similar to: how to optionally include variables in a data.frame at assignment

Dear all, I have a quasipoisson glm for which I need confidence bands in a graphic: gm6 <- glm(num_leaves ~ b_dist_min_new, family = quasipoisson, data = beva) summary(gm6) library('VIM') b_dist_min_new <- as.numeric(prepare(beva$dist_min, scaling="classical", transformation="logarithm")). My first steps for the solution are following: range(b_dist_min_new)

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

Hi there, I'm sure there's an easy answer to this, and I can't wait to see it. The question: is there an easy way to sort a data frame by it's row names? My dilemma: I've had to pull apart a data frame, run it through a loop to do some calculations and generate new variables, and then re-construct the chunks back into a data frame at the end. Doing this preserves the row

Hello, I am getting an unexpected result from quantile(). Specifically, the return value falls outside the range of the data, which I wouldn't have thought possible for a weighted average of 2 order statistics. Is this an unintended accuracy issue or am I being too casual in my comparison (is there some analogue of 'all.equal' for "<=")? Small example: > foo <-

Hi, I am performing meta-regression using linear mixed-effect model with the lme() function that has two fixed effect variables;one as a log transformed variable (x) and one as factor (y) variable, and two nested random intercept terms. I want to save the predicted values from that model and show the log curve in a plot ; predicted~log(x) mod<-lme(B~log(x)+as.factor(y),

Hi, there I'm currently trying to figure out how to keep my "factor" levels for a variable when moving it from one data frame or matrix to another. Example below: vec1<-(rep("10",5)) vec2<-(rep("30",5)) vec3<-(rep("80",5)) vecs<-c(vec1, vec2, vec3) resp<-rnorm(2,15) dat<-as.data.frame(cbind(resp, vecs))

Hello, I am trying to run a logistic regression with random effects on proportional data in glmmBUGS. I am a newcomer to this package, and wondered if anyone could help me specify the model correctly. I am trying to specify the response variable, /yseed/, as # of successes out of total observations... but I suspect that given the error below, that is not correct. Also, Newsect should be a

Dear Alina If I understand you correctly you cannot just have a single predicted curve but one for each level of your factor. On 09/08/2017 16:24, Alina Vodonos Zilberg wrote: > Hi, > > I am performing meta-regression using linear mixed-effect model with the > lme() function that has two fixed effect variables;one as a log > transformed variable (x) and one as factor (y)

In Venables \& Ripley 3rd edition (p. 231) the proportional odds model is described as: logit(p<=k) = zeta_k + eta but polr apparently thinks there is a minus in front of eta, as is apprent below. Is this a bug og a feature I have overlooked? Here is the naked code for reproduction, below the results. ------------------------------------------------------------------------ --- version

Thank you Michael, Curves for each level of the factor sounds very interesting, Do you have a suggestion how to plot them? Thank you! Alina *Alina Vodonos Zilberg* On Thu, Aug 10, 2017 at 7:39 AM, Michael Dewey <lists at dewey.myzen.co.uk> wrote: > Dear Alina > > If I understand you correctly you cannot just have a single predicted > curve but one for each level of your

Dear useRs, I am using the gamm function in the mgcv package to model a smooth relationship between a covariate and my dependent variable, while allowing for quantification of the subjectwise variability in the smooths. What I would like to do is to make subjectwise predictions for plotting purposes which account for the random smooth components of the fit. An example. (sessionInfo() is at

Hi (R 2.13.1, OSX 10.6.8) I am trying to use predict.rma with continuous and categorical variables. The argument newmods in predict.rma seems to handle coviariates, but appears to falter on factors. While I realise that the coefficients for factors provide the answers, the goal is to eventually use predict.rma with ANCOVA type model with an interaction. Here is a self contained example

Hi, I am an R newbie and one thing I am having trouble with binding variables that I have created within one data frame into a new data frame when using na.omit(). To illustrate this problem I will give the example I am working on and the approah I have been using:- data.frame1<-filepath.... attach(data.frame1) #create a new variable using a function new.variable<-rep(1,length(weight3))

Hello, I'm analysing reaction time data from a linguistic experiment (a variant of a lexical decision task). To ascertain that the data was normally distributed, I used *shapiro.test *for each participant (see commands below), but only one out of 21 returns a p value above p.0 05. > f = function(dfr) return(shapiro.test(dfr$Target.RTinv)$p.value) > p = as.vector(by(newdat,

I am making a battery of levelplots and wireframes for several fitted models. I wrote a function that takes the fitted model object as the sole argument and produces these plots. Various strange behavior ensued, but I have identified one very concrete issue (illustrated below): when my figure-drawing function includes the addition of points/lines to trellis plots, some of the

Loris, thank you thank you so much!!! do you know which CRAN mirror i select? There are loads - all related to various countries.. so appreciate the help Jenny Jenny Coetzee (PhD Candidate) Head of Prevention in Key Populations & Head of the Soweto Sex Worker Project Perinatal HIV Research Unit (PHRU) New Nurses Home Chris Hani Baragwanath Academic Hospital Soweto B: +27 11 989 9821 M:

I am having great difficulty plotting what should be a simple graph. I have measured 1 'y' and 5 'x' variables in each of two groups. Linear regression shows significant differences in the slopes of the regression for each 'x' variable between the two groups. All that I want to do is to plot one graph that shows the scatterplot for the three groups (each group represented

I am trying to get a prediction interval from a glm regression. With newdat being my set of values to be fitted, and glmreg the name of my regression, I am using the following code. predict(glmreg, newdat, se.fit = TRUE, interval = "confidence", level = 0.90) The problem is that I am only getting the standard error and the fitted value, not a prediction interval. Any help would be

Hi Maria If you have problems just start with a small model with predictions and then plot with xyplot the same applies to xyplot Try library(gamm4) spring <- dget(file = "G:/1/example.txt") str(spring) 'data.frame': 11744 obs. of 11 variables: $ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2 3 3 3 4 4 ... $ SITE_ID

I am positive this problem has a very simple solution, but I have been unable to find it, so I am asking for your help. I need to know how to look something up in one data frame and add it as a column in another. If I have a data frame that looks like this: > frame1 ID score test age 1 Guy1 10 1 20 2 Guy1 13 2 20 3 Guy2 9 1 33 4 Guy2 11 2 33 and another frame that looks like this: