similar to: A question about using “by” in GAM model fitting of interaction between smooth terms and factor

Hello, everybody, There is the first time for me to post a question, because I really cannot find answer from books, websites or my colleagues. Thank you in advance for your help! I am running likelihood ratio test to find if the simpler model is not significant from more complicated model. However, when I run LRT to compare them, the test did not return F value and p-value for me. What's the

Dear R users, my dataframe looks like this head(dat) Id sex byear age 1 300 m 2003 50 2 300 m 2003 36 3 402 f 2003 29 4 402 f 2003 21 5 402 f 2003 64 6 150 m 2005 43 ... ...(where Id is just the Identification number of Individual, sex (male or female), byear (=birthyear)) now, I 'd like to add a column, where each Individual gets an automated number starting

hi ,I am writing a soft package based on R. But when I try to read a configure file showed as below. How can I read the parameter in this text file. How can I read the parameter into each variable in this file ? configinfo<-scan(file(configfile),ok=TRUE,n=-1) scan seems need every line have same column ? configinfo <- readLines(configfile,ok=TRUE,n=-1) methodnum <-

Dear R users, I have a problem with multicollinearity in mixed models and I am using lmer in package lme4. From previous mailing list, I learn of a reply "http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg38537.html" which states that if not for interpretation but just for prediction, multicollinearity does not matter much. However, I am using mixed model to interpret something,

Hi, I have three original curves as follows, n<-seq(20,200,by=10) t<-c(0.1138, 0.1639, 0.2051, 0.2473, 0.2890, 0.3304, 0.3827, 0.4075, 0.4618, 0.4944, 0.5209, 0.5562, 0.5935, 0.6197, 0.6523, 0.6771, 0.6984, 0.7209, 0.7453) es<-c(0.3682, 0.4268, 0.5585, 0.6095, 0.7023, 0.7534, 0.8225, 0.8471, 0.8964, 0.9098, 0.9371, 0.9514, 0.9685, 0.9747, 0.9812, 0.9859, 0.9905, 0.9923, 0.9940)

Hi , I am trying to get the estimate of smooth functions from a gam model by in the library(gam). What I get by constructing this model below are " edf. values ...and p-values" for the smooths functions and intercept. model <- gam(y~ s(height)+ s(weight)+s(time)+s(pol)) and I also get the smoothing parameters estimation by typing coef(model). The difficulty I am having

Hi, I have modified the USJudgeRatings data (available in R) to illustrate my question. # Use the first 4 variables of USJudgeRatings and add a group variable with two levels USJudgeRatings <- USJudgeRatings[,1:4] USJudgeRatings$group <- factor(c(rep(1, 22), rep(0, 21))) # I can draw a pairs graph where members of each group are drawn in different colors: pairs(USJudgeRatings[,1:4], col

Dear useRs, I am using mgcv version 1.7-16. When I create a model with a few non-linear terms and a random intercept for (in my case) country using s(Country,bs="re"), the representative line in my model (i.e. approximate significance of smooth terms) for the random intercept reads: edf Ref.df F p-value s(Country) 36.127 58.551 0.644

In the HistData package, I have a data frame, PearsonLee, containing observations on heights of parent and child, in weighted form: library(HistData) > str(PearsonLee) 'data.frame': 746 obs. of 6 variables: $ child : num 59.5 59.5 59.5 60.5 60.5 61.5 61.5 61.5 61.5 61.5 ... $ parent : num 62.5 63.5 64.5 62.5 66.5 59.5 60.5 62.5 63.5 64.5 ... $ frequency: num 0.5 0.5

Hi folks, I originally tried R-SIG-Mixed-Models for this one (https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q3/004170.html), but I think that the final steps to a solution aren't mixed-model specific, so I thought I'd ask my final questions here. I used gamm4 to fit a generalized additive mixed model to data from a AxBxC design, where A is a random effect (human participants in

In the summary of the gam object produced by gamm, the "Approximate significance of smooth terms" appears to be a test of the improvement in fit over a linear model, rather than a test of the significance of the overall effect of x on y: test.gamm<-gamm(y~te(x, bs="cr"), random=list(grp=~1)) summary(test.gamm$gam) . . . Approximate significance of smooth terms:

Hi, all, Does anyone know how to get the functional form of the smooth terms in GAM? eg. I fit y=a+b*s(x) where s is the smooth function. After fitting this model with GAM in R, I want to know the form of the s(x). Any suggestion is appreciated. Thanks, Jin --------------------------------- Ahhh...imagining that irresistible "new car" smell?

hi, i'm using gam() function from package mgcv with default option (edf estimated by GCV). >G=gam(y ~ s(x0, k = 5) + s(x1) + s(x2, k = 3)) >SG=summary(G) Formula: y ~ +s(x0, k = 5) + s(x1) + s(x2, k = 3) Parametric coefficients: Estimate std. err. t ratio Pr(>|t|) (Intercept) 3.462e+07 1.965e+05 176.2 < 2.22e-16 Approximate significance of smooth

Dear R help list, This is a long post so apologies in advance. I am estimating a model with the mgcv package, which has several covariates both linear and smooth terms. For 1 or 2 of these smooth terms, I "know" that the truth is monotonic and downward sloping. I am aware that a new package "scam" exists for this kind of thing, but I am in the unfortunate situation that I am

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

I'd like to compare tests based on the mixed model representation of additive models, testing among others y=f(x1)+f(x2) vs y=f(x1)+f(x2)+f(x1,x2) (testing for additivity) In mixed model representation, where X represents the unpenalized part of the spline functions and Z the "wiggly" parts, this would be: y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 vs y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 + Z_12

I want to use gamm to generate smoothed trend line for three groups identified by dummy variable genea and geneb. My question is how to add in an interaction term between the time and another dummy variable such as gender? fitm<-gamm(change_gfr~ genea+geneb+s(timea_n,bs="ps")+s(timeb_n,bs="ps")+s(timec_n,bs="ps"),data=mm,random=list(time_n=~1|PID)) -- View this

Hi R-users, I'd like to smooth a matrix to dismiss spikes and to interpolate in plane example of a matrix: Map[1:3,1:3] [,1] [,2] [,3]... [1,] 34.4 34.2 35.1 [2,] 33.4 34.2 35.4 [3,] 34.1 33.2 32.1 .... dim(Map)[1] =/= dim(Map)[2] What functions can I use? Thanks a lot for any response, M [[alternative HTML version deleted]]

hi, I have been trying to do this in R (have implemented it in Excel) but I have been using a very inefficent way (loops etc.). I have matrix A (columns are years and ages are rows) and matrix B (columns are birth yrs and rows are ages) I would like to first turn matrix A into matrix B And then I would like to convert matrix B back again to the original matrix A. (I have left out details of