mgcv

I have used smooth.spline to estimate a cubic spline for my data. But when I calculate the 90% point-wise confidence interval using equation, the results seems to be a little bit off. Can someone please tell me if I did it wrongly? I am just wondering if there is a function that can automatically calculate a point-wise interval band associated with smooth.spline function. boneMaleSmooth = smooth.spline( bone[males,"age"], bone[males,"spnbmd"], cv=FALSE) error90_male = qnorm(.95)*sd(boneMaleSmooth$x)/sqrt(length(boneMaleSmooth$x)) plot(boneMaleSmooth, ylim=c(-0.5,0.5), col="blue", lwd=3, type=

I have a quite simple time series data set consisting of annual averages of a singe variable ("AVERAGE"). I wish to investigate the rate of change (1st derivative) and acceleration (2nd derivative) and associated standard errors of the "trend" component of the time series. I have obtained the "trend" using the GAM and PREDICT functions of MGCV simply as follows: A <- gam(AVERAGE ~ s(YEAR), data=DF, na.action=na.omit) B <- predict(A, type="response", se.fit=TRUE) I have determined derivatives through 2 separate methods, applying a high DoF cubic smooth spline and via first and second

I am fairly new to R and presently reading a book “Generalized Additive Models”, an Introduction with R by Wood (2006) and going through some of the exercises, particularly the part on air pollution and death which is my area of interest. Using the mgcv package I run the following model. library(gamair) library(mgcv) data(chicago) ap1<-gam(death ~ pm10median + so2median + o3median +s(time,bs="cr",k=200)+ s(tmpd,bs="cr"), data=chicago,family=poisson) How can I extract the effect estimates of pm10median and 95% CI of x and export the output to CSV or any other option? Save the summary of the

This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 5 years ago . Learn more . When applying gam.check in the mgcv package, R produces some residual plots and basis dimension output. Is there a way to only produce the plots and not the printed output? library(mgcv) set.seed(0) dat <- gamSim(1,n=200) b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3), data=dat) plot(b, pages=1) gam.check(b, pch=19, cex=.3) There are four plots, from top left, moving down and across we have: A QQ plot of the residuals A histogram of the residuals A plot of residuals vs the

I'm searching for a method to add the predicted (real, not standardized) values of every single variable in my model > model<-gam(LN_Brutto~s(agecont,by=Sex)+factor(Sex)+te(Month,Age)+s(Month,by=Sex), data=bears) This is the summary of my model: > summary(m13) Family: gaussian Link function: identity Formula: LN_Brutto ~ s(agecont, by = Sex) + factor(Sex) + te(Month, Age) + s(Month, by = Sex) Parametric coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.32057 0.01071 403.34 <2e-16 *** factor(Sex)m 0.27708 0.01376 20.14 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0

I am trying to extract the placement of the knots from a GAM model in order to delineate my predictor variable into categories for another model. My data contains a binary response variable (used) and a continuous predictor (open). data <- data.frame(Used = rep(c(1,0,0,0),1250), Open = round(runif(5000,0,50), 0)) I fit the GAM as such: mod <- gam(Used ~ s(Open), binomial, data = data) I can get the predicted values, and the model matrix etc with either type=c("response", "lpmatrix") within the predict.gam function but I am struggling with out to extract the knot locations at which which the

I am trying to run a general additive model using the mgcv package, but I keep getting a model.frame.default error: Error in model.frame.default(formula = Presence ~ Sex + wind_speed + baro + : attempt to apply non-function Here is the code I am using (I am using "bam()" because of the size of the dataset): stormGAM <- bam(Presence~Sex+wind_speed+s(wind_direc)+baro+s(SST_C)+as.factor(daynight), data=PJstorm_alldata, family=binomial, na.action=TRUE) and here is what the data looks like: 'data.frame': 31795 obs. of 25 variables: $ Con_hour : num 20127330 20127340 20127350 20127360 20127370 ... $

I am trying to plot a gam object in R, which I made with the gam package. I receive the same error reported in Error in 1:object$nsdf : argument of length 0 when using plot.gam . However, the solution found there, updating to the latest versions (I think), is not working for me. I am running R 3.3.1, gam 1.12, and mgcv 1.8.12 (mgcv is where the plot.gam function is from). Unfortunately, I cannot share the data I am working with. However, the following code -- pulled directly from the p.294 of Intro. to Statistical Learning with R -- reproduces the error for me: library(gam) library(ISLR) #

Detaching packages in R isnt good practice (see ?detach ), but for some reasons I have to switch between the packages gam and mgcv . Once mgcv was attached and detached (and all the dependencies in the namespace unloaded!), functions of gam produce some strange errors (please forgive the terminology). It seems that - even though unloaded one step before - mgcv and friends are back in the namespace and function dispatching goes wrong. Does anyone had the same problem before? # fresh session t.s1 <- search() t.lN1 <- loadedNamespaces() # some dummy data data <-data.frame(is.exc=sample(x=c(0,1)