search for: mod0

...8) OK, I can do this using nls() - but "only just" as there are not as many observations as might be desired. Now the problem is that we have a factor "Site" and I want to include a corresponding error component. I tried something like (excuse the doctored R output): > mod0.nlme <- nlme(Y ~ Yhat(theta1,...,theta8) + site.err , + start = c(mod0.st,0), + groups = ~ Site, + fixed = theta1 + ... + theta8 ~ 1, + random = site.err ~ 1) Error in nlme.formula(Y ~ Yhat(theta1,...,theta8 : s...

...models <- list() mod <- glm(formula, family, data, ...) terms <- terms(formula) tl <- attr(terms, "term.labels") nt <- length(tl) models[[1]] <- mod for(i in 2:order) { models[[i]] <- update(mod, .~.^i) } # null model mod0 <- update(mod, .~1) models <- c(mod0, models) class(models) <- "glmlist" models } > mods <- Kway(Freq ~ A + B + C, data=df, family=poisson) Error in terms.formula(tmp, simplify = TRUE) : invalid power in formula I still don't understand how to manipula...

I am curious as to the "lm" calculation of R2 (multiple coefficient of determination, I assume) when intercept is zero. I have 18 data points, two independent variables: First, a model with an intercept: > mod0=lm(Div~Rain+Evap,data=test) > summary(mod0)$r.squared [1] 0.6257541 > cor(predict(mod0),test$Div)^2 [1] 0.6257541 The $r.squared and the result from "cor" are the same, as I would expect. Now we try a model with zero intercept: > mod1=lm(Div~0+Rain+Evap,data=test) > summary...

...avidsonB ates.pdf&ei=FhqTUoXuJKKV7Abds4GYBA&usg=AFQjCNFst7GT7mBX7w9lXItJTtELJSKWJg&si g2=KGA5MHxOvEGwDxf-Gcqi6g&bvm> R.H. et al 2008) Here, dT_purs is the response variable, T and Z are the fixed effects, and subject is the random effect. Random and fixed effects are crossed.: mod0 <- lmer(dT_purs ~ T + Z + (1|subject), data = x) mod1 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject), data = x) mod2 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject) + (1+ Z| subject), data = x) mod3 <- lmer(dT_purs ~ T * Z + (1 +tempo| subject) + (1+ Z| subject), data = x) mod4 <- lmer...

...pplicable method for 'lrtest' applied to an object of class "c('glm', 'lm')" Calls: assocTestRegression ... append -> RunRegression -> append -> unlist -> lrtest Relevant line of code in assocTestRegression is tmp <- append(tmp, unlist(lrtest(mod, mod0))[c(8,10)]) where mod and mod0 are both results of the glm() function. If I instead import the entire package in my NAMESPACE: import(lmtest) The example runs without error. Is there a way to import all methods for an S3 generic function without importing the entire package? thanks, Stephanie...

I'm trying to fit a function y=k*l^(m*x) to some data points, with reasonable starting value estimates (I think). I keep getting "singular matrix 'a' in solve". This is the code: ox <- c(-600,-300,-200,1,100,200) ir <- c(1,2.5,4,9,14,20) model <- nls(ir ~ k*l^(m*ox),start=list(k=10,l=3,m=0.004),algorithm="plinear") summary(model) plot(ox,ir) testox <-

...ata.frame( "y"=factor(sample(LETTERS[1:3], 143, repl=T, prob=c(4, 1, 10))), "a"=sample(1:5, 143, repl=T), "b"=sample(1:7, 143, repl=T), "c"=sample(1:2, 143, repl=T) ) library(nnet) mod1 <- multinom(y ~ ., data=df, trace=F) deviance(mod1) # 199.0659 mod0 <- update(mod1, . ~ 1, trace=FALSE) deviance(mod0) # 204.2904 Output data and syntax for SPSS: df2 <- df df2[, 1] <- as.numeric(df[, 1]) write.csv(df2, file="dfxy.csv", row.names=F, na="") syntaxfile <- "dfxy.sps" cat('GET DATA /TYPE=TXT /FILE=\...

...DavidsonB ates.pdf&ei=FhqTUoXuJKKV7Abds4GYBA&usg=AFQjCNFst7GT7mBX7w9lXItJTtELJSKWJg&si g2=KGA5MHxOvEGwDxf-Gcqi6g&bvm> R.H. et al 2008) Here, dT_purs is the response variable, T and Z are the fixed effects, and subject is the random effect. Random and fixed effects are crossed.: mod0 <- lmer(dT_purs ~ T + Z + (1|subject), data = x) mod1 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject), data = x) mod2 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject) + (1+ Z| subject), data = x) mod3 <- lmer(dT_purs ~ T * Z + (1 +tempo| subject) + (1+ Z| subject), data = x) mod4 <- lmer(...

Hi, which test do I have to use if I want to test if the following data follow a monotone trend; 0min 5min 10min 20min 30min 5 20 55 70 90 ... where the dependent variable contains frequencies. And how is that implemented in R? thanks for any help (on this more statistical-question ...).

...;Female" - attr(*, "class")= chr [1:2] "xtabs" "table" - attr(*, "call")= language xtabs(formula = as.formula(paste("freq ~", paste(tvars, collapse = "+"))), data = table) # fit baseline log-linear model for Status as response hoyt.mod0 <- loglm(~ Status + (Sex*Rank*Occupation), data=Hoyt1) > (hoyt.add1 <- add1(hoyt.mod0, ~.^2, test="Chisq")) Single term additions Model: ~Status + (Sex * Rank * Occupation) Df AIC LRT Pr(Chi) <none> 2166.36 Status:Sex 1 2129.54 38.82 4.658e-10 *** Status:Rank 2 1430.03 7...

...a simulated dataset (with anova tests at the end): library(lme4) Y1<-sample(c(rbinom(90,10,0.1),rbinom(90,10,0.7))) Y2<-10-Y1 Y<-cbind(Y1,Y2) Group<-c(rep("A",80),rep("B",50),rep("C",50)) Group<-as.factor(sample(Group)) X<-Y1*rnorm(180,mean=0,sd=10) mod0<-lmer(Y~X+(1|Group),family=binomial) #model using binomial family summary(mod0) Generalized linear mixed model fit using Laplace Formula: Y ~ X + (1 | Group) Family: binomial(logit link) AIC BIC logLik deviance 872.9 882.4 -433.4 866.9 Random effects: Groups Name Variance Std.Dev. Group (Interc...

Hi people, I want to obtain the adjusted r-square given a set of coefficients (without the intercept), and I don't know if there is a function that does it. Exist???????????????? I know that if you make a linear regression, you enter the dataset and have in "summary" the adjusted r-square. But this is calculated using the coefficients that R obtained,and I want other coefficients

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

...http://cran.r-project.org/web/checks/check_results_vcdExtra.html But, I can't see what to do to avoid this, nor understand what has changed in R devel. Sure enough, CRAN now reports errors in examples using MASS::loglm(), using R Under development (unstable) (2013-09-11 r63906) > Caesar.mod0 <- loglm(~Infection + (Risk*Antibiotics*Planned), data=Caesar) Error: could not find function "loglm" In DESCRIPTION I have Depends: R (>= 2.10), vcd, gnm (>= 1.0.3) Suggests: ca,gmodels,Fahrmeir,effects,VGAM,plyr,rgl,lmtest,MASS,nnet,ggplot2,Sleuth2,car and the vcd DESCRIPTI...

Hello, I have a question regarding estimation of the dispersion parameter (theta) for generalized linear models with the negative binomial error structure. As I understand, there are two main methods to fit glm's using the nb error structure in R: glm.nb() or glm() with the negative.binomial(theta) family. Both functions are implemented through the MASS library. Fitting the model using these

...r R list, > > is it possible to force the intercept to assume the value of 0 (that is no > intercept) in gam from gam package? Just like you would in lm or glm for example, by adding -1 to your formula. ?gam suggests you look at ?lm to see about the formulas for example. data(airquality) mod0 <- gam(Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp) - 1, data = airquality, na = na.gam.replace) mod1 <- gam(Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data = airquality, na = na.gam.replace) summary(mod0) summary(mod1) HTH G > > Regards -- %~%~%~%~%~%~%~%~%~%...