similar to: Confidence Bands in Polynomial Regression

Dear R-experts, my goal is to visualize the following polynomial regression as a 3D-surface: Z = b0 + b1*X + b2*Y + b3*XY + b4*X^2 + b5*Y^2 I believe that a solution to this problem may be of interest to a wider range of scientists because the problem is a derivative of a more general problem, i.e.: how to describe the relationship between one dependent variable and the DIFFERENCE between two

Hello R users, I've read the posts on this topic, and had a look at the R documentation for nlme, but I can't seem to make this work. I'd like to be able to fit a mixed effects model with crossed random effects, but also be able to specify the covariance matrix structure for the residuals. Here's the syntax using the lmer function in lme4 (which doesn't currently allow

Dear R users, The package 'mfp' that fits fractional polynomial terms to predictors. Example: data(GBSG) f <- mfp(Surv(rfst, cens) ~ fp(age, df = 4, select = 0.05) + fp(prm, df = 4, select = 0.05), family = cox, data = GBSG) print(f) To describe the association between the original predictor, eg. age and risk for different values of age I can plot it the polynomials

Hello, I am using the mda package and in particular the fda routine to classify/predict in terms of color to a set of 20 samples for which i don?t know the color. I preformed a flexible discriminant analysis (FDA) using a set of 147 samples for which i know all the information. My script and data follow in attachment. A total of 23 predictors were considered. 20 of the predictors are

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

Dear list, I have a list of matrices : i1 <- matrix(1:10, nrow = 2, ncol = 5) i2 <- matrix(11:20, nrow = 2, ncol = 5) j <- list(i1 = i1, i2 = i2) I would like to attribute names to each dimension, for each matrix, as follows : $i1 B1 B2 B3 B4 B5 A1 1 3 5 7 9 A2 2 4 6 8 10 $i2 B1 B2 B3 B4 B5 A1 11 13 15 17 19 A2 12 14 16 18 20 However, I have to use lapply function and attribute

Hello, I'm new in R and I want to do one thing that is very easy in excel, however, I cant do it in R. Suppose we have the data frame: data<- data.frame(A=c("a1","a2","a3","a4","a5")) I need to obtain another column in the same data frame (lets say B=c(b1,b2,b3,b4,b5) in the following way: b1=a1/(a1+a2+a3+a4+a5)

Hi, I am hoping some one can help with this. I am using nlme to fit a random coefficients model. It ran for hours before returning Error: Singularity in backsolve at level 0, block 1 The model is > plavix.nlme<-nlme(PLX_NRX~loglike(PLX_NRX,PD4_42D,GAT_34D,VIS_42D,MSL_42D,SPE_ROL,XM2_DUM,THX_DUM,b0,b1,b2,b3,b4,b5,b6,b7,alpha), + data=data, + fixed=list(b0 +

Dear R users, Basically, from the following arbitrary data set: a <- data.frame(id=c(c("A1","A2","A3","A4","A5"),c("A3","A2","A3","A4","A5")),loc=c("B1","B2","B3","B4","B5"),clm=c(rep(("General"),6),rep("Life",4))) > a

Hi All, I have data on the sequence of births for families with completed fertility cycle (in a data frame); the relevant variables are called b1, b2, b3, b4, b5, b6 and record the birth of the first, second, ..., sixth child. So, b1=1 if the first birth is male, b1=2 if the first birth is female, and b1=NA if the family did not record any first birth. Similarly for b2, b3, b4, b5 and b6. I

We have a wireless network that uses 802.1x authentication, in which domain joined computers use their machine credentials to connect. Windows machines do this automatically, and until recently Linux computers could join using wicd, wpa-supplicant, and a simple script that would retrieve the machine password with tdbdump. ( specifically tdbdump -k SECRETS/MACHINE_PASSWORD/DOMAIN

Dear all, Is there any package or function can do the pointwise confidence interval and confidence band for the local polynomial regression? Maybe the local linear regression is enough. Thanks. Regards, Zhen

Hi all, I?m trying to plot confidence intervals for the fitted values I get with my lme model in R. Is there any way I can plot this in the form of a shaded band, like the output of geom_smooth() in ggplot2 package. ggplot2 seems to use only lm, glm, gam, loess and rlm as smoothing methods. Any advice on the functions I should use to accomplish this will be very helpful. Thank you very much.

Hi, I'm trying to reshape my data set from wide to tall format for multilevel modeling. Unfortunately, the function I typically use (make.univ from the multilevel package) does not appear to work with unbalanced data frames, which is what I'm dealing with. Below is an example of the columns of a data frame similar to what I'm working with: ID a1 a2 a4 b2 b3 b4 b5 b6 Below

Hi, does anyone know how do I plot confidence intervals as a shaded band around a curve, rather than as errors bars? many thanks, ulisses. [[alternative HTML version deleted]]

Hello, R experts, Sorry for asking this question again again since I really want a help! I have a two-factor experiment data and like to calculate estimates of interation contrasts say factor A has levels of a1, a2, and B has levels of b1, b2, b3, b4, and b5 with 3 replicates. I am not sure the constrast estimate I got is right using the script below:

Here's another example of my plotmath whipping boy, the Normal distribution. A colleague asks for a Normal plotted above a series of axes that represent various other distributions (T, etc). I want to use vectors of equations in plotmath to do this, but have run into trouble. Now I've isolated the problem down to a relatively small piece of working example code (below). If you would

i know this is not about R. After applying quantile regression with t=0.5,0.6 on the data set WBC( Wisconsin Breast Cancer)with 678 observations and 9 independent variables(inp1,inp2,...inp9) and 1 dependent variable(op) i have got the following results for beta values. when t=0.5(median regression) beta values b1=0.002641,b2=0.045746,b3=0.

Hi, Can anyone help to write a more elegant version of my code? I am sure this can be put into a loop but I am having trouble creating the objects b1,b2,b3,...,etc. b1 <- rigamma(50,1,1) theta1 <- rgamma(50,0.5,(1/b1)) sim1 <- rpois(50,theta1) b2 <- rigamma(50,1,1) theta2 <- rgamma(50,0.5,(1/b2)) sim2 <- rpois(50,theta2) b3 <- rigamma(50,1,1) theta3 <-

Using S+ 6 for Linux and R 1.4.1 Taking verbatim some lines from S-Plus that work perfectly, and running them on R, I get an unexpected error message: R : Copyright 2002, The R Development Core Team Version 1.4.1 (2002-01-30) > library(MASS) > data <- read.table("/usr/local/data/mcmanus.dat",header=T) > mcmanus.frm <-