similar to: Question about predict function

Hi, I am fairly new to GAM and started using package mgcv. I like the fact that optimal smoothing is automatically used (i.e. df are not determined a priori but calculated by the gam procedure). But the mgcv manual warns that p-level for the smooth can be underestimated when df are estimated by the model. Most of the time my p-levels are so small that even doubling them would not result

Hi All, When we run the command : summary ( newmod<-gam(Dlq~ formula,family,,data) ) in R, the output would the effect of smoothness in R. As of now to calculate the probability I am following the below approach: 1) Run the plot of the GAM , interpret the curves 2) Re Run the Regression as a GLM after taking into account the non linear terms in step1 3) Calculate the probability from

Hi - I have a series of 9 GAM regressions with about 5 parametric effects and three non-parametric effects in each. What is a good library or command for turning GAM outputs into publication-ready regression tables? I tried apsrtable and the mtable command in memisc but neither seemed to work with the gam output. I'd be okay with two separate tables - one for the parametric effects and

Hi, All, I am a beginner for R. Now I have installed R 2.5.1 in Window environment. After I run a program such as "gam" I would like to display a plot for the object. The following is an example. When I did this, only the last plot was presented on my screen. How can I get a plot before the last plot? I mean if the object has several plots how can I get those? "gam.object <-

Is it easy or difficult to label the abscissa of a scatter graph as 1/trueScaleValue at that point? -- View this message in context: http://r.789695.n4.nabble.com/labeling-abscissa-using-a-function-of-the-plotted-scale-tp4678075.html Sent from the R help mailing list archive at Nabble.com.

Hi all. I have defined a plot thus: par(mar=c(5,5,4,5),las=1, xpd=NA) plot(Day, Ym1Imp, ylim=c(0,100), type="b", bty="l", main="Ym1 Expression", cex=1.3, xaxt="n", yaxt="n") #plot implant data axis(side=1, at=c(0,1,3,5,7,10,14,21), labels=c(0,1,3,5,7,10,14,21)) # label x axis mtext("Day", side =1, at=10, line=3, cex=1.2) # title x

I've got a small problem. I have some observational data (environmental samples: abiotic explanatory variable and biological response) to which I've fitted both a multiple linear regression model and also a gam (mgcv) using smooths for each term. The gam clearly fits far better than the lm model based on AIC (difference in AIC ~ 8), in addition the adjusted R squared for the gam is

I'm fitting a Poisson gam model, say model<-gam(a65tm~as.factor(day.week )+as.factor(week)+offset(log(pop65))+s(time,k=10,bs="cr",fx=FALSE,by=NA,m=1),sp=c( 0.001),data=dati1,family=poisson) Currently I've difficulties in obtaining right predictions by using gam.predict function with MGCV package in R version 2.2.1 (see below my syntax).

First of all, thanks to everyone who answers these questions - it's most helpful. I'm new to R and despite searching have not found an example of what I want to do (there are some good beginner's guides and a lot of complex plots, but I haven't found this). I would like to plot two variables against the same abscissa values. They have different scales. I've found how to make

Hello everyone. I am doing a logistic gam (package mgcv) on a pretty large dataframe (130.000 cases with 100 variables). Because of that, the gam is fitted on a random subset of 10000. Now when I want to predict the values for the rest of the data, I get the following error: > gam.basis_alleakti.1.pr=predict(gam.basis_alleakti.1, +

Sometimes, for specific models, I get this error from predict.gam in library mgcv: Error in complete.cases(object) : negative length vectors are not allowed Here's an example: model.calibrate <- gam(meansalesw ~ s(tscore,bs="cs",k=4), data=toplot, weights=weight, gam.method="perf.magic") > test <- predict(model.calibrate,newdata) Error in

Readers, I have looked at various documents hosted on the web site; I couldn't find anything on interpolation. So I started r and accessed the help (help.start()). (by the way is it possible to configure r to open help in opera instead of firefox?) Initially I read the help for the akima package but couldn't understand it. Next I tried the asplines package help. I tried to copy the

Hello I'm using function gam from package mgcv to fit splines. ?When I try to make a prediction slightly beyond the original 'x' range, I get this error: > A = runif(50,1,149) > B = sqrt(A) + rnorm(50) > range(A) [1] 3.289136 145.342961 > > > fit1 = gam(B ~ s(A, bs="ps"), outer.ok=TRUE) > predict(fit1, newdata=data.frame(A=149.9), outer.ok=TRUE) Error

Hi all, I am currently trying to compare different plant occurrence prediction maps generated in R and exported into GRASS. One of these maps was generated from a glm fitted to some data, and subsequently applying this glm model to a wider region using predict.glm. The outcome here was a probability of occurrence. The second map I generated using a gam (mgcv), however, this map seems to have

Hi everybody, I am using predict.gam() now. I but it seems there is no such option to get standard errors of the predicted values. I tried to set se=T or se.fit=T but no use. If you know anything about that please let me know. Thanks very much. Kevin. [[alternative HTML version deleted]]

Dear list, I would like to study the dynamics of functions using R (instead of mathematica e.g.), i.e. the behavior of points under iteration of a function. So I tried (in vain) writing a function myfunction <- function(f,n,x){...} in order to compute f^{n}(x), f^{n}(x) being the function f composed with itself n-1 times. n is a natural number, and the argument x is the abscissa of the point I

? data=read.table("http://statcourse.com/research/boston.csv", , sep=",", header = TRUE) ? library(rpart) ? fit=rpart (MV~ CRIM+ZN+INDUS+CHAS+NOX+RM+AGE+DIS+RAD+TAX+ PT+B+LSTAT) predict(fit,data[4,]) plot only reveals part of the tree in contrast to the results on obtains with CART or C5 -------- Original Message -------- Subject: Re: [R] help with rpart From: Sarah

Recently I was using GAM and couldn't help noticing the following incoherence in prediction: > data(gam.data) > data(gam.newdata) > gam.object <- gam(y ~ s(x,6) + z, data=gam.data) > predict(gam.object)[1] 1 0.8017407 > predict(gam.object,data.frame(x=gam.data$x[1],z=gam.data$z[1])) 1 0.1668452 I would expect that using two types of predict arguments

All: I am trying to use gam in a scatterplot smoothing problem. The data being smoothed have greater 1000 observation and have multiple "humps". I can smooth the data fine using a function something like: out <- ksmooth(x,y,"normal",bandwidth=0.25) plot(x,out$y,type="l") The problem is when I try to fit the same data using gam out <-

Dear useRs, I am using the gamm function in the mgcv package to model a smooth relationship between a covariate and my dependent variable, while allowing for quantification of the subjectwise variability in the smooths. What I would like to do is to make subjectwise predictions for plotting purposes which account for the random smooth components of the fit. An example. (sessionInfo() is at