similar to: Discriminant of a cubic polynomial

Dear Thomas, Unfortunately, I do not know if any packages implement this functionality. Though, it is a topic that interests me. Unlike the "classic discriminant", I prefer to work with the reduced polynomial. This "discriminant" is generalizable to a superset of Chebysev polynomials (which I called Cardano-polynomials). x^3 - 3*c*x - 2*d = 0 x^5 - 5*c*x^3 + 5*c^2*x - 2*d =

Hi, I'm responding to a post about finding roots of a cubic or quartic equation non-iteratively. One obviously could create functions using the explicit algebraic solutions. One post on the subject noted that the square-roots in those solutions also require iteration, and one post claimed iterative solutions are more accurate than the explicit solutions. This post, however, is about

How do you remove one of the terms from an ordered polynomial contrast in your linear model. For example, I have significant terms for linear and cubic but not quadratic, how would i remove the quadratic term from lm(response~treatment) Cheers, Chris -- View this message in context: http://www.nabble.com/Orthogonal-polynomial-contrasts-tp16608353p16608353.html Sent from the R help mailing list

I have a function that fits polynomial models for the orders in n: lmn <- function(d,n){ models=list() for(i in n){ models[[i]]=lm(y~poly(x,i),data=d) } return(models) } My data is: > d=data.frame(x=1:10,y=runif(10)) So first just do it for a cubic: > mmn = lmn(d,3) > predict(mmn[[3]]) 1 2 3 4 5 6 7 8

Hi, I'm using the cubic splines from termstrc package. I invoked the splines_estim function with a group of 43 bonds. It computes 6 knot points and returns values for alpha1 to alpha7. My question is how to use these alpha1 to alpha7 in the equation of yield? For example, if I'm trying to find the yield at, say, 12.25 years, which falls between 3rd & 4th knot points what should the

Hello, I am using R and the smooth.Pspline function in the pspline package to smooth some data by using natural cubic splines. After fitting a sufficiently smooth spline using the following call: (ps=smooth.Pspline(x,y,norder=2,spar=0.8,method=1) [the values of x are age in years from 1 to 100] I tried to check that R in fact had fitted a natural cubic spline by checking that the resulting

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,

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests) I am following http://www.nabble.com/file/p25569553/SPLINES.PDF SPLINES.PDF where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) +

The cubic regression of my model is significant and I want to plot a line that best fits. It's not abline() function, because it has a curve. Please, how can I plot it? --- Fernando Andreacci Biólogo Fone +55 47 9921 4015 fandreacci@gmail.com [[alternative HTML version deleted]]

Does lme prediction work correctly with poly() terms? In the following simulated example, the predictions are wildly off. Or am I doing something daft? Milk yield for five cows is measured weekly for 45 weeks. Yield is simulated as cubic function of weekno + random cow effect (on intercept) + residual error. I want to recover an estimate of the fixed curve. ############### library(nlme)

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests). I am following Smoothing Splines, D.G. Pollock (available online) where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) + e_i

Colleagues, Many of my colleagues come to me for a recommendation for statistical software. Since I am an R user, that's my typical answer. Some colleagues of mine refuse to use it because of its steep learning curve and lack of a GUI. They wanted a statistical software that's free and that had a GUI. I recently learned about JASP. See https://jasp-stats.org/ for more details This may be

Looking to the wonderful statistical advice that this group can offer. In behavioral science applications of stats, we are often introduced to coefficients for orthogonal polynomials that are nice integers. For instance, Kirk's experimental design book presents the following coefficients for p=4: Linear -3 -1 1 3 Quadratic 1 -1 -1 1 Cubic -1 3 -3 1 In R orthogonal

Env: R 2.8.1, Win Xp, Eclipse/StatET In a .Rd file, I have an example containing the lines: # calculate Y M, using polynomial contrasts trends <- as.matrix(VocabGrowth) %*% poly(8:11, degree=3) colnames(trends)<- c("Linear", "Quad", "Cubic") [At the risk of a long message, I'll append the complete .Rd file at the end of this message, in case this was

Dear R users, The package 'mfp' that fits fractional polynomial terms to predictors. Example: data(GBSG) f <- mfp(Surv(rfst, cens) ~ fp(age, df = 4, select = 0.05) + fp(prm, df = 4, select = 0.05), family = cox, data = GBSG) print(f) To describe the association between the original predictor, eg. age and risk for different values of age I can plot it the polynomials

re: Cubic splines in package "mgcv" I don't have access to Gu (2002) but clearly the function R(x,z) defined on p126 of Simon Wood's book is piecewise quartic, not piecewise cubic. Like Kunio Takezawa (below) I was puzzled by the word "cubic" on p126. As Simon Wood writes, this basis is not actually used by mgcv when specifying bs="cr". Maybe the point is

Dear useRs, I need to compute zero of polynomial function fitted by lm. For example if I fit cubic equation by fit=lm(y~x+I(x^2)+i(x^3)) I can do it simply by polyroot(fit$coefficients). But, if I fit polynomial of higher order and optimize it by stepAIC, I get of course some coefficients removed. Then, if i have model y ~ I(x^2) + I(x^4) i cannot call polyroot in such way, because there is

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

Hello everyone I'm trying to use Scheffe's complete cubic model (mixture design). In the bibliographies, they indicate that the term is of the type: A * B * (A-B). But I see that trying to adjust the three cubic terms results in singularities. I know this implies not having the inverse matrix: solve (t (X)% *% X) does not exist. The bibliographies show all three cubic terms. So my

I have used LME to fit a mixed effects model on my data. The data has 274 subjects with 1 to 6 observations per subject. Time is not linearly associated with the outcome, so I used ns to fit a natural cubic spline with 3 auto knots. Subject and the natural cubic time of spline are both treated as random effects. This model has run without any problem, but now I would like to plot trajectories for