search for: coefs

Any better (more efficient, built-in) ideas for computing coef[1]+coef[2]*x+coef[3]*x^2+ ... than polynom <- function(coef,x) { n <- length(coef) sum(coef*apply(matrix(c(rep(x,n),seq(0,n-1)),ncol=2),1,function(z)z[1]^z[2])) } ? Ben -- 318 Carr Hall bolker at zoo.ufl.edu Zoology Department, University of Florida http://www.zoo.ufl.edu/bolker

Dear List, A posting to R-Help exposed this problem with the print method for objects of class Arima: > set.seed(1) > x <- arima.sim(n = 100, list(ar = 0.8897, ma = -0.2279)) > mod <- arima(x, order = c(1,0,1)) > coefs <- coef(mod) > mod2 <- arima(x, order = c(1,0,1), fixed = coefs) > mod2 Call: arima(x = x, order = c(1, 0, 1), fixed = coefs) Coefficients: Error in se && nrow(x$var.coef) : invalid 'y' type in 'x && y' > print(mod2, se = FALSE) Call: arima(x = x, o...

Hola! I have the following in a .Rd file: \eqn{\mbox{coef} = c(\mbox{coef}[1],\ldots, \mbox{coef}[n]) } {coef = c(coef[1], coef[2], \dots, coef[n])} However, both arguments come out in the latex file! Whats happening? Kjetil Halvorsen

Hi R experts, I'm trying to use nls() for a piecewise linear regression with the first slope constrained to 0. There are 10 data points and when it does converge the second slope is almost always over estimated for some reason. I have many sets of these 10-point datasets that I need to do. The following segment of code is an example, and sorry for the overly precise numbers, they are just

Hi, I have a question about the usage of eval(). Wonder if any experienced user can help me out of it. I use eval() in the following function: semireg.pwl <- function(coef.s=rnorm(1),coef.a=rnorm(1),knots.pos=knots.x,knots.ini.val=knots.val){ knotn <- length(knots.pos) def.par.env <- sys.frame(1) print(def.par.env) print(environment(coef.s)) tg <- eval( (parse(text=

It would be nice to have something like stata's outreg that lets regression output go into a form like Specification (1) Specification (2) Var 1 coef(1,1) coef(1,2) se(1,1) se(1,2) Var 2 coef(2,1) coef(2,2) se(2,1) se(2,2) I don't think this can be done in xtable? Thomas Davidoff Assistant Professor Haas School of Business UC Berkeley Berkeley, CA 94618 Phone: (510)

Hi, I have a small dataset that I'm fitting a segmented regression using nls on. I get a step below minimum factor error, which I presume is because residual sum of square is still "not small enough" when steps in the parameter space is already below specified/default value. However, when I look at the trace, the convergence seems to have been reached. I initially thought I might

Dear R-helpers, I'm trying to associate linear coefficients (intercept and slope) to tens of thousands of observations based on a table with benchmark values. #####Example - Value table and their corresponding coefficients (intercept and slope) coef = data.frame(cbind(st=c(1:5),b = runif(5,0.3,5),a = seq(0.5,5,1)))print(coef) #Example of observations to be computedobs = runif(20,1,5)print(obs)

Relatively new to R, I'm trying to do a relatively simple task. I have data set that has several variables arranged by SubjID and visit, with multiple observations for that combination. I do linear regression on those multiple observations, then generated a set of interpolated values from the regression at fixed intervals along "x". I now want to average each of those across all the

Dear R People: I have the output from an arima model fit in an object xxx. I want to verify that the ma1 coefficient is there, so I did the following: > xxx$coef ar1 ar2 ma1 intercept 1.3841297 -0.4985667 -0.9999996 -0.1091657 > str(xxx$coef) Named num [1:4] 1.384 -0.499 -1 -0.109 - attr(*, "names")= chr [1:4] "ar1" "ar2"

Hi - I'm running R 1.3.0 on i686-pc-linux-gnu > rm(x, y, z) > df <- data.frame(x=1:20,y=1:20,z=factor(1:20 <= 10)) dummy.coef falls over: > dummy.coef.lm(lm(y ~ z * poly(x,1), data=df)) Error in poly(x, 1): Object "x" not found > dummy.coef.lm(lm(y ~ z * I(x), data=df)) Error in unique(c("AsIs", class(x))): Object "x" not found but

In the rockchalk package, I want to provide functions for regression objects that are "well behaved." If an object responds to the methods that lm or glm objects can handle, like coef(), nobs(), and summary(), I want to be able to handle the same thing. It is more difficult than expected to ask a given fitted model object "do you respond to these functions: coef(), nobs(),

Hi, I want to fit multiple cox models using the coxph() function. To do this, I use a for-loop and save the relevant results in a separate matrix. In the example below, only two models are fitted (my actual matrix has many more columns), one gives a warning message, while the other does not. Right now, I see all the warning message(s) after the for-loop is completed but have no idea which model

Hello, Could somebody please tell me what am I doing wrong in following? I try extract coefficients (using arm-package) from the lmer frunction, but I get the following warning: a<-data.frame(coef(res)) Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class "coef.mer" into a data.fram I think I have done it before

Hi, I am trying to place my own functions in the nlme environment: The following statement works: environment(coef.corSPT) <- environment(getS3method("coef","corSpatial")) but this one returns an error: environment(get("coef<-.corSPT")) <- environment(getS3method("coef<-","corSpatial")) Error in

Dear List, How do I extract the approximate Wald test for the frailty (in the following example 17.89 value)? What about the P-values, other Chisq, DF, se(coef) and se2? How can they be extracted? ######################################################> kfitm1 Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id, dist = "gauss"), data = kidney)

--- silk/NSQ.c | 18 ++------------- silk/NSQ.h | 27 ++++++++++++++++++++++ silk/arm/NSQ_neon.c | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++ silk/arm/NSQ_neon.h | 10 ++++++++ 4 files changed, 105 insertions(+), 16 deletions(-) diff --git a/silk/NSQ.c b/silk/NSQ.c index d8513dc..ec81f3b 100644 --- a/silk/NSQ.c +++ b/silk/NSQ.c @@ -205,7 +205,7 @@ void

--- silk/NSQ.c | 18 ++------------- silk/NSQ.h | 27 ++++++++++++++++++++++ silk/arm/NSQ_neon.c | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++ silk/arm/NSQ_neon.h | 10 ++++++++ 4 files changed, 105 insertions(+), 16 deletions(-) diff --git a/silk/NSQ.c b/silk/NSQ.c index d8513dc..ec81f3b 100644 --- a/silk/NSQ.c +++ b/silk/NSQ.c @@ -205,7 +205,7 @@ void

Dear Martin, I think that your plan makes sense. It's too bad that aov() behaved differently in this respect from lm(), and thus created more work, but it's not be a bad thing that the difference is now explicit and documented. I expect that that other problems like this will surface, particularly with contributed packages (and I know that you're aware that this has already happened

Dear list members I have a doubt on how p-values for t-statistics are calculated in the summary of Linear Models. Here goes an example: x <- rnorm(100,50,10) y <- rnorm(100,0,5) fit1<-lm(y~x) summary(fit1) summary(fit1)$coef[2] # b summary(fit1)$coef[4] # Std. Error summary(fit1)$coef[6] # t-statistic summary(fit1)$coef[8] # Pr(>|t| summary(fit1)$df [2] # degrees of freedom #