search for: quadrats

I'm trying out a basic plot, but something about the way I subset my data leads to problems with the plot. Here is the first bit of my data set year,date,location,quadrat_juvenile,photo_location,photo_exists,genus,count,divers 2005,2005-04-30, 1 Fringing Reef,1, 1 Fringing Reef Coral Transect Pole 1-2 Quadrat 1,t,Acanthastrea,0,HP+MEM 2005,2005-04-30, 1 Fringing Reef,1, 1 Fringing Reef

Hi R experts, Could you please help me to fit a linear, cubic and quadratic curve in a figure? I was trying to show all these three fitting curves with different colour in one figure. I spent substantial time to figure it out, but I could not. I have given here a example and what I did for linear, but no idea for cubic and quadratic fitting curve > dput(test) structure(list(sp = c(4L, 5L,

Hi all, I am using quadratcount in spatstat to divide a window containing a point pattern into a grid of quadrats containing the intensity of points in each quadrat. My data is in UTM co-ordinates. My window is defined as follows: >p15<-ppp(x,y,window=owin(c(341710,342100),c(3126465,3126780)),marks=NUL L, checks=TRUE) Giving me a distance of 390m in the 'x' direction and 315m in the 'y...

...a follow up - what would be the most appropriate model formula (using glmmPQL syntax) to specify both a nested facor and repeated observations? Specifically, I am dealing with experimental data with three factors. ZONE is a fixed effect. Three sites (SITE) are nested within each ZONE. Multiple quadrats within each SITE are measured across multiple years. I want to represent the nesting of SITE within ZONE and allow for repeated observations within each QUADRAT over time (the YEAR | QUADRAT random effect). -- I am assuming that glmmPQL is the best option at this point because of recent discussio...

> > >> > LVI is one of those analyses with quadratic runtime, but has a cutoff to > its search depth so that it is technically not quadratic. So increased > inlining could easily exacerbate it more than non-"quadratic" passes. > (increased inlining would also cause a general slowdown too). > > LVI is only quadratic because of the way we've built it

Hi, I need to maximize a quadratic function under constraints in R. For minimization I used solve.QP but for maximization it is not useful since the matrix D of the quadratic function should be positive definite hence I cannot simply change the sign. any suggestion ? thanks -- View this message in context:

...ry to insert the character value via the function call, it doesn't work. I don't get an error, but the TotalCover.df dataframe does not update according to the loop criteria. Any obvious problems that you can see? ################Cover Function######################### #Define Variables Quadrats.df<-unique(data.df$Quadrat) TotalCover.df<-cbind(0:750/10,0,0,0,0,0,0) colnames(TotalCover.df)<- c("Station","Shrub","Tree","Invasive","Herb","Litter","Bare") Shrub.df<-data.df[data.df$Layer=="Shrub",] T...

How do I calculate the confidence interval for the value x given by the intersection of two quadratics (i.e. parabolas)? I fit two quadratics of the form: y = C1 + B1*x + A1*x^2 y = C2 + B2*x + A2*x^2 to two sets of points N1 and N2. I test for whether they intersect, if they do then I calculate the roots of: 0 = (C1 - C2) + (B1 - B2)*x + (A1 - A2)*x^2 to determine where they intersect

Hi, Does anybody have experience to solve an quadratic programming problem with quadratic constraints in R? It seems that the package "quadprog" only handles the quadratic programming with linear constraint. My probelm is to maximze x^T\Sigma_{xy} y, subject to x^Tx=1, y^T\Sigma_{yy} y=1, and sum(y)<t, or sum(y)=t, where x and y are the variable, and the Sigma's and t are

HI All, I am unable to solve a optimization Problem Please Help Me out of this to solve. The Optimization problem is as follows :- My objective function is linear and one of the constraint is quadratic. Min z = 5 * X1 + 9* X2 + 7.15 *X3 + 2 * X4 subject to X1 + X2 + X3 +X4 = 9 X1 + X4 < = 6.55 X3(X3 - 3.5) >=0 X1,X2,X3,X4 >=0 Now the problem is how to solve this kind of

Hello, I have a system of quadratic equations (results of a Hamiltonian optimization) which I need to find the roots for. Is there a package and/or function which will find the roots for a quadratic system? Note that I am not opimizing, but rather solving the first order conditions which come from a Hamiltonian. I am basically looking for something in R that will do the same thing as fsolve in

I was playing around with a simple example using solve.qp ( function is in the quadprog package ) and the code is below. ( I'm not even sure there if there is a reasonable solution because I made the problem up ). But, when I try to use solve.QP to solve it, I get the error that D in the quadratic function is not positive definite. This is because Dmat is zero because I don't have a

Hi, I need to solve an optimization problem in R having linear objective function and quadratic constraints(number of variables is around 80). What are the possible choices to do this in R. optim() function only allows box constrained problems. Is it possible in nlm()? Or please tell me if there is any other routine. Thanks Amit

...tographs of plots that look like so: http://r.789695.n4.nabble.com/file/n4631960/Untitled.jpg I need to divide it up so each circle has an equal area surrounding it. So into 20 equal segments, each of which contains a circle. Quadratcount is not sufficient because if I divide it up into 36 equal quadrats, some quadrats do not contain one of the circles. I'm not even sure how to do it mathematically, let alone using R. Can anyone help? -- View this message in context: http://r.789695.n4.nabble.com/Quadrat-counting-with-spatstat-tp4631960.html Sent from the R help mailing list archive at Na...

...h the window (an entire country) and the points are projected using WGS84. When I do quadratcount with only one quadrat for the entire country it holds all my 154 points: test <- quadratcount(ppp_cameroon, nx=1, ny=1) plot(test) sum(test) [1] 154 But when I split the country up into several quadrats (which is the only thing that makes sense for the statistics), the function returns results that are plainly wrong. The counts always add up to 154, but when overlaid with the actual points it looks like some points are not at all counted (0 count quadrats where points are) and some count value...

Dear list users How is it possible to visualise both a linear trend line and a quadratic trend line on a plot of two variables? Here my almost working exsample. data(Duncan) attach(Duncan) plot(prestige ~ income) abline(lm(prestige ~ income), col=2, lwd=2) Now I would like to add yet another trend line, but this time a quadratic one. So I have two trend lines. One linear trend line

-----Original Message----- Message: 6 Date: Mon, 20 Jan 2003 01:11:24 +0100 From: Martin Michlmayr <tbm at cyrius.com> To: r-help at stat.math.ethz.ch Subject: [R] quadratic trends and changes in slopes I'd like to use linear and quadratic trend analysis in order to find out a change in slope. Basically, I need to solve a similar problem as discussed in

...4 different sources of mortality) for 5 months . I need to test 1) if the overall proportion of these different fates is different over the entire study and 2) to see if there are monthly differences within (and among) fate types. Thus, I am pretty sure this is an RM analysis -( I measure the same quadrats each month). I am fine running the analysis in R - with the code below, however, there is no output for the among group variation...this is an important component - any ideas on how to solve this problem? I have included code and sample data below. Many thanks in advance for help and suggestions...

Hello, I have been trying to take the derivative of a quadratic B-spline obtained by using the COBS library. What I would like to do is similar to what one can do by using fit<-smooth.spline(cdf) xx<-seq(-10,10,.1) predict(fit, xx, deriv = 1) The goal is to fit the spline to data that is approximating a cumulative distribution function (e.g. in my example, cdf is a 2-column matrix with x

Good day for all, I'm a beginner aRgonaut, thus I'm having a problem to plot a quadratic model of regression in a plot. First I wrote: >plot(Y~X) and then I tried: >abline(lm(Y~X+I(X^2)) but "abline" only uses the first two of three regression coefficients, thus I tried: >line(lm(Y~X+I(X^2)) but a message error is showed ("insufficient observations").