similar to: Plotting a cubic line from a multiple regression

I've spent quite a lot of the day trying to construct a fairly standard line plot in R, and I have the feeling there is a simple way that I haven't discovered. I have a large vector of measurements (TIME), and each measurement falls into one of three categories (PHASE). For each PHASE value, I want a mean of the corresponding TIME measurements plotted as a point along with

I've read through many postings about principle component analysis in the R-help archives, but haven't been able to piece together the information I need. I'd like to recreate an SPSS-like experience of factor analysis using R. Here's what SPSS produces: 1. Scatterplots of all possible variable pairs, with regression lines. xyplot(my.dataframe) is perfect but for the lack of

I'm not sure this is the proper forum to post to, but I hope someone can point me in the right direction if not have the answers. We have a RedHat Enterprise 5 AS machine, fully patched, with the most recent samba RPMs installed. The machine is joined to Active Directory via a 3rd party program called Centrify. An AD user, when logged into this system, is given permission to run /bin/mount via

My installation is R version 2.4.0 on Mac OS X version 10.4.8 (the latest release in both cases). I've recently had to restore my hard drive after a crash, and fear that this may have screwed up my Perl installation slightly. Whenever I run the read.xls function from library(gtools) I get the following errors: ###################### > file =

I've seen that this question has been asked before, in the archives, but I haven't been able to find a workable answer. This may be a failure to understand the statistics! The problem is that, while I can easily get partial correlation values out of corpcor, how do I get significance values? Surely the significance of a partial correlation between two variables isn't the

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,

Dear colleagues, I've been searching for information on the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (MSA). This statistic is generated in SPSS and is often used to determine if a dataset is "appropriate" for factor analysis -- it's true utility seems quite low, but it seems to come up in stats classes a lot. It did in mine, and a glance through the R-help

Hallo, I'm facing a problem and I would really appreciate your support. I have to translate some Matalb code in R that I don't know very well but I would like to. I have to interpolate 5 point with a cubic spline function and then I expect my function returns the Y value as output a specific X value inside the evaluation range. Let's suppose that: 1- *X = [-10, -5, 0, 5, 10]* 2

Dear all, Is the restricted cubic spline function working properly in the glm model? We used glm(y~rcs(x,5), family=binomial) but it seems that for some theoretical reasons the rcs, restricted cubic spline function can not be fitted by a glm function. Is this correct? Regards, Jan ((Originally, we used lrm(y~ rcs(x,5)) but we couldn't find how to derive the AIC value of the fitted model.

Greetings, I would like to use a cubic spline or smoother to model the fixed effects within nlme. So far the only smoother I have been able to get to run successfully in nlme is smooth(). I tried smooth.spline: fixed=list(lKa~1,lCL~smooth.spline(BSA, df=3)) the error I got was the following. Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : invalid

Hi, all I am trying to get the basis matrix and penalty matrix for natural cubic splines. In the "splines" package of R,"ns" can generate the B-spline basis matrix for a natural cubic spline. How can I get the basis matrix and penalty matrix for natural cubic spline. Thanks a lot! Lee [[alternative HTML version deleted]]

Hello everone,            Anyone who knows how to force a cubic smoothing spline to pass through a particular point?            I found on website  someone said that we can use "cobs package" to force the spline pass through certain points or impose shape           constraints (increasing, decreasing). However,  this package is using  B-spline and can only do linear and quadratic

Suppose I have two var x and y,now I want to fits a natural cubic spline in x to y,at the same time create new var containing the smoothed values of y. How can I get it?

Hi Everyone, As you may recall (I posted a while back) I'm currently doing a project measuring the energy consumption for different compiler flags. I've now gathered a set of benchmarks which I think are appropriate, for these (very) low end embedded platforms. The benchmarks that have been chosen: - CRC32, SHA - Prime sifting - Integer / floating point matrix multiplication - Cubic

Hello,   does anyone have an example on how to use restricted cubic splines function rcs within survfit.cph, if cph (Cox Proportional Hazard Regression) was done with restricted cubic splines (which I made to work)? Thank you. > [[alternative HTML version deleted]]

Which function implements the piecewise cubic Hermite interpolation? I am looking for equivalent of matlab's interp1 with the method = 'pchip' Here is the reference http://www.mathworks.com/access/helpdesk/help/techdoc/index.html?/access/helpdesk/help/techdoc/ref/interp1.html& -- View this message in context:

R-users E-mail: r-help@r-project.org My understanding is that package "mgcv" is based on "Generalized Additive Models: An Introduction with R (by Simon N. Wood)". On the page 126 of this book, eq(3.4) looks a quartic equation with respect to "x", not a cubic equation. I am wondering if all routines which uses cubic splines in mgcv are based on this quartic

(corrected version of previous posting) I fit a GAM to turtle growth data following methods in Limpus & Chaloupka 1997 (http://www.int-res.com/articles/meps/149/m149p023.pdf). I want to obtain figures similar to Fig 3 c & f in Limpus & Chaloupka (1997), which according to the figure legend are "expected size-at-age functions" obtained by numerically integrating

I'm trying to use R to determine the quality of a cubic Bezier curve approximation of an elliptical arc. I know the four control points and I want to compute (x,y) coordinates of many points on the curve. I can't find anything in either the base distribution or CRAN that does this; all the spline-related packages seem to be about *fitting* piecewise Bezier curves to a data set.

I want to compare the fit of a quadratic model to continuous data, with that of a cubic spline fit. Is there a way of computing AIC from for e.g. a GAM with a smoothing spine, and comparing this to AIC from a quadratic model? Cheers ****************************************** Tom Reed PhD Student Institute of Evolutionary Biology 102 Ashworth Laboratories Kings Buildings University of