search for: coef2

Hello, I want to model my data with the following model: Y1=X1*coef1+X2*coef2 Y2=X1*coef2+X2*coef3 Note: coef2 appears in both lines Xi, Yi is input versus output data respectively How can I do this in R? I got this far: lm(Y1~X1+X2,mydata) now how do I add the second line of the model including the cross dependency? Your help is greatly appreciated! Cheers...

Dear all: I'm trying to fit the optimal Box-Cox transformation related to nls (see the code below) for the demand of money data in Green (3th Edition) but in the last step R gives the next error message. Error en `[.data.frame`(eval(object$data), , as.character(formula(object)[[2]])[2]) : undefined columns selected. ?Any idea to solve the problem? Thanks in advance,

.....,1000) for x (the predictor) with some noise (x+.02*x) and I set the response variable y=x. Then, I try rcs and ols as follows: m = ( sqrt(y1) ~ ( rcs(x1,3) ) ); #I tried without sqrt also f = ols(m, data=data_train.df); print(f); [I plot original x1,y1 vectors and the regression as in y <- coef2[1] + coef2[2]*x1 + coef2[3]*x1*x1] But this gives me a VERY bad fit: " Linear Regression Model ols(formula = m, data = data_train.df) n Model L.R. d.f. R2 Sigma 1000 4573 2 0.9897 0.76 Residuals: Min 1Q Median...

Jonathan Lennox wrote: > +opus_int32 silk_noise_shape_quantizer_short_prediction_neon(const opus_int32 *buf32, const opus_int32 *coef32) > +{ > + int32x4_t coef0 = vld1q_s32(coef32); > + int32x4_t coef1 = vld1q_s32(coef32 + 4); > + int32x4_t coef2 = vld1q_s32(coef32 + 8); > + int32x4_t coef3 = vld1q_s32(coef32 + 12); > + > + int32x4_t a0 = vld1q_s32(buf32 - 15); > + int32x4_t a1 = vld1q_s32(buf32 - 11); > + int32x4_t a2 = vld1q_s32(buf32 - 7); > + int32x4_t a3 = vld1q_s32(buf32 - 3); > + > + int64x2_...

...ion seems to be extremely sensitive both of these. I am wondering if I've missed something in my function? I'd be very grateful for any tips. Thanks! Martin TwoSlope <-function(lm1, lm2) { ## lm1 and lm2 are two linear models on independent data sets coef1 <-summary(lm1)$coef coef2 <-summary(lm2)$coef sigma <-(sum(lm1$residuals^2)+sum(lm2$residuals^2))/(lm1$df.residual + lm2$df.residual-4) SSall <-sum(lm1$model[,2]^2) + sum(lm2$model[,2]^2) SSprod <-sum(lm1$model[,2]^2) * sum(lm2$model[,2]^2) F.val <-(as.numeric(coefficients(lm1)[2]) - as.numeric(coefficient...

...e: > > Jonathan Lennox wrote: >> +opus_int32 silk_noise_shape_quantizer_short_prediction_neon(const opus_int32 *buf32, const opus_int32 *coef32) >> +{ >> + int32x4_t coef0 = vld1q_s32(coef32); >> + int32x4_t coef1 = vld1q_s32(coef32 + 4); >> + int32x4_t coef2 = vld1q_s32(coef32 + 8); >> + int32x4_t coef3 = vld1q_s32(coef32 + 12); >> + >> + int32x4_t a0 = vld1q_s32(buf32 - 15); >> + int32x4_t a1 = vld1q_s32(buf32 - 11); >> + int32x4_t a2 = vld1q_s32(buf32 - 7); >> + int32x4_t a3 = vld1q_s32(buf32 - 3); &...

Hello, I have posted this mail a few days ago but I did it wrong, I hope is right now: I have the following doubts related with lm.ridge, from MASS package. To show the problem using the Longley example, I have the following doubts: First: I think coefficients from lm(Employed~.,data=longley) should be equal coefficients from lm.ridge(Employed~.,data=longley, lambda=0) why it does not happen?

...)) ## compute raw prediction for classifier (i,j) predone <- function (i,j) { ## ranges for class i and j: ri <- start[i] : (start[i] + object$nSV[i] - 1) rj <- start[j] : (start[j] + object$nSV[j] - 1) ## coefs for (i,j): coef1 <- object$coefs[ri, j-1] coef2 <- object$coefs[rj, i] ## return raw values: crossprod(coef1, kernel[ri]) + crossprod(coef2, kernel[rj]) } ## compute votes for all classifiers votes <- rep(0,object$nclasses) c <- 0 # rho counter for (i in 1 : (object$nclasses - 1)) for (j in (i + 1) : object$nclas...

...#include "celt/cpu_support.h" +#include "celt/arm/armcpu.h" + +opus_int32 silk_noise_shape_quantizer_short_prediction_neon(const opus_int32 *buf32, const opus_int32 *coef32) +{ + int32x4_t coef0 = vld1q_s32(coef32); + int32x4_t coef1 = vld1q_s32(coef32 + 4); + int32x4_t coef2 = vld1q_s32(coef32 + 8); + int32x4_t coef3 = vld1q_s32(coef32 + 12); + + int32x4_t a0 = vld1q_s32(buf32 - 15); + int32x4_t a1 = vld1q_s32(buf32 - 11); + int32x4_t a2 = vld1q_s32(buf32 - 7); + int32x4_t a3 = vld1q_s32(buf32 - 3); + + int64x2_t b0 = vmull_s32(vget_low_s32(a0), vget_...

...#include "celt/cpu_support.h" +#include "celt/arm/armcpu.h" + +opus_int32 silk_noise_shape_quantizer_short_prediction_neon(const opus_int32 *buf32, const opus_int32 *coef32) +{ + int32x4_t coef0 = vld1q_s32(coef32); + int32x4_t coef1 = vld1q_s32(coef32 + 4); + int32x4_t coef2 = vld1q_s32(coef32 + 8); + int32x4_t coef3 = vld1q_s32(coef32 + 12); + + int32x4_t a0 = vld1q_s32(buf32 - 15); + int32x4_t a1 = vld1q_s32(buf32 - 11); + int32x4_t a2 = vld1q_s32(buf32 - 7); + int32x4_t a3 = vld1q_s32(buf32 - 3); + + int64x2_t b0 = vmull_s32(vget_low_s32(a0), vget_...

As promised, here's a re-send of all my Aarch64 patches, following comments by John Ridges. Note that they actually affect more than just Aarch64 -- other than the ones specifically guarded by AARCH64_NEON defines, the Neon intrinsics all also apply on armv7; and the OPUS_FAST_INT64 patches apply on any 64-bit machine. The patches should largely be independent and independently useful, other

Hi, I have a question about running multiple in regressions in R and then storing the coefficients. I have a large dataset with several variables, one of which is a state variable, coded 1-50 for each state. I'd like to run a regression of 28 select variables on the remaining 27 variables of the dataset (there are 55 variables total), and specific for each state, ie run a regression of

I'm trying to fit a Cox proportional hazards model to some hospital admission data. About 25% of the patients have had at least one admission, and of these, 40% have had two admissions within the 12 month period of the study. Each patients has had one of 4 treatments, and one of the treatment groups has had no admissions for the period. I used:

This sequence of patches provides arm64 support for Opus. Tested on iOS, Android, and Ubuntu 14.04. The patch sequence was written on top of Viswanath Puttagunta's Ne10 patches, but all but the second ("Reorganize pitch_arm.h") should, I think, apply independently of it. It does depends on my previous intrinsics configury reorganization, however. Comments welcome. With this and

Here are my aarch64 patches rebased to the current tip of Opus master. They're largely the same as my previous patch set, with the addition of the final one (the Neon fixed-point implementation of xcorr_kernel). This replaces Viswanath's Neon fixed-point celt_pitch_xcorr, since xcorr_kernel is used in celt_fir and celt_iir as well. These have been tested for correctness under qemu

Following Tim's comments, here are my reworked patches for the Neon intrinsic function patches of of my Aarch64 patchset, i.e. replacing patches 5-8 of the v2 series. Patches 1-4 and 9-18 of the old series still apply unmodified. The one new (as opposed to changed) patch is the first one in this series, to add named constants for the ARM architecture variants. There are also some minor code

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)

......,1000) for x (the predictor) with some noise (x+.02*x) and I set the response variable y=x. Then, I try rcs and ols as follows: m = ( sqrt(y1) ~ ( rcs(x1,3) ) ); #I tried without sqrt also f = ols(m, data=data_train.df); print(f); [I plot original x1,y1 vectors and the regression as in y <- coef2[1] + coef2[2]*x1 + coef2[3]*x1*x1] But this gives me a VERY bad fit: " Linear Regression Model ols(formula = m, data = data_train.df) n Model L.R. d.f. R2 Sigma 1000 4573 2 0.9897 0.76 Residuals: Min 1Q Median...