similar to: Bootstraping GAMs: confidence intervals

Dear R-Users, I am resending this message just to reminder my question regarding the calculation of a bootstrap confidence intervals for a GAM plot. I am trying to apply a bootstrap to a GAM in order to calculate the 95% confidence intervals for a smooth curve obtained by the ?plot.gam? function of the mgcv package. Nonetheless, I am getting some difficulties in transposing the results for

Thanks to the help of people from this forum I was able to bootstrap my data and then apply a model to it. Thanks for all your help. Everything worked out well, but I am having a difficult time getting the new parameter values. I bootstrapped the data 300 times and I want to get the 300 sets of parameter estimates and plot them in Excel. Here is my code:

Dear all, I have a standard method and two alternatives to perform a test, called method A and method B. I have calculated the sensitivity and specificity for standard method vs. method A and standard method vs. method B. Hence, I have two sensitivity values an two specificity values. To be clear, sensitivity and specificity was calculated from: Disease(A) No Disease(Ac) Total Positive

Hi I'm doing a bootstrap of a linear model using: boot.fishpower <- function(data, i){ data <- data[i,] fplm <- lm(log(U)~Q+S+P+B+D, data=data) fp <- coef(fplm) exp(fp) } > boot(logglm.data,boot.fishpower,100) Error in "[<-"(*tmp*, r, , value = statistic(data, i[r, ], ...)) : number of items to replace is not a multiple of replacement length

Dear R-friends, Unfortunately the tables that I "past" on last email gone with bad visual structure. So I send it again. Sorry to do this so confuse. Miltinho ==== Table1 - Bird records State,SampleSite,Species,Bodysize SaoPaulo,Site1,Spp01,4.39 SaoPaulo,Site1,Spp04,4.05 SaoPaulo,Site1,Spp01,2.75 SaoPaulo,Site1,Spp02,8.18 SaoPaulo,Site1,Spp02,0.80

Hi, Does anyone know what the error message mean? > Boot2.Pillai <- function(x, ind) { + x <- as.matrix(x[,2:ncol(x)]) + boot.x <- as.factor(x[ind, 1]) + boot.man <- manova(x ~ boot.x) + summary(manova(boot.man))[[4]][[3]] + } > > man.res <- manova(as.matrix(pl.nosite) ~ + as.factor(plankton.new[,1]))$residuals > boot2.plank <-

Hi. I''m learning AcriveRecord (I''m not building web apps for now, just playing with the database layer). Problem is, when I run the following code: require ''rubygems'' require ''activerecord'' puts ''hi'' It takes about 6 seconds. I''m using a slow computer. Now, it''s no fun playing when it takes so long to

I'm having trouble getting openldap through its initial setup. I created a /etc/openldap/slap.conf file with a default rootdn and rootpw, and they didn't seem to take effect. After much wailing and gnashing of teeth I found that if there is a config directory at /etc/openldap/slapd.d, it will ignore slapd.conf. I can't figure out how to translate slapd.conf into the (new?) standard

Dear R-experts, I have a table with following collumns: State, SamplePlot, Species and BodySize. I sampled bird species at 34 SamplePlots and 5 States (regions) monthly during two years. On each bird record I measured bodysize and identified the species. So I have many records of each species (about 150 species) at each SamplePlot and each Region (State). Now I would like bootstrap

This command cdmoutcome<- glm(log(value)~factor(year) > +log(gdppcpppconst)+log(gdppcpppconstAII) > +log(co2eemisspc)+log(co2eemisspcAII) > +log(dist) > +fdiboth > +odapartnertohost > +corrupt > +log(infraindex) > +litrate > +africa >

Dear list, I?m wondering if there is something analogous to the TukeyHSD function that could be used for parametric terms in a GAM. I?m using the mgcv package to fit models that have some continuous predictors (modeled as smooth terms) and a single categorical predictor. I would like to do post hoc test on the categorical predictor in the models where it is significant. Any suggestions?

Hi there, I was wondering if by the way the isotropic bivariate function works in the mgcv package, one can use highly correlated coordinates (given the shape of the study area) without worrying about the potential problems of correlation between explanatory variables, i.e., does s(LON, LAT) deal with that by considering their combined effect? Although this sounds more like a statistical

Dear list, I'm interested in fitting a generalized additive model in which some of the covariates are derived quantities and known measurement errors, and I am not sure how to incorporate the uncertainty of these covariates into the model fitting process. Could anyone point me towards relevant examples or documentation? Thanks in advance, Julian Julian M. Burgos Fisheries Acoustics

Hi I have been searching for goodness-of-fit tests (or lack of fit tests) for GAMs and cannot find anything. My problem is: after fitting a GAM to mortality data (smoothing crude estimated rates of mortality - a process called graduation in the actuarial literature), (1) how to assess the fit of the model with reference to "adherence to data" for the fitted model (I do not think the

Hi all, I wonder if it's possible to include a double interaction in a GAM formula. Example: If I do this: mod=gam(energy~s(size, *by=color, by=sex*, k=5) + temperature, ...) I get the interaction betwen size*color and size*sex. But I need size*color*sex, being size a smoother. I've created a new variable (colorsex) which combines all the level of both color (2 levels) and sex (2

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, I am looking at the effects of two explanatory variables on chlorophyll. The data are an annual time-series (so are autocorrelated) and the relationships are non-linear. I want to account for autocorrelation in my model. The model I am trying to use is this: Library(mgcv) gam1 <-gam(Chl~s(wintersecchi)+s(SST),family=gaussian, na.action=na.omit, correlation=corAR1(form =~

Hello. I'm trying to analyze data, which is looking at the relationship between temperature and survival for fish (from fertilization to emergence). Looking at the raw data, there appears to be a bell shaped relationship. Ordinarily for survival data, I would run a generalized linear model (because the data has a binomial error structure). However, I am thinking that running a generalized

Dear list, I?m using the ?mgcv? package to fit some GAMs. Some of my covariates are derived quantities and have an associated standard error, so I would like to incorporate this uncertainty into the GAM estimation process. Ideally, during the estimation process less importance would be given to observations whose covariates have high standard errors. The gam() function in the ?mgcv? package

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