similar to: how to compute the explained variance?

Dear all, I used metaMDS (vegan) to generate two NMDS axis and now I would like to know the amount of variance explained for each axis. Is there a way to do that? kind regards, miltinho [[alternative HTML version deleted]]

Picking up an ancient thread (from Oct 2007), I have a somewhat more complex problem than given in Simon Wood's example below. My full model has more than two smooths as well as factor variables as in this simplified example: b <- gam(y~fv1+s(x1)+s(x2)+s(x3)) Judging from Simon's example, my guess is to fit reduced models to get components of deviance as follows: b1 <-

Hello, I'm looking for a textbook that can explain some of the math behind the intro-to-intermediate stuff like ANOVA, multiple regression, non- parametric tests, etc. A little background: I took an intro stats course last year and would like to further my education. Being as that was the highest (and only) stats class the local community college offers, it looks like I'm on

I am learning R via a textbook that performs analysis with SPSS and SAS. In trying to reproduce the results for an ordinal logit model, I get very similar point estimates for my cut-off points, but the parameters for the covariate q60 do not match. The estimate for q51 also matches. Is this because I need to change a base case for the ordered covariate q60? Can this be done in or is it always the

Could somebody recommend some textbook how to compute contrast when there are interactions terms? "Applied Linear Regression Models" (book) mentioned contrast, but I cannot extend it to the case where there are interaction terms.

Hi dear R-users I try to reproduce the steps included in a LDA. Concerning the eigenvectors there is a difference to SPSS. In my textbook (Bortz) it says, that the matrix with the eigenvectors V usually are not normalized to the length of 1, but in the way that the following holds (SPSS does the same thing): t(Vstar)%*%Derror%*%Vstar = I where Vstar are the normalized eigenvectors. Derror

Hello! I've run a simple linear model: result<-lm(DV~A+B+C,data=Data) My Data$A,Data$B, and Data$C are factors. So, lm automatically recoded them into dummy variables. I have all the results I need but one. Question: Where could I see the variance explained by all A dummy variables together, then all B dummy variables together, and all C dummy variables together - when other predictors

I found this paper on ANOVA on unequal error variance. Has this be incorporated to any R package? Is there any textbook that discuss the problem of ANOVA on unequal error variance in general? http://www.jstor.org/stable/2532947?cookieSet=1

Hello, I have a question concerning fitting a cox model with a random intercept, also known as a frailty model. I am using both the coxme package, and the frailty statement in coxph. Often 'shared' frailty models are implemented in practice, to group people who are from a cluster to account for homogeneity in outcomes for people from the same cluster. I am more interested in the classic

Hello, As suggested in "De'ath, 2002. Multivariate regression trees: A new technique for modelling species-environment relationships. Ecology, 83 (4):1105-1117" (for those interested), I am trying to compare the performance of a multivariate regression tree to a cluster analysis. A simple partitioning with k clusters (as done by `pam`) seemed straightforward and appropriate

After doing a PCA using princomp, how do you view how much each component contributes to variance in the dataset. I'm still quite new to the theory of PCA - I have a little idea about eigenvectors and eigenvalues (these determine the variance explained?). Are the eigenvalues related to loadings in R? Thanks, Paul -- View this message in context:

Hi All How can I fit a repeated measures analysis using gls? I want to start with a unstructured correlation structure, as if the the measures at the occations are not longitudinal (no AR) but plainly multivariate (corSymm). My data (ignore the prox_pup and gender, occ means occasion): > head(dta,12) teacher occ prox_self prox_pup gender 1 1 0 0.76 0.41 1 2

My statistics teacher wrote a textbook and included .rdata files for the problem set data. I downloaded these and opened one of them up. Then I used the ls() command, and it gave me this: > ls() [1] "ex02-12" However, when i try to use the object, it interprets it as ex02 - 12, and I get the following error: > ex02-12 Error: object 'ex02' not found I don't know why

Hi All, Can somebody help me set this up. I'm trying to set up a public folder on my SAMBA server which will let my users create their own folders to store their files. And not let everyone delete them. Following are the problems I'm having: 1) If I set the directory rights to 755 on /exports and share the /exports/public directory with ~/public set to 755 Users can't create

Using the Cement hardening data in Anderson (2008) Model Based Inference in the Life Sciences. A Primer on Evidence, and working with the best model which is lm ( y ~ x1 + x2, data = cement ) the AIC value from R is model <- lm ( formula = y ~ x1 + x2 , data = cement ) AIC ( model ) 64.312 which can be converted to AICc by adding the bias

Hi all, I was trying to test the assumption of proportional hazards assumption, I used the cox.zph function >cox.zph(coxfit6) Results are: rho chisq p x1 -0.0396 1.397 2.37e-01 x2 0.1107 9.715 1.83e-03 x3 -0.0885 7.743 5.39e-03 x4 0.0366 1.092 2.96e-01 x5 0.0242 0.455 5.00e-01 GLOBAL

Hello everybody ! I want to evaluate the goodness of the fit of a trend surface. I use the Least square methods and the function surf.ls. The I write tma3.sur3 <- surf.ls(3, x, y, z) summary(tma3.sur3) Now the question: is the Pr(>F) the probability to obtain a value of F equal or greater than the obtained one ? Is the confidence level 0.05 ? Thanks a lot for any help. Paola.

Hola, Esta pregunta la hice en stackoverflow <http://stackoverflow.com/questions/31137940/randomly-assign-teachers-to-classrooms-imposing-restrictions/31143808#31143808>pero nadie pudo contestarla. 1. Quiero generar N escuelas, con G grados y C divisiones. 2. Quiero asignar cada uno de T maestros a 2 divisiones en un grado y escuela Si tengo C=4 divisiones, puedo lograr lo que quiero con

Hola, 0. La falta de 'elegancia' hace que sea mas dificil hacer cambios al codigo. Por ejemplo cambiar n.classrooms <- 4 a n.classrooms <- 20 1. Cuando tengo solo 4 puedo hacer esto: schoolGrade$A <- Teachers$Teacher.ID[1:cuttoff1] schoolGrade$B <- Teachers$Teacher.ID[1:cuttoff1] schoolGrade$C <- Teachers$Teacher.ID[(cuttoff1+1):n.teachers] schoolGrade$D <-

Gracias Carlos, Tu codigo es un gran paso en el sentido correcto pero no produce exactamente lo que estoy buscando. Mi "solucion" en stackoverflow <http://stackoverflow.com/questions/31137940/randomly-assign-teachers-to-classrooms-imposing-restrictions/31143808#31143808> produce un data frame `schoolGrade` con 240 observaciones y 7 variables. Mi objetivo es poder generar un data