xy)
lines( log(x), fit$predicted)
It's complementary to the residual plot and the QQ plot in the plot.lm display
for consideration of the possibility that this may not be a truly
log-log-linear relationship.
--
David
Eric
Hi,
I would like to plot a linear relationship between variable x and y.
Can anyone help me with scaled plotting and axes so that all data
points can be visualized somehow evenly? Plaint plot(x,y) will
generate condensed points near (0,0) due to several large data
points. Thank you.
Eric
dput(x)
c(0.349763, 3.39489, 1.52249, 0.269066, 0.107872, 0.0451689,
0.590268, 0.275755, 0.751845, 1.00599, 0.652409, 2.80664, 0.0269933,
0.137307, 0.282939, 1.23008, 0.436429, 0.0555626, 1.10624, 53,
1.30411, 1.29749, 53, 3.2552, 1.189, 2.23616, 1.13259, 0.505039,
1.05812, 1.18238, 0.500926, 1.0314, 0.733468, 3.13292, 1.26685,
3.10882, 1.01719, 0.13096, 0.0529692, 0.418408, 0.213299, 0.536631,
1.82336, 1.15287, 0.192519, 0.961295, 51, 0.470511, 4.05688,
1.78098, 0.364686, 1.24533)
dput(y)
c(0.423279, 0.473681, 0.629478, 1.09712, 0.396239, 0.273577,
0.303214, 0.628386, 0.465841, 0.687251, 0.544569, 0.635805, 0.358983,
0.16519, 0.366217, 1.08421, 0.668939, 0.181861, 0.782656, 13.3816,
1.15256, 0.965943, 20, 2.86051, 0.304939, 1.94654, 0.967576,
0.647599, 0.520811, 1.27434, 0.363666, 0.93621, 0.544573, 0.696733,
1.0031, 3.78895, 0.694053, 0.289111, 0.178439, 0.746576, 0.391725,
0.363901
[R] Non-uniformly distributed plot
Hi, I would like to plot a linear relationship between variable x and y. Can anyone help me with scaled plotting and axes so that all data points can be visualized somehow evenly? Plaint plot(x,y) will generate condensed points near (0,0) due to several large data points. Thank you. Eric x [1] 0.3497630 3.3948900 1.5224900 0.2690660 0.1078720 0.0451689 0.5902680 0.2757550 0.7518450 [10] 1.0059900 0.6524090 2.8066400 0.0269933 0.1373070 0.2829390 1.2300800 0.4364290 0.0555626 [19] 1.1062400 53.000 1.3041100 1.2974900 53.000 3.2552000 1.189 2.2361600 1.1325900 [28] 0.5050390 1.0581200 1.1823800 0.5009260 1.0314000 0.7334680 3.1329200 1.2668500 3.1088200 [37] 1.0171900 0.1309600 0.0529692 0.4184080 0.2132990 0.5366310 1.8233600 1.1528700 0.1925190 [46] 0.9612950 51.000 0.4705110 4.0568800 1.7809800 0.3646860 1.2453300 y [1] 0.423279 0.473681 0.629478 1.097120 0.396239 0.273577 0.303214 0.628386 0.465841 [10] 0.687251 0.544569 0.635805 0.358983 0.165190 0.366217 1.084210 0.668939 0.181861 [19] 0.782656 13.381600 1.152560 0.965943 20.00 2.860510 0.304939 1.946540 0.967576 [28] 0.647599 0.520811 1.274340 0.363666 0.936210 0.544573 0.696733 1.003100 3.788950 [37] 0.694053 0.289111 0.178439 0.746576 0.391725 0.363901 1.202970 0.461934 0.364011 [46] 0.691368 20.00 0.819470 1.695940 1.563810 0.900398 0.960948 [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Non-uniformly distributed plot
Thanks David. I am reposting the data here. Eric Hi, I would like to plot a linear relationship between variable x and y. Can anyone help me with scaled plotting and axes so that all data points can be visualized somehow evenly? Plaint plot(x,y) will generate condensed points near (0,0) due to several large data points. Thank you. Eric dput(x) c(0.349763, 3.39489, 1.52249, 0.269066, 0.107872, 0.0451689, 0.590268, 0.275755, 0.751845, 1.00599, 0.652409, 2.80664, 0.0269933, 0.137307, 0.282939, 1.23008, 0.436429, 0.0555626, 1.10624, 53, 1.30411, 1.29749, 53, 3.2552, 1.189, 2.23616, 1.13259, 0.505039, 1.05812, 1.18238, 0.500926, 1.0314, 0.733468, 3.13292, 1.26685, 3.10882, 1.01719, 0.13096, 0.0529692, 0.418408, 0.213299, 0.536631, 1.82336, 1.15287, 0.192519, 0.961295, 51, 0.470511, 4.05688, 1.78098, 0.364686, 1.24533) dput(y) c(0.423279, 0.473681, 0.629478, 1.09712, 0.396239, 0.273577, 0.303214, 0.628386, 0.465841, 0.687251, 0.544569, 0.635805, 0.358983, 0.16519, 0.366217, 1.08421, 0.668939, 0.181861, 0.782656, 13.3816, 1.15256, 0.965943, 20, 2.86051, 0.304939, 1.94654, 0.967576, 0.647599, 0.520811, 1.27434, 0.363666, 0.93621, 0.544573, 0.696733, 1.0031, 3.78895, 0.694053, 0.289111, 0.178439, 0.746576, 0.391725, 0.363901, 1.20297, 0.461934, 0.364011, 0.691368, 20, 0.81947, 1.69594, 1.56381, 0.900398, 0.960948) __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Pipeline pilot fingerprint package
Hi Rajarshi, Here is a post I found from Pipeline pilot community help pages: https://community.accelrys.com/message/3466 Eric -Original Message- From: Rajarshi Guha [mailto:rajarshi.g...@gmail.com] Sent: Wednesday, October 13, 2010 7:52 AM To: Eric Hu Cc: r-help@r-project.org Subject: Re: Pipeline pilot fingerprint package On Tue, Oct 12, 2010 at 8:54 PM, Eric Hu eric...@gilead.com wrote: Hi, I am trying to see if I can use R to perform more rigorous regression analysis. I wonder if the fingerprint package is able to handle pipeline pilot fingerprints (ECFC6 etc) now. Currently no - does Pipeline Pilot out put their ECFP's in a standard format? if so can you send me an example file? (Asuming they output fp's for a single molecule on a single row, you could implement your own line parse and supply it via the lf argument in fp.read. See cdk.lf, moe.lf or bci.lf for examples) The other issue is how one evaluates similarity between variable length feature fingerprints, such as ECFPs. One approach is to map the features into a fixed length bit string. Another approach is to just look at intersections and unions of features to evaluate the Tanimoto score. It seems to me that the former leads to loss of resolution and that the latter could lead to generally low Tanimoto scores. Do you know what Pipeline Pilot does? -- Rajarshi Guha NIH Chemical Genomics Center __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Pipeline pilot fingerprint package
Hi, I am trying to see if I can use R to perform more rigorous regression analysis. I wonder if the fingerprint package is able to handle pipeline pilot fingerprints (ECFC6 etc) now. Thank you, Eric [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Plot time range with rect or boxplot
Hi, I am trying to use rect (R2.11) to plot a set of data as following data CompanyPt Pri Pub 1AWO520 8/5/09 2/11/10 2BWO893 7/30/03 2/24/05 3AWO258 12/8/08 6/17/10 4C WO248 1/13/09 9/2/10 pri- strptime(pri,%m/%d/%y) pub - strptime(pub,%m/%d/%y) plot.new() plot.window(xlim=c(min(pri,pub),max(pri,pub)),ylim=c(0,length(company)-1)) %y - seq(0,0.5*(length(company)-1),0.5) %h - 0.1 %rect(pri, y-h, pub, y+h, col=c(light blue,pink,yellow,red)) Neither xlim nor rect/boxplot recognizes pri/pub with date format. I wonder if there is a good way to deal with the date ploting so the x-axis can reflect the actual time range. Thank you, Eric __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Plot data on year/mon/day
Hi, I am trying to use rect (R2.11) to plot a set of data as following Company Pt Pri Pub A W200 4/5/2009 3/11/2010 B W293 2/30/2003 3/24/2005 A W258 2/8/2008 8/17/2010 C W248 5/13/2009 1/2/2010 %y - seq(0,0.5*(length(company)-1),0.5) %h - 0.1 %rect(pri, y-h, pub, y+h, col=c(light blue,pink,yellow,red)) I wonder if there is a good way to deal with the date conversion so the x-axis can reflect the actual time range. Thank you, Eric [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
