similar to: Plot different regression models on one graph

Package: logcheck-database Version: 1.2.68 Severity: minor openssh-server version 1:5.1p1-2 ^\w{3} [ :0-9]{11} [._[:alnum:]-]+ sshd\[[0-9]+\]: reverse mapping checking getaddrinfo for [._[:alnum:]-]+ failed - POSSIBLE BREAK-?IN ATTEMPT!$ should look like ^\w{3} [ :0-9]{11} [._[:alnum:]-]+ sshd\[[0-9]+\]: reverse mapping checking getaddrinfo for [._[:alnum:]-]+ \[[.[:alnum:]:]+\] failed -

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Package: logcheck Version: 1.2.68 Severity: important I upgraded my lenny server today. I guess the previous update is something like two weeks ago. Now logcheck started sending mails containing only this perl error: This email is sent by logcheck. If you wish to no-longer receive it, you can either deinstall the logcheck package or modify its configuration file (/etc/logcheck/logcheck.conf).

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,

Hallo, I'm facing a problem and I would really appreciate your support. I have to translate some Matalb code in R that I don't know very well but I would like to. I have to interpolate 5 point with a cubic spline function and then I expect my function returns the Y value as output a specific X value inside the evaluation range. Let's suppose that: 1- *X = [-10, -5, 0, 5, 10]* 2

Dear all, Is the restricted cubic spline function working properly in the glm model? We used glm(y~rcs(x,5), family=binomial) but it seems that for some theoretical reasons the rcs, restricted cubic spline function can not be fitted by a glm function. Is this correct? Regards, Jan ((Originally, we used lrm(y~ rcs(x,5)) but we couldn't find how to derive the AIC value of the fitted model.

Greetings, I would like to use a cubic spline or smoother to model the fixed effects within nlme. So far the only smoother I have been able to get to run successfully in nlme is smooth(). I tried smooth.spline: fixed=list(lKa~1,lCL~smooth.spline(BSA, df=3)) the error I got was the following. Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : invalid

Hi all, I?m working with a dataset with 9 columns and 2000 rows. Each row represents an individual and one of the columns represents the volume of that individual (measured in cubic meters). I?d like to select a sample from this dataset (without considering any probability of the rows) in which the sum of the volume of the individuals in that sample >= 100 cubic m. I?ll appreciate any

Hi, all I am trying to get the basis matrix and penalty matrix for natural cubic splines. In the "splines" package of R,"ns" can generate the B-spline basis matrix for a natural cubic spline. How can I get the basis matrix and penalty matrix for natural cubic spline. Thanks a lot! Lee [[alternative HTML version deleted]]

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

I would like to fit a logistic regression using a smothing spline, where the spline is a piecewise cubic polynomial. Is the knots option used to define the subintervals for each piece of the cubic spline? If yes and there are k knots, then why does the coefficients field in the returned object from gam only list k coefficients? Shouldn't there be 4k -4 coefficients? Sincerely, Bill

For compile and link Basicmath files (using shell script): llvm-gcc -emit-llvm basicmath_small.c -c -o basicmath_small.bc llvm-gcc -emit-llvm cubic.c -c -o cubic.bc llvm-gcc -emit-llvm isqrt.c -c -o isqrt.bc llvm-gcc -emit-llvm rad2deg.c -c -o rad2deg.bc llvm-link basicmath_small.bc cubic.bc isqrt.bc rad2deg.bc -o basicmath.bc

Suppose I have two var x and y,now I want to fits a natural cubic spline in x to y,at the same time create new var containing the smoothed values of y. How can I get it?

I have some data which consists of time series for a number of sites. It appears that there is not much autocorrelation in the data and I have fitted a cubic for each site using lm. I would like to obtain a lattice plot with one panel for each site and showing the original data, and the fitted cubic. The closest I have got to doing what I want is: fit <- fitted(paraslm1) temp <-

I am trying to understand idea for splint, but it use Fortran code. Does anyone know how to see that Fortran code? Is splint a not-knot spline method? Thanks, jfm [[alternative HTML version deleted]]

Hello,   does anyone have an example on how to use restricted cubic splines function rcs within survfit.cph, if cph (Cox Proportional Hazard Regression) was done with restricted cubic splines (which I made to work)? Thank you. > [[alternative HTML version deleted]]

Dear all, I'm attempting to plot a cubic relationship between two variables controlling for the effects of a third variable. In this short example, I'm trying to use AGE to predict CORTEX while controlling for the effects of TIV (total intracranial volume): ######################## cortex = rnorm(100, mean=0.5, sd=0.5) age = rnorm(100, mean=10, sd=2) tiv = rnorm(100, mean=1000,

I have a function that fits polynomial models for the orders in n: lmn <- function(d,n){ models=list() for(i in n){ models[[i]]=lm(y~poly(x,i),data=d) } return(models) } My data is: > d=data.frame(x=1:10,y=runif(10)) So first just do it for a cubic: > mmn = lmn(d,3) > predict(mmn[[3]]) 1 2 3 4 5 6 7 8

Hi, I'm trying to compile the basicmath benchmark from the MiBench suite to the MIPS target. However, when I call llc with the linked llvm bitcode, the compilation gives an error apparently related to some unsupported feature. The same error occurs when I select -march=arm, but it works for X86. In details: Commands I used to compile basicmath: clang -static -O3 basicmath_small.c rad2deg.c

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