similar to: Error message in function mars() in package mda

I'm trying to use mars{mda} to model functions that look fairly close to a sequence of straight line segments. Unfortunately, 'mars' seems to totally miss the obvious places for the knots in the apparent first order spline model, and I wonder if someone can suggest a better way to do this. The following example consists of a slight downward trend followed by a jump up after

Hi, I have run mars() with the default degree=1, and have constructed splines which are similar to what I would get from Salford System's MARS. I then use these splines as potential predictors, along with rpart( ) terminal nodes and other potential predictors, in logistic regression in SAS. The output of mars() and the splines I created are shown below (pardon the lengthy output): >

Hello all, So I'm doing some data analysis using MARS. I have a matrix of 65 independent variables, from which I'm trying to predict 71 dependent variables. I have 900+ data points (so a 900x136 matrix of data), which I randomly split into training and validation sets, for ~450 data points in each set. Occasionally, this works well, and I get decent predictions. However, quite often

Hi, I'm trying to use mars function in R to interpolate nonlinear multivariate functions. However, it seems that mars gives me a fit which uses only very few basis function and it underfits very badly. For example, I have tried the following code to test mars: require("mda") f <- function(x,y) { x^2-y^2 }; #f <- function(x,y) { x+2*y }; # Grid x <-

>>>>> Anupam Tyagi >>>>> on Tue, 9 Jul 2024 16:16:43 +0530 writes: > How can I do automatic knot selection while fitting piecewise linear > splines to two variables x and y? Which package to use to do it simply? I > also want to visualize the splines (and the scatter plot) with a graph. > Anupam NB: linear splines, i.e. piecewise

Hi, is there an R function which can compute the R-squared for a mars model fitted using the mda package? many thanks in advance best regards Marco Girardello

Is there a function for MARS (Modeling with Automated Regression Tools) available for R PC window users ? I know there is a commercial package from Salford systems and there is a shared function for Unix system. Nancy Lo -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

Dear helpers, I am trying to use the mda package downloaded from the R website, but the data set has missing values so I got an error message. Should I manually handle these missing values? I was trying to read the documents to specify any option related to missing values, but I did not find it. Please forgive me if I ignore something obvious. Thanks, Zhu Wang Statistical Science Department

Hi! I have some questions about MARS model's coefficient of determination. I use the MARS method in my master's thesis and I have noticed some problems with the MARS model's R^2. You can see the following example that the MARS model's R^2 is too big when i have used mars() -function for MARS model building, and when I have made MARS-model using a linear regression, it

dear all, I apologize for my delay in replying you. Here my contribution, maybe just for completeness: Similar to "earth", "segmented" also fits piecewise linear relationships with the number of breakpoints being selected by the AIC or BIC (recommended). #code (example and code from Martin Maechler previous email) library(segmented) o<-selgmented(y, ~x, Kmax=20,

Hello. First, I want to thank those responsible for Dovecot IMAP as it was a real treat to implement in comparison to my mostly-failed attempts with Cyrus. The passwd-file authentication was exactly what I needed as I really didn't want to have to deal with PAM or LDAP but where I am setting up a server with virtual accounts. I'm using Postfix as an MTA, and it quite handily passes

Dear List, I'm using gam in a multiple imputation framework -- specifying the knot locations, and saving the results of multiple models, each of which is fit with slightly different data (because some of it is predicted when missing). In MI, coefficients from multiple models are averaged, as are variance-covariance matrices. VCV's get an additional correction to account for how

Dear All, I'm interested in creating a function similar to ns() from package splines that can be passed in a model formula. The idea is to produce "safe" predictions from a model using this function. As I have seen, to do this I need to use makepredictcall(). Consider the following toy example: myns <- function (x, df = NULL, knots = NULL, intercept = FALSE, Boundary.knots =

For a private package ("soon" to be on CRAN), I started looking at and thinking about the function confusion() from the mda package and wondered if it wouldn't make sense to provide the generic function in base R and also think about making the default for the `true' argument equal to fitted(object). One could write methods for confusion() for almost all classifiers which

Hi all, I'm at the stage of planning our new mail system and am evaluating dovecot for it. For large mailstore setups, it makes sense to split maildirs across many directories. I see that setting default_mail_env to something like maildir:/var/imap/domain/%1d/%d/%1u/user/%u/ would create almost identical on-disk layout as multidomain cyrus setup. Now, as dovecot does not have its own

Goodmorning, This is a documentation related question about the B-spline function in R. In the help file it is stated that: "df degrees of freedom; one can specify df rather than knots; bs() then chooses df-degree-1 knots at suitable quantiles of x (which will ignore missing values)." So if one were to specify a spline with 6 degrees of freedom (and no intercept) then a basis

Dear, When fitting the following model knots <- 5 lrm.NDWI <- lrm(m.arson ~ rcs(NDWI,knots) I obtain the following result: Logistic Regression Model lrm(formula = m.arson ~ rcs(NDWI, knots)) Frequencies of Responses 0 1 666 35 Obs Max Deriv Model L.R. d.f. P C Dxy Gamma Tau-a R2 Brier 701 5e-07 34.49

Dear Rusers, I am now using R and SAS to fit the piecewise linear functions, and what surprised me is that they have a great differrent result. See below. #R code--Knots for distance are 16.13 and 24, respectively, and Knots for y are -0.4357 and -0.3202 m.glm<-glm(mark~x+poly(elevation,2)+bs(distance,degree=1,knots=c(16.13,24)) +bs(y,degree=1,knots=c(-0.4357,-0.3202

Dear List, I'm using GAMs in a multiple imputation project, and I want to be able to combine the parameter estimates and covariance matrices from each completed dataset's fitted model in the end. In order to do this, I need the knots to be uniform for each model with partially-imputed data. I want to specify these knots based on the quantiles of the unique values of the non-missing

Dear R-help, In the rms package, I have fitted an ols model with a variable represented as a restricted cubic spline, with the knot locations specified as a previously defined vector. When I save the model object and open it in another workspace which does not contain the vector of knot locations, I get an error message if I try to predict with that model. This also happens if only one workspace