gam

问题 The mgcv package in R lets you estimate smooth regressions on multiple variables. Here is an example: library(mgcv) set.seed(2) ## simulate some data... dat = gamSim(1,n=400,dist="normal",scale=2) mod = gam(y~te(x0, x1),data=dat) par(mfrow = c(2,1)) plot(mod) plot(mod, xlim = c(0, 0.5)) When I run this, the two plots look exactly the same. Why doesn't xlim = c(0, 0.5) restrict the range of the horizontal axis? How can I achieve this? Update on my session info: > sessionInfo() R version 3.0.1

问题 Apologies if the answer is obvious but I've spent quite some time trying to use a custom link function in mgcv.gam In short, I want to use a modified probit link from package psyphy ( I want to use psyphy.probit_2asym, I call it custom_link ) I can create a {stats}family object with this link and use it in the 'family' argument of glm. m <- glm(y~x, family=binomial(link=custom_link), ... ) It does not work when used as an argument for {mgcv}gam m <- gam(y~s(x), family=binomial(link=custom

问题 I'm trying to explore the difference in how the "gam" function works in the mgcv package versus the gam package. But, I'm not able to run both gam functions in one R session. I thought if I preface with mgcv::gam or gam::gam it would be able to run the right function, but it looks like I have to detach mgcv in order to run the gam function in the gam package. library(ISLR) library(mgcv) library(gam) # I get an error message when it runs this gam.m3 <- gam::gam(wage~s(year,4)+s(age,5)

问题 Background I am trying to replicate figure 2.6 in the book An Introduction to Statistical Learning: A rough thin-plate spline fit to the Income data from Figure 2.3. This fit makes zero errors on the training data. What have I tried so far? I tried to replicate the previous figure 2.5, a smooth thin-plate spline fit, not sure if succesfully. income_2 <- read.csv("http://www-bcf.usc.edu/~gareth/ISL/Income2.csv") library(mgcv) model1 <- gam(Income ~ te(Education, Seniority, bs=c("tp", "tp")),

问题 I am using the following geoadditive model library(gamair) library(mgcv) data(mack) mack$log.net.area <- log(mack$net.area) gm2 <- gam(egg.count ~ s(lon,lat,bs="gp",k=100,m=c(2,10,1)) + s(I(b.depth^.5)) + s(c.dist) + s(temp.20m) + offset(log.net.area), data = mack, family = tw, method = "REML") Here I am using an exponential covariance function with range = 10 and power = 1 ( m=c(2,10,1) ). How can I retrieve from the results the variogram parameters (nugget, sill)? I couldn't find anything

问题 I don't understand why the below two gam models produce different results. The only difference is in one of the models I added the namespace specifier gam:: before the functions gam and s . I want to do this because I am exploring the differences between running the gam function in the gam package and in the mgcv package. library(ISLR) library(gam) gam.m3 <- gam::gam(wage ~ gam::s(year,4) + gam::s(age,5) + education,data=Wage) gam.m3.orig <- gam(wage ~ s(year,4) + s(age,5) + education, data

问题 I am using the mgcv package to model the ozone pollution concentration according to some environmental covariates. The model takes the form : model1 <- gam(O3 ~ s(X, Y, bs = "tp", k = 10) + wd + s(date, bs = "cc", k = 100) + district, data = mydata, family = gaussian(link ="log"), na.action = "na.omit", method = "REML") And here is the structure of covariates: > str(mydata) 'data.frame': 7100 obs. of 286 variables: $ date : Date, format: "2016-01-01" "2016-01-01" "2016-01-01" ... $ O3 : num 0

问题 This question already has answers here : R plot.gam Error “Error in 1:object$nsdf : argument of length 0” (2 answers) Closed last year . I am seeing the error message when trying to plot a gam object in R: Error in 1:object$nsdf : argument of length 0 I am using the basic command: plot(myGamObject) This error is not specific to any data I am using as it also comes up at the end of the routine: example(gam) For completeness, here is some sample code: library(ISLR) library(gam) gam7 <- gam(wage

问题 I produced the below graph using ggplot2. PlotEchi = ggplot(data=Echinoidea, aes(x=Year, y=mean, group = aspect, linetype = aspect, shape=aspect)) + geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.025, position=pd) + geom_point(position=pd, size=2) + geom_smooth(method = "gam", formula = y~s(x, k=3), se=F, size = 0.5,colour="black") + xlab("") + ylab("Abundance (mean +/- SE)") + facet_wrap(~ species, scales = "free", ncol=1) + scale_y_continuous(limits=c(min(y=0), max(Echinoidea$mean

问题 Apologies if this is better suited in CrossValidated. I am fitting GAM models to binomial data using the mgcv package in R. One of the covariates is periodic, so I am specifying the bs = "cc" cyclic cubic spline. I am doing this in a cross validation framework, but when I go to fit my holdout data using the predict function I get the following error: Error in pred.mat(x, object$xp, object$BD) : can't predict outside range of knots with periodic smoother Here is some code that should replicate