search for: abscissa

Is it easy or difficult to label the abscissa of a scatter graph as 1/trueScaleValue at that point? -- View this message in context: http://r.789695.n4.nabble.com/labeling-abscissa-using-a-function-of-the-plotted-scale-tp4678075.html Sent from the R help mailing list archive at Nabble.com.

...4, 3, 4, 5) xymodel <- lm( y~x) boxplot( y~x) abline( xymodel) # Wrong abcissae abline( v = 2.5) # Wrong abcissa abline( h = 2.75) # Right ordinate # -------------- end -------------- Here, I'm making a box plot with abscissae that start at 2. The box plot looks fine: the numbers 2, 3, and 4 appear on the x-axis and the boxes are centered at 2, 3, and 4. When I add the first abline, the line appears too low, but actually it is too far to the right. The abscissae are being interpreted without realizing that the plot ori...

...yaxt="n") #plot implant data axis(side=1, at=c(0,1,3,5,7,10,14,21), labels=c(0,1,3,5,7,10,14,21)) # label x axis mtext("Day", side =1, at=10, line=3, cex=1.2) # title x axis The problem with this graph is that the main title is missing and so is the digit "1" at the abscissa position 1 - although the other abscissa labels are all there as defined in the "axis" call. Can anyone shed anylight on why this is? I'm using R 2.1 on OS X. Thanks Iain -- Iain Gallagher Institute for Infection & Immunology Research Ashworth Laboratories Kings Buildings U...

...last 3 commands and then show only the first line. How do I achieve this please? I am learning this package in order to perform my next task; interpolation. If I have a linear relationship between two variables and plot the results, how do I manipulate the graph to be able to show a value of the abscissa, especially for negative values, i.e. where the linear line intersects the x axis left of the y axis? There are 4 packages that claim interpolation (akima, aspline, interp, interpp) but they seem far to complicated, especially the latter two. Is there a simpler package I could use? Yours, rhelp...

Hi, i am working on a data set with EDA. That includes QQ-Plots of residuals vs expected normal distribution. What puzzles me is that the range of ordinate and abscissae is so different: while the theoretical quantiles range from [-2, 2] the sample quantiles on the ordinate do extent from [-20, 50]. Quite obviously some kind of transformation is done. Although i intensively RTFM i could not find, what is done here. What exactly characterizes the range of the...

...one who answers these questions - it's most helpful. I'm new to R and despite searching have not found an example of what I want to do (there are some good beginner's guides and a lot of complex plots, but I haven't found this). I would like to plot two variables against the same abscissa values. They have different scales. I've found how to make a second axis on the right for labeling, but not how to plot two lines at different scales. Thanks, - Rick rc at ece.pdx.edu

...g to determine if R is useful to me. I've browsed 'The Basics of S and S-Plus' (Krause & Olson), and like the logic of the language. However, I don't see an easy way to do things like this: * given a set of observations (x,y) (x and y equal-length vectors), and a 2nd set of abscissas x2, interpolate y at the new abscissas x2. (for now, I don't really care what kind of interpolation -- linear, some type of spline, etc.) * given observations (x,y), (with unequally-spaced x), create vector z with the numerical derivative of y with respect to x (eg evaluated at the values x)...

...of mathematica e.g.), i.e. the behavior of points under iteration of a function. So I tried (in vain) writing a function myfunction <- function(f,n,x){...} in order to compute f^{n}(x), f^{n}(x) being the function f composed with itself n-1 times. n is a natural number, and the argument x is the abscissa of the point I would like to `follow'. Can someone give me a boost? I appreciate your help, Tobias Verbeke

...7 7 Question 1: This is the simplest possible repeated measures design. It should yield F(4,24)=16.033 for the effect of condition. (The example is from the Systat manual, slightly changed.) How do I show this with R? Question 2: I would like a graph in which each line is a subject, and the abscissa is labeled A B C D E. Thus, one line would connect the points 4 1 5 10 10. Another would connect 9 2 10 7 7, and so on. (Systat calls this a parallel plot, and I have come to find it very useful for a first look at data.) Can this be done in R? Jonathan Baron, Professor of Psychology, Universi...

....math.ethz.ch Tue Jan 29 06:13:23 2002 >I've got a data-frame contining variables i columns named after the >specified subject and with data which represents time of observation. > >What I would like to do is to get a plot with observation time on the >ordinate and subject on the abscissa -- kind of a "at what time did I >observe the subjects?" summary.. One way to do what I think you want is: matplot(mydataframe,type="l") The type="l" means "lines". If subjects are columns, then each line is a plot of one subject. If subjects are rows...

...ling with how to better control plotting of time data and just can't seem to see the right path. My dataset has thousands of points distributes across a number of years. I would like to plot the responses according to increasing time with nice boundaries - perhaps integer months - along the abscissa. However, the earliest time occurs mid-month and I cannot figure out how to properly truncate the minimum date in the dataset to an integer month for use in the xlim= and at= statements to set the plotting range on month boundaries. Thanks in advance for your help or advice. Best Regards, Stev...

Hi, In my discpipline, it is common to plot one acoustic property on a positive scale but from top to bottom on the ordinate and the same for another measurement on the abscissa. So, the origin of the plot is on the top right of the plot, with increasing values to the left /down. This is to highlight the correlation between the acoustic measurement and the position of the forming structure, for instance when teaching it to students. The grouping ability of the trellis plo...

Dear all, I've got a problem with the function atan2. For a couple of coordinates x and y, This function returns the angle between the vector of coordinates (x, y) and the abscissa axis, i.e. it is the same as atan(y/x) (as indicated on the help page). If we consider the vector with coordinates x = 0 and y = 0, we have the following result: > atan(0/0) [1] NaN This is expected. However: > atan2(0,0) [1] 0 Instead of a missing value, the function atan2 returns an...

The contributed package e1071 does exactly what I want except that I need to have (1) the abscissa and ordinate axes swapped, with the probability scale on the bottom and the quantiles scale on the LHS. Using the following example: library(e1071) x <- rnorm(100, mean=5) probplot(x, line=FALSE) and (2) I need to have lines connecting the plotted symbols, as you get with: x<-log(seq(1:...

I'm new to making bubble plots with R, so I was going to try the following: https://stat.ethz.ch/pipermail/r-sig-geo/2009-August/006258.html Unfortunately, it looks like rgdal is not part of 2.14.  I'm not sure if it was the mirror I selected or something else is going on. > install.packages("rgdal") --- Please select a CRAN mirror for use in this session --- Warning

I am working with a non-parametic smoothing operation using a Generalized Additive Model. It is a bivariate data set. I know how to do the smooth, and out comes a nice smooth curve. Now I want to find the value of the smoothed curve for several values of x (the abscissa). This can be done (please correct me if I am wrong) by using the predict.gam function. You feed the predict.gam function a data frame, telling it what you want. Let's start with the predict.gam function. Are you supposed to be able to look up how to use it? E.g., what goes into the various...

...unction that computes a measure of interest, are such tools general enough to handle this? (The goal being to determine a confidence interval for the computed quantity.) As an attempt to understand the issues, I have used SQL to generate discrete sampled normal distributions, and then computed new abscissa values using a function such as division and computing the joint probability as the ordinate, and then re-partitioned the result into new bins using GROUP BY. This is general enough to handle non-normal distributions as well, though I don't know how to quantify the numerical stability/accuracy...

...pposed to be c(length(x), length(y)). This suggests that the user is supplying the midpoints of the rectangles. For all but the outer rectangles this is true. The outer rectangles, however, are only displayed in half width. So it is not possible to show correctly an image plot of equally-spaced abscissa. I would hazard a guess that these kinds of plots make up the bulk of calls to image() either directly or after a simple transformation (eg starting with boundary values rather than midpoints). Can I suggest that the code for image() be modified to show full rectangles for all values on the basis...

...500000 Standard Parallel: 45.500000 Longitude of Central Meridian: -96.000000 Latitude of Projection Origin: 23.000000 False Easting: 0.000000 False Northing: 0.000000 Planar Coordinate Information Planar Distance Units: meters Coordinate Encoding Method: coordinate pair Coordinate Representation Abscissa Resolution: 0.008192 Ordinate Resolution: 0.008192 Geodetic Model Horizontal Datum Name: North American Datum of 1927 Ellipsoid Name: Clarke 1866 Semi-major Axis: 6378206.400000 Denominator of Flattening Ratio: 294.978698 _________________ Bounding coordinates Horizontal In decimal degrees West:...

...unction(y) { sapply(y, function(y) { integrate(function(x) x*y, 0, 1)$value }) }, 0, 1) but I would like to use Gauss Quadrature to do it. I have written the following code (using R's statmod package) which works fine for one integral but it doesn't work for a double one: # Gauss-Legendre abscissas nodes <- gauss.quad.prob(25,dist="uniform",l=-1,u=1)$nodes # and weights weights <- gauss.quad.prob(25,dist="uniform",l=-1,u=1)$weights weights <- weights*2 # Approximate integral of f from a to b using Gauss-Legendre gauss_legendre<-function(f,a,b,nodes,weights)...