search for: gam2

...x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1) x3 <- runif(n, 0, 1) f0 <- function(x) 2 * sin(pi * x) f1 <- function(x) exp(2 * x) f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10 f <- f0(x0) + f1(x1) + f2(x2) g<-exp(f/4) y<-rpois(rep(1,n),g) mean.y <- mean(y) gam2 <- gam(y~ s(x2), poisson) # to plot on the response scale val.for.pred <- data.frame(x2=seq(min(x2), max(x2), length.out=100)) pred.2.resp <- predict.gam(gam2, val.for.pred ,type="response", se.fit=TRUE) lower.band <- pred.2.resp$fit - 2*pred.2.resp$se.fit upper.band <-...

...)). I've checked and it seems this is done through attr(,"specials"). However, the section of code below (as an example extracted from the gam package) gives me an error as shown at the end. Evidently I'm missing something here...any help is much appreciated. Thanks, Leo. "gam2.slist" <- c("s", "lo", "random") "gam2" <- function(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, control = gam.control(...), model = FALSE, method="glm.fit", x =...

...e to someone using R for Windows. (I myself use Linux.) The way the data is organised makes it convenient to use the boxplot function in a manner similar to this example given in the help. > mat <- cbind(Uni05 = (1:100)/21, Norm = rnorm(100), + T5 = rt(100, df = 5), Gam2 = rgamma(100, shape = 2)) > boxplot(data.frame(mat), main = "boxplot(data.frame(mat), main = ...)") If one of those columns had a numeric beginning to its name, such as: > colnames(mat)[3] <- "5T" and then using boxplot the same way, it will prepend an "X&q...

...> satisfaction','lamy1',1, + 'compcar <-> personcar','beta',NA, + 'compcar -> loyalty','gam1',NA, + 'personcar -> loyalty','gam2',NA), + ncol=3, byrow=TRUE) $ $ $ $ sem.h <- sem(h.semModel,h.sem, 2802,debug=T) observed variables: [1] "1:nemploy" "2:sales" "3:sex" "4:age" "5:loyalty" latent variables: [1] "6:...

...number of sites. I've created a GAM to do this: gam1=gam(trait~s(env1)+s(env2)+te(env1,env2)) and I know how to create a 3D plot using vis.gam. I want to be able to show points on the 3D plot indicating the sites that the data came from. I can do this on a 2D plot when there is one term, e.g. gam2=gam(trait~s(env1)) but cannot figure it out for the 3D plot. Any help would be much appreciated Monica -- View this message in context: http://r.789695.n4.nabble.com/plot-points-using-vis-gam-tp2314926p2314926.html Sent from the R help mailing list archive at Nabble.com.

...#39;m experiencing disk usage that is much higher on the destination PC. The origin samba server is Debian Lenny with samba 3.2.5. The destination samba server is Debian Wheezy with samba 3.6.9 The PC running robocopy is Windows Server 2003 with robocopy XP026. I'm copying from //gam/c$ to //gam2/c$. Either source and destination shares are mounted on /smbshare on the corresponding PCs, ext3 the former and ext4 the latter. From gam (the source): -------------------------- # du -s -h /smbshare/ 45G /smbshare/ # du -s -h --apparent-size /smbshare/ 45G /smbshare/ # df -h Filesystem...

Greetings, I have what I hope is a simple question. I would like to change my contour interval on the vis.gam( plot.type="contour") in the mgcv package. Is this a situation where I need to modify the function or is there a default value I can change? Thanks

...8.3915 0.0037697 s(PSD) 1 6.0234 0.014118 s(OPD) 2.289 12.745 0.0024349 s(GED) 3.791 152.68 < 2.22e-16 R-sq.(adj) = 0.885 Deviance explained = 91.1% GCV score = 2124.9 Scale est. = 1617.3 n = 60 ________________ >summary.gam(mod.gam2) Family: gaussian Link function: identity Formula: INT ~ s(IGS) + s(L2E) + s(TED) + s(PSD) + s(OPD) Parametric coefficients: Estimate std. err. t ratio Pr(>|t|) constant 302.32 4.736e-14 6.384e+15 < 2.22e-16 Approximate significance of smooth terms:...

...on argument, but that not the main point. I want to have a smooth term for time over a year, the same pattern repeating in succesive years. It would be natural then to impose the condition s(0)=s(12). Is this possible within mgcv? I tried to obtain this with trigonometric terms, aca: > Rot.gam2 <- gam(cbind(Rotavirus,Total)~ s( I(sin((MesN/12)*2*pi)), bs="cr" )+ + s( I(cos((MesN/12)*2*pi)), bs="cr" ), data=na.omit(Rot), + family=binomial) Warning messages: 1: Termwise estimate degrees of freedom are unreliable but this doe...

...(S_matrix) = colnames(S_matrix) = c("dec_mean_EVI", "density", "ALL_Jack1") I then construct a model using a symbolic ram specification as follows >tmodel <- specify.model() >dec_mean_EVI -> density, gam1, NA >density -> ALL_Jack1, gam2, NA >dec_mean_EVI -> ALL_Jack1, gam3, NA >dec_mean_EVI <-> dec_mean_EVI, ps1, NA >density <-> density, ps2, NA >ALL_Jack1 <-> ALL_Jack1, theta1, NA >dec_mean_EVI <-> density, theta2, NA >dec_mean_EVI <-> ALL_Jack1, theta2, NA >de...

...p-value s(Country) 36.127 58.551 0.644 0.982 Can I interpret this as there being no support for a random intercept for country? However, when I compare the simpler model to the model including the random intercept, the latter appears to be a significant improvement. > anova(gam1,gam2,test="F") Model 1: .... Model 2: .... + s(BirthNation, bs="re") Resid. Df Resid. Dev Df Deviance F Pr(>F) 1 789.44 416.54 2 753.15 373.54 36.292 43.003 2.3891 1.225e-05 *** I hope somebody could help me in how I should proceed in these situation...

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

...measure of reliability. What follows is the following path analysis model in theory (i.e., in practice the formulas are replaced with actual numbers): path.inf.final <- specifyModel() pRU -> sRU, test1 pRU -> rRU, test2 sRU -> rRU, test3 sRU -> power_alt, gam1 pRU -> power_alt, gam2 rRU -> power_alt, gam3 sRU -> ms_alt, gam7 pRU -> ms_alt, gam8 rRU -> ms_alt, gam9 sRU <-> sRU, NA, (1 - alpha(sRU))*(variance(sRU)) pRU <-> pRU, NA, (1 - alpha(pRU))*(variance(pRU)) rRU <-> rRU, NA, (1 - alpha(rRU))*(variance(rRU)) power_alt -> em, iota1 power_alt...

...confirmatory factor analysis models. For example, from ?sem, the Duncan, Haller, and Portes peer-influences model can now be specified as model.dhp.1 <- specifyEquations(covs="RGenAsp, FGenAsp") RGenAsp = gam11*RParAsp + gam12*RIQ + gam13*RSES + gam14*FSES + beta12*FGenAsp FGenAsp = gam23*RSES + gam24*FSES + gam25*FIQ + gam26*FParAsp + beta21*RGenAsp ROccAsp = 1*RGenAsp REdAsp = lam21(1)*RGenAsp # to illustrate setting start values, not necessary here FOccAsp = 1*FGenAsp FEdAsp = lam42(1)*FGenAsp and the Wheaton alientation model as model.wh <- specifyEquations() Anomia67 = 1...

...confirmatory factor analysis models. For example, from ?sem, the Duncan, Haller, and Portes peer-influences model can now be specified as model.dhp.1 <- specifyEquations(covs="RGenAsp, FGenAsp") RGenAsp = gam11*RParAsp + gam12*RIQ + gam13*RSES + gam14*FSES + beta12*FGenAsp FGenAsp = gam23*RSES + gam24*FSES + gam25*FIQ + gam26*FParAsp + beta21*RGenAsp ROccAsp = 1*RGenAsp REdAsp = lam21(1)*RGenAsp # to illustrate setting start values, not necessary here FOccAsp = 1*FGenAsp FEdAsp = lam42(1)*FGenAsp and the Wheaton alientation model as model.wh <- specifyEquations() Anomia67 = 1...

Dear R list members, Since today seems to be the day for optimization questions, I have one that has been puzzling me: I've been doing some work on sem, my structural-equation modelling package. The models that the sem function in this package fits are essentially parametrizations of the multinormal distribution. The function uses optim and nlm sequentially to maximize a multinormal

...inst x where \sum_i f(x_i)=0, and \alpha is an intercept parameter. So the zero line on the first plot, corresponds to the \exp(\alpha) line on the second plot (which is not the same as the mean of the response data). If you replace > abline(h=mean.y,lty=5,col=grey(0.35)) by > abline(h=coef(gam2)[1],lty=5,col=grey(0.35)) then everything should work. best, Simon On Sun, 5 Mar 2006, Denis Chabot wrote: > Hi, > > In fitting GAMs to assess environmental preferences, I use the part > of the fit where the lower confidence interval is above zero as my > criterion for positive as...