similar to: Regarding Exponentail in R Stat

Hello, I'm working on a very large project in which we do many calculations which include many types of regression such as, Liner, Quadratic, Cubic, Exponential, Sinusoidal, and Logarithmic. Im well aware that its easy enough to do Linear regression in R but what about the other types? I've been searching on google for such functions but to no avail. Thank you, -- Samuel Bravo

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and

Hi, I am new-comer to statistics and R-Project. I would like to know if these features can be attained in R-Project.Please help. 1) beta 1 and Beta 2, or gamma one and gamma two for skewness and kurtosis, respectively, including standard errors and tests for significance (relative to values for a Gaussian distribution). 2) linear correlation 3) quadratic regression 4) polynomial regression

-----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

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 R help, I'm having some trouble with model formulas for the ols function in the rms package. I want to have two variables represented as restricted cubic splines, and also include an interaction as a product of linear terms, but I get an error message. library(rms) d <- data.frame(x1 = rnorm(50), x2 = rnorm(50), y = rnorm(50)) ols(y ~ rcs(x1,3) + rcs(x2,3) + x1*x2, data=d) Error in

  Hi, I fitted tree growth data with Chapman-Richards growth function using nls. summary(fit.nls) Formula: Parameters: Estimate Std. Error t value Pr Signif. codes: 0 ''***'' 0.001 ''**'' 0.01 ''*'' 0.05 ''.'' 0.1 '' '' 1 Residual standard error: 1.879 on 713 degrees of freedom Algorithm

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

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

The following variables have the following significant relationships (x is the explanatory variable): linear, cubic, exponential, logistic. The linear relationship plots without any trouble. Cubic is the 'best' model, but it is not plotting as a smooth curve using the following code: cubic.lm<- lm(y~poly(x,3)) lines(x,predict(cubic.lm),lwd=2) How do I plot the data and the estimated

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

Dear all, I'm performing RDA on plant presence/absence data, constrained by geographical locations. I'd like to constrain the RDA by the "extended matrix of geographical coordinates" -ie the matrix of geographical coordinates completed by adding all terms of a cubic trend surface regression- . This is the command I use (package vegan): >rda(Helling ~

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

-----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

Hi Folks, No doubt a question with a well-known answer, but I'm unfortunately not managing to find it readily ... ! I have a quantitative variable Y and a 4-level ordered factor A (with very unequal numbers at the different levels, by the way). The command lm(Y ~ A) returns (amongst other stuff) an intercept, and coefficients A.L, A.Q and A.C for the Linear, Quadratic and Cubic effects.

Hi Bert, I won't post any more messages on this thread as problem has shifted from Optimization in R to Graph Algorithms. Rest fine Khris. On Aug 2, 2012, at 9:13 PM, Bert Gunter [via R] wrote: > This discussion needs to be taken off (this) list, as it appears to > have nothing to do with R. > > -- Bert > > On Thu, Aug 2, 2012 at 2:27 AM, khris <[hidden email]>

Hello R-help I don’t know how to interpret significance from the contr.poly() function . From the example below : how can I tell if data has a significant Linear/quadratic/cubic trend? > contr.poly(4, c(1,2,4,8))               .L         .Q          .C [1,] -0.51287764  0.5296271 -0.45436947 [2,] -0.32637668 -0.1059254  0.79514657 [3,]  0.04662524 -0.7679594 -0.39757328 [4,]  0.79262909 

Hi all, I'm looking for smooth monotone estimation packages, preferably using splines. I downloaded the 'cobs' package and intend to use it, but since it offers only quadratic splines based on L1 minimization, I'd like to compare its performance to that of a more 'mainstream' cubic-spline, L2-norm minimizing spline. Preferably a smoothing spline. Does anyone know of such

I use smooth.spline(x, y) in package modreg and I would like to get value of penalized log likelihood and preferable also its two parts. To make clear what I am asking for (and make sure that I am asking for the right thing) I clarify my problem trying to use the same notation as in help(smooth.spline): I want to find the natural cubic spline f(x) such that L(f) = \sum_{k=1}{n} w[k](y[k] -

?? If I haven't misunderstood, they are completely different! 1) NIR must be a matrix, or poly(NIR,...) will fail. 2) Due to the previously identified bug in poly, degree must be explicitly given as poly(NIR, degree =2,raw = TRUE). Now consider the following example: > df <-matrix(runif(60),ncol=3) > y <- runif(20) > mdl1 <-lm(y~df*I(df^2)) > mdl2