similar to: please help! what are the different using log-link function and log transformation?

I'm beginner in R! I have a lot of problems on R..... I have three questions about GAM 1. What is the function of Gaussian distribution in GAM?(if I choose family is Gaussian) Is it used in the predictand value (Y)? 2. How to plot a graph the gam function? For example: y<-gam(a~s(b),family=gaussian (link=log) ,Data) how to plot x axis is s(b) and y axis is log a??? 3. if I use GAM to

First of all, I have GAMs noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian (link=log),groupD,methods=REML) Then I type " summary(noxd)". and show Family: gaussian Link function: log Formula: newNO2 ~ pressure

hi! I hope all of you can help me this question for example GAMs: ozonea<-gam(newozone~ pressure+maxtemp+s(avetemp,bs="cr")+s(ratio,bs="cr"),family=gaussian (link=log),groupA,methods=REML) formula(ozonea) newozone ~ pressure + maxtemp + s(avetemp, bs = "cr") + s(ratio,bs = "cr") #formula of gams coef(ozonea) # extract the coefficient of GAMs

I have 5 GAMs ( model1, model2, model3, model4 and model5) Before I use some data X(predictor -January to June data) to form a equation and calculate the expected value of Y (predictand -January to June). After variable selection, GAMs (Model 1)were bulit up! R-square :0.40 NOW, I want to use new X'( predictor -July - December data) and put into Model 1, then get the expected value of Y'

Hi Hadley, Thanks for the counterpoint. Response below. On Thu, May 16, 2019 at 1:59 PM Hadley Wickham <h.wickham at gmail.com> wrote: > The existing behaviour seems inutitive to me. I would consider these > invariants for n vector x_i's each with size m: > > * nrow(rbind(x_1, x_2, ..., x_n)) equals n > Personally, no I wouldn't. I would consider m==0 a degenerate

Dear All, Suppose I have a vector X = (x_1, x_2, ...., x_n), X_sort = sort(X) = (x_(1), x_(2), ... , x(n) ), and I would like to know the original position of these ordered x_(i) in X, how can I do it? case 1: all values are unique x <- c( 3, 5, 4, 6) x.sort <- sort(x) # # I would like to obtain a vector (1, 3, 2, 4) which indicates that 3 in x is still the 1st element in x.sort, 5 is at

Hi all, Apologies if this has been asked before (a quick google didn't find it for me),and I know this is a case of behaving as documented but its so unintuitive (to me at least) that I figured I'd bring it up here anyway. I figure its probably going to not be changed, but I'm happy to submit a patch if this is something R-core feels can/should change. So I recently got bitten by

I noticed that there is no generalised additive model functions in R (1.1.1) ... is there a package that implements them? Thanks Prasad ***************************************************************** Mr. Anantha Prasad, Ecologist/GIS Specialist USDA Forest Service, 359 Main Rd. Delaware OHIO 43015 USA Ph: 740-368-0103 Email: aprasad at fs.fed.us Web:

i read a book from WOOD, there's an example which is talking about the pollutant. library(gamair) library(mgcv) y<-gam(death~s(time,bs="cr",k=200)+s(pm10median,bs="cr")+s(so2median,bs="cr")+s(o3median,bs="cr")+s(tmpd,bs="cr"),data=chicago,family=Possion) lag.sum<-function(a,10,11) {n<-length(a) b<-rep(0,n-11) for(i in 0:(11-10))

Dear R users, I'm new to both R and to this list and would like to get advice on how to build generalized additive models in R. Based on the description of gam, which I found on the R website, I specified the following model: model1<-gam(ST~s(MOWST1),family=binomial,data=strikes.S), in which ST is my binary response variable and MOWST1 is a categorical independent variable. I get the

Dear all, The help page on the t distribution says: The most used applications are power calculations for t-tests: Let T= (mX - m0) / (S/sqrt(n)) where mX is the `mean' and S the sample standard deviation (`sd') of X_1,X_2,...,X_n which are i.i.d. N(mu,sigma^2). Then T is distributed as non-centrally t with `df'= n-1 degrees of freedom and non-centrality

Greetings, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,

Hi, I need further help with my GAMs. Most models I test are very obviously non-linear. Yet, to be on the safe side, I report the significance of the smooth (default output of mgcv's summary.gam) and confirm it deviates significantly from linearity. I do the latter by fitting a second model where the same predictor is entered without the s(), and then use anova.gam to compare the

Hi R community! I am analyzing the data set "motorins" in the package "faraway" by using the generalized additive model. it shows the following error. Can some one suggest me the right way? library(faraway) data(motorins) motori <- motorins[motorins$Zone==1,] library(mgcv) >amgam <- gam(log(Payment) ~ offset(log(Insured))+ s(as.numeric(Kilometres)) + s(Bonus) + Make +

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

Hello, I'm a Korean researcher who have been started to learn the "R" package. I want to make gam model and AIC value of the model to compare several models. I did the GAM model, but there were error for AIC. SO, how can I do? pleas help me!!! I did like below; > a.fit <- gam(pi~ s(t1r), family = gaussian(link="log")) >

On 04/11/2017 10:20 PM, Daniel Nordlund wrote: > Tirthankar, > > "random number generators" do not produce random numbers. Any given > generator produces a fixed sequence of numbers that appear to meet > various tests of randomness. By picking a seed you enter that sequence > in a particular place and subsequent numbers in the sequence appear to > be unrelated.

Dear R-devel, I am experiencing issues with running GAM models using mclapply, it fails to return any values if the data input becomes large. For example here the code runs fine with a df of 100 rows, but fails at 1000. library(mgcv) library(parallel) > df <- data.frame( + x = 1:100, + y = 1:100 + ) > > mclapply(1:2, function(i, df) { + fit <- gam(y ~ s(x, bs =

Hi again! I have a question about R. I have done gam in previous version of R with "mgcv" package and saved the workspace. This workspace contains different models and I will do prediction by these GAMs. However, I install new version of R. and use the same workspace. when I type summary(models), and the error message showed Error in Predict.matrix.cr.smooth(object, dk$data) : F is