similar to: GAM, how to set qr=TRUE

Hello, I'd like to understand 'what' is predicting the response for library(mgcv) gam? For example: library(mgcv) fit <- gam(y~s(x),data=as.data.frame(l_yx),family=binomial) xx <- seq(min(l_yx[,2]),max(l_yx[,2]),len=101) plot(xx,predict(fit,data.frame(x=xx),type="response"),type="l") I want to see the generalized function(s) used to predict the response

Hello, How do I plot a gam fit object on probability (Y axis) vs raw values (X axis) axis and include the confidence plot lines? Details... I'm using the gam function like this: l_yx[,2] = log(l_yx[,2] + .0004) fit <- gam(y~s(x),data=as.data.frame(l_yx),family=binomial) And I want to plot it so that probability is on the Y axis and values are on the X axis (i.e. I don't want log

Sorry if this is a duplicate: This is a re-post because the pdf's mentioned below did not go through. Hello, I'm new'ish to R, and very new to glm. I've read a lot about my issue: Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurred ...including: http://tolstoy.newcastle.edu.au/R/help/05/07/7759.html

Dear R people, I do not have much knowledge about linear algebra but currently I need to understand what the function qr.qty is actually doing. The documentation states that it calculates t(Q) %*% y via a previously performed QR matrix decomposition. In order to do that, I tried following basic example: m<-matrix(c(1,0,0,0,1,0,0,0,1,0,0,1),ncol=3) # 4x3 matrix

With this example set.seed(123) A <- matrix(runif(40), nrow = 8) y <- 1:nrow(A) A.laqr <- qr(A, LAPACK=TRUE) both qr.qy(A.laqr,y) and qr.qty(A.laqr,y) give the respective error messages Error in qr.qy(A.laqr, y) : 'b' must be a numeric matrix Error in qr.qty(A.laqr, y) : 'b' must be a numeric matrix However when Lapack is not used as in A.liqr <- qr(A,

Dear maintainers, I'm reporting a bug in qr.coef that mishandles the colnames of matrix. A minimal reproducible example is as follows: x <- cbind(rep(0, 10), rep(0, 10)) y <- rep(0, 10) q <- qr.default(x) qr.coef(q, y) [1] NA NA If x has colnames, then qr.coef will end up with an error: x <- cbind(x1 = rep(0, 10), x2 = rep(0, 10)) y <- rep(0, 10) q <- qr.default(x)

Hi, Why the qr() produces a negative Q compared with Gram-Schmidt? (note example below, except Q[2,3]) Here is an example, I calculate the Q by Gram-Schmidt process and compare the output with qr.Q() a <- c(1,0,1) b <- c(1,0,0) c <- c(2,1,0) x <- matrix(c(a,b,c),3,3) ########################## # Gram-Schmidt ########################## A <- matrix(a,3,1) q1 <-

The following snippet suggests that there is either a bug in qr(,LAPACK=T), or some bug in my understanding. Note that the detected rank is correct (= 2) using the default LINPACK qr, but incorrect (=3) using LAPACK. This is running on Linux Redhat 9.0, using the lapack library that comes with the Redhat distribution. I'm running R 1.7.1 compiled from the source. If the bug is in my

Might it be possible to tighten the definition of "is.qr". I noticed that after I mistakenly typed example(lm) # make lm object named lm.D9 qr.Q(lm.D9) which exhausted the heap memory and produced two warning messages. As an object of class "lm" has a "qr" component, "is.qr" failed to detect that "lm.D9" was not a "qr" object. The

The bug I submitted yesterday (It's not entered in the bug data base, so I have no ID for it) included a suggested fix that is not correct. It worked for the examples I gave because there was no pivoting in fact, or only pivot permutations that were idempotent. A correction that works in general on the examples I gave makes these two changes in qr.coef(): ## coef[qr$pivot, ]

Ladies and Gentlemen, using > A <- structure(c(1, 0, 0, 3, 2, 1, 4, 5, -3, -2, 1, 0), .Dim = as.integer(c(3,4))) I get > dim(A) [1] 3 4 > qr.R(qr(A),complete=TRUE) [,1] [,2] [,3] [,4] [1,] -1 -3.000000 -4.000000 2.0000000 [2,] 0 -2.236068 -3.130495 -0.8944272 [3,] 0 0.000000 -4.919350 -0.4472136 > qr.R(qr(A),complete=FALSE) [,1]

Hi, I have noticed different rank values calculated by qr() depending on LAPACK parameter. When it is FALSE (default) a true rank is estimated and returned. Unfortunately, when LAPACK is set to TRUE, the min(nrow(A), ncol(A)) is returned which is only occasionally a true rank. Would not it be more consistent to replace the rank in the latter case by something based on the following pseudo code ?

man for `qr` says that the function uses LINPACK's DQRDC, while it in fact uses DQRDC2. ``` The QR decomposition of the matrix as computed by LINPACK or LAPACK. The components in the returned value correspond directly to the values returned by DQRDC/DGEQP3/ZGEQP3 ```

When creating a package, I would like a way to tell R that a function with a period in its name is not a method. I'm writing a package now with a modified version of qr.R. R CMD check gives warnings: * checking S3 generic/method consistency ... WARNING qr: function(x, ...) qr.R: function(qr, complete, pivot) See section ‘Generic functions and methods’ of the ‘Writing R Extensions’

I suggest adding a 'pivot' argument to qr.R, to obtain columns in the same order as the original x, so that a <- qr(x) qr.Q(a) %*% qr.R(a, pivot=TRUE) returns x. -------------------------------------------------- # File src/library/base/R/qr.R qr.R <- function(qr, complete = FALSE, pivot = FALSE) { # Args: # qr: a QR decomposition, produced by qr() # complete:

I am testing 'qr' with an admittedly contrived matrix and I am getting different results than I am from another package. The matrix that I am using is: x <- matrix(seq(.1, by=.1, length.out=12), 4) So the whole test is: x <- matrix(seq(.1, by=.1, length.out=12), 4) qr(x) And the output from 'R' is: $qr [,1] [,2] [,3] [1,] -0.5477226 -1.2780193

2017-06-22 19:49 GMT+02:00 Uwe Ligges <ligges at statistik.tu-dortmund.de>: > On 22.06.2017 17:11, Bernd Funovits wrote: >> >> Hello, >> >> I experienced some unexpected behaviour while determining the rank of matrices (sometimes 1x1 matrices): >> base::qr(matrix(1e-20))$rank returns 1 (incorrect) >> base::qr(diag(c(1, 1e-20)))$rank returns 2

On Thu, 19 Apr 2007, brech at delphioutpost.com wrote: > Full_Name: Christian Brechbuehler > Version: 2.4.1 Patched (2007-03-25 r40917) > OS: Linux 2.6.15-27-adm64-xeon; Ubuntu 6.06.1 LTS > Submission from: (NULL) (24.61.47.236) > > > Splus and R have different ideas about what qr.coef(qr()) should return, > which is fine... but I believe that R has a bug in that it is not

I have been struggling to find some informaation on what lm exactly does. I know it uses the QR decomp. However, I was recently faced with a somewhat badly scaled matrix and summary(lm) said Coefficients: ( 4 not defined because of singularities) does anyone know how lm chooses these 4 coef. is it forward building of the model --> drop last when qr sends a non full rank design matrix? My

Hello, I experienced some unexpected behaviour while determining the rank of matrices (sometimes 1x1 matrices): base::qr(matrix(1e-20))$rank returns 1 (incorrect) base::qr(diag(c(1, 1e-20)))$rank returns 2 (incorrect) Best regards, Bernd > R.version _ platform x86_64-w64-mingw32 arch x86_64 os mingw32 system x86_64, mingw32 status major