similar to: Missing interaction effect in binomial GLMM with lmer

Hello, everyone. I'm hoping to prevent myself from doing a lot of pointing and clicking in Excel. I have a dataframe of bird nest check observations, in which I know the date of the first check, the date of the second check (both currently in Julian date format), the status of the nest at the second check (alive or failed), and the date that the nest hatched (i.e. changed from Incubation

Dear R-experts, Quite new to R on this end, but learning fast (I hope). I am running version 2.7.1 on Windows Vista. I have small dataset which consists of: # NestID: nest indicator for each chicken. Siblings sharing the same nest have the same nest indicator. # Chick: chick indicator consisting of a unique ID for each single chick. # Year: 1, 2. # ClutchSize: 1-, 2- , 3-eggs. # HO:

Dear Listers, I am drawing a plot of two density curves, for male and female incomes. I would like to shade/hatch/color (whatever) the areas under the curves which are distinctive for each gender. This is the code I have tried so far: m <- density(topmal.d$y, bw = "sj") f <- density(topfem.d$y, bw = "sj") par(mfrow = c(1,1)) plot(x = c(0,400), y = c(0,0.02), type =

I have used polygon() to mark the confidence limits of a survival curve. In another project, I have used the col parameter to fill my boxplots. The poly() description refers to filling but actually produces hatching (i.e. lines ). boxplot() does truly fill the boxes with a colour or shades of grey (e.g. col="red"). My novices perception of R graphics is: If you can hatch it, you

Dear list, i'm a new R user, so I apologize if the topic is already being addressed by some other user. I'm trying to determine if the reproductive success of a species of bird is related to a list of covariates. These are the covariates: ? elev: elevation of nest (meters) ? seadist: distance from the sea (meters) ? meanterranova: records of temperature ? minpengS1: records

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

Hello, I have a bar plot where I am already using colour to distinguish one set of samples from another. I would also like to highlight a few of these bars as ones that should be looked at in detail. I was thinking of using hatching, but I can't work out how or if you can have a background colour and hatching which is different between bars. Any suggestions on how I should do this? Thanks

Hello, I would like to build rectangles in a plot and use color and different type of hatching for filling rectangles. I don't find the way to draw hatchings. I'm thinking to build segment by segment inside each rectangle but I'm sure that exists a better way to do that. I didn't find any documentation about that. > symbols(1,1,rectangles=cbind(1,1),bg="red", ...

Sorry that I was unclear. For an individual to qualify for my analysis I want both of the following two criteria to be fulfilled: First, I want to select measurement taken at a certain age: for the focal individual the year of measurement (year) should be the same as year.hatch Second, I want the focal individual to be born by a mother that reproduces for the first time. So the /parents /of

Hello, I am using the following model model1=lmer(PairFrequency~MatingPair+(1|DrugPair)+(1|DrugPair:MatingPair), data=MateChoice, REML=F) 1. After reading around through the R help, I have learned that the above code is the right way to analyze a mixed model with the MatingPair as the fixed effect, DrugPair as the random effect and the interaction between these two as the random effect as well.

Dear all, I have received some data on birds that looks sth like this: # a unique id for each individual id <- c(1,1,1,2,2,2,3,3,3,4,4,5,6) # the year the bird was measured year <- c(1995, 1996, 1997, 1995, 1996, 1997, 1996, 1997, 1998, 1996, 1997, 1997, 1998) # the year the bird was hatched year.hatch <- c(1995, 1995, 1995, 1995, 1995, 1995, 1996, 1996, 1996, 1996, 1996, 1997, 1998)

Hello R Help! I would like to make a legible boxplot of tree growth rates for each of seven tree species at each of seven different sites. It's a lot of data to put on one figure, I know. I made a beautiful, interpretable figure using color, but my target journal can't deal with color figures. I can use seven shades of grey to fill the boxes, but the figure then becomes uninterpretable -

If I have a model line = lm(y~x1) and I want to use a for loop to change the number of explanatory variables, how would I do this? So for example I want to store the model objects in a list. model1 = lm(y~x1) model2 = lm(y~x1+x2) model3 = lm(y~x1+x2+x3) model4 = lm(y~x1+x2+x3+x4) model5 = lm(y~x1+x2+x3+x4+x5)... model10. model_function = function(x){ for(i in 1:x) { } If x =1, then the list

Is there any way to put an argument into an object name. For example, say I have 5 objects, model1, model2, model3, model4 and model5. I would like to make a vector of the r.squares from each model by code such as this: rsq <- summary(model1)$r.squared for(i in 2:5){ rsq <- c(rsq, summary(model%i%)$r.squared) } So I assign the first value to rsq then cycle through models 2 through

M. Camanm posted in Jul 1999 the following message: " Is there any way to fill the bars in a barplot() with solid lines for postscript output, i.e. cross hatch or parallel lines, or a halftone gray rather than (semi) continuous-tone gray produced by gray()? S allows this, or at least used to, via the angle and density arguments to barplot(). The objective of course, is to produce camera

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

Dear R-Community, I am relatively new with R, so sorry for things which for you might be obvious... I am trying to automatically update lmer formulae. the variables of the model are: depM= my dependent measure Sb2= a random factor OS = a predictor VR= another predictor So, I am building the first model with random intercept only: model = lmer(depM ~ (1 |Sb2)) then I update the formula

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

I am running an lme model with the main effects of four fixed variables (3 continuous and one categorical – see below) and one random variable. The data describe the densities of a mite species – awsm – in relation to four variables: adh31 (temperature related), apsm (another plant feeding mite) awpm (a predatory mite), and orien (sampling location within plant – north or south). I have read

Hello, I'm having an error when trying to fit the next GLM: >>model<-glm(response ~ CLONE_M + CLONE_F + HATCHING +(CLONE_M*CLONE_F) + (CLONE_M*HATCHING) + (CLONE_F*HATCHING) + (CLONE_M*CLONE_F*HATCHING), family=quasipoisson) >> anova(model, test="Chi") >Error in if (dispersion == 1) Inf else object$df.residual : missing value where TRUE/FALSE needed If I fit