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Stephen Clark | 18 Jan 10:12
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Modelling data at two separate locations

Hello,

I am looking for advice on what kind of models I can use for my data.

The dependent (y) variable are count data (poisson) and are located at an irregular pattern. The
independent data (x1, x2, x3, ...) are based on regions (closed polygons) (but could be represented at the
centroids of these regions if this helped, but these centroid will not correspond to the location of the
y's). My problem is that I do not have an exact (other than geographical) data match between the y's and x's.
In particular I have about 8,000 y observation points and 20,000 x regions.

What I would like to examine is how the proximity of various regions (via their x1, x2, x3, ... values)
influenced the value of my y variable. I have looked at some common spatial models (SEM, SAR and GWR) but
they all require the y and x data to be observed at the same locations. What I have done is to aggregate my y
counts to the region geography, so that I have for each region a y aggregated count and the corresponding
x1, x2, x3, ... , but I have lots of missing values where a region has no y count, and my concern is that these
missing values will mean I lose the information about these regions in forming my relationship.

Thanks.

--
Stephen Clark,
First year PhD,
School of Geography
0113 343 6707

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Spencer, Neil | 11 Nov 18:00
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COMPUTING FOR GRAPHICAL MODELS, 16 December 2011, RSS, London

Computing for Graphical Models

16 December 2011

Royal Statistical Society,  London

One day meeting of the Statistical Computing Section

Details and registration at

http://tinyurl.com/RSS16December

Graphical models has expanded substantially over the past decade with the analysis of large data sets, in
particular from bioinformatics and retail, with developments of inference in relation to causality,
and with applications involving complex data structures. The ubiquitous nature of conditional
independence has meant these models are applied in many different subjects. Computing for graphical
models has always been difficult but recently user friendly open source software has become available.

This meeting provides a platform to review the current provision and to elucidate remaining challenges in
making graphical modelling more accessible to the wider scientific community.

Contact: joe.whittaker <at> lancaster.ac.uk<mailto:joe.whittaker <at> lancaster.ac.uk>

==============================================================
Dr Neil H. Spencer
Principal Lecturer in Statistics
Director of Statistical Services and Consultancy Unit

Business School, University of Hertfordshire,
de Havilland campus, Hatfield,
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David Bird | 20 Apr 22:20
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succession graphic

I would like to produce charts of phytoplankton biomass changes 
through time. Each species has a line, and the biomass varies in 
mirror form along the line for each species along the X time axis. 
Here is an example of what I'd like to do: 
http://www.er.uqam.ca/nobel/r30240/Succession.jpg

Thanks
David Bird
UQAM, Montreal

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Chun Wang | 23 Feb 20:10
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mai in par

Dear R users,

In using par, it seems one can adjust the plot location using mai. but what
is the unit for the number used in mai? Can I normalize it to the interval
(0,1)? Thank you very much for any help in advance.

with best regards,

Chun

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FMH | 25 Feb 17:01
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How to add a title to represent four different plot in lm function

Dear All,

A linear regression model could be fitted by using lm function and the plot function can be used to check the
assumptions of the model. The example is as followed.

require(graphics)
## Annette Dobson (1990) "An Introduction to Generalized Linear Models".
## Page 9: Plant Weight Data.
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2,10,20, labels=c("Ctl","Trt"))
weight <- c(ctl, trt)
anova(lm.D9 <- lm(weight ~ group))
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(lm.D9, las = 1)     

Could someone advice me the way to add a single title either at the above or bottom of these 4 plots, entitled
"The verification of model assumtion via four different plots" ?

Thanks
Fir
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FMH | 8 Feb 18:45
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Color intervals in image.plot function


Hi,

The script below is my current coding in order to produce a contour plot of temperature across altitude and
time. In my case, time,altitude and temperature are represented by x, y and z variables.

##############################################
Brazilan.Pallete <- colorRampPalette(c("blue","green","yellow","red"))
image.plot(x, y, z, col = Brazilan.Pallete(50))
contour(x,y,z, levels = seq(1, 40, by = 1), add = TRUE, col = 'peru')
##############################################

The plot worked fine but i found difficult to fix the interval of the color corresponding to z value. In
my case, the range of z values is between 1 and 40 and  i'd like to fix the color in the image correspoding
to four sub-intervals of z values. For instance:

1. 1 < z < 10 : blue
2. 11 < z < 20: green
3. 21 < z < 30: yellow
4. 31 < z < 40 : red.

I did't find a suitable code to do this. Could someone please give an advice on this matter?

Thank you.
Fir
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Lu, Kevin | 27 Jan 08:12
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meta-analysis

To someone who could help me:

I am a PhD student, working in the Wageningen University, the
Netherlands.  I am now using metafor package to do meta-analysis. 

Since there are few studies conducted on my topic, I have to combine the
results from 5 case-control studies and 1 cohort study. But I do not
know how to do it.

I tried this way, first, use the escalc (measure="OR", ai, bi, ci, gi)
to calculate the effect estimate of yi and variance of vi.

I could extract the RR and 95% confidence interval from the cohort study
to get the point estimate of LnRR and SE.

but I can not combine the results from case-control study and the
results from cohort study due to the difference of vi and SE.

For example, if I use rma(yi, vi, data,), I can not get vi (variance)
for the cohort study. I can only get SE for cohort study. How could
solve it?

Thanks for your time.

Best wishes,

Yingchang (Kevin) Lu

PhD student,
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FMH | 3 Sep 19:28
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color index in image function

Dear All,

I was looking for the color index in image function, such as from topo.colors and etc. but still never
found it. For instance, from the help menu.

###########################################
# Volcano data visualized as matrix. Need to transpose and flip
# matrix horizontally.
image(t(volcano)[ncol(volcano):1,])

# A prettier display of the volcano
x <- 10*(1:nrow(volcano))
y <- 10*(1:ncol(volcano))
image(x, y, volcano, col = terrain.colors(100), axes = FALSE)
contour(x, y, volcano, levels = seq(90, 200, by = 5),
        add = TRUE, col = "peru")
axis(1, at = seq(100, 800, by = 100))
axis(2, at = seq(100, 600, by = 100))
box()
title(main = "Maunga Whau Volcano", font.main = 4)
#########################################

>From the script above, it yields a beautiful  image of volcano with variety of colors but i have to list
down the color index that could show the meaning of each color in my thesis. 

Could someone please help me to extract this color index?

Thank you
Fir
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FMH | 20 Aug 17:59
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contour plot

Hi,

Could someone give some ideas on plotting a contour by using geoR package, please?

Thank you

Kagba

      
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Matthias Buehlmaier | 1 Jul 09:22
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Re: deal package

Hi Kevin, 

You mentioned that your problem is the following: bnlearn cannot cope with both discrete and continuous
variables. 

One potential solution would be to simply discretize your continuous variables using some (not
necessarily equidistant) grid. I am unsure whether or not this is the best solution, but it should work. 

Cheers, 

Matthias 

Lu, Kevin wrote:
> Dear all,
> 
> I learn something from my colleagues about the beauty of Bayesian
> network. I want to use the deal package to analyze my genetic data. I
> have 3575 subjects. For each subject, I have 20 genetic marker
> information (discrete, three levels) and his or her plasma cholesterol
> levels (continuous). I want to construct the markov blanket of plasma
> cholesterol levels. I tried the deal in my computer and computer
> cluster. Due to so many genetic markers, I can not successfully pass the
> calculation of the prior probability of my network. The maximum
> variables I could put into the network is about 12. I can not add more.
> 
> The algorithms (gs, iamb, hc) in bnlearn package can not deal the
> discete and continuous variables at the same time. Could someone give me
> some suggestions how I should do it? 
> 
> Thanks in advanve,
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jaime chumacero | 24 Jun 23:25
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how have software R?

I'll like have this software, but also this manuals y other

Jaime Chumacero 

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Descarga gratis el nuevo Internet Explorer 8. 

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