search for: purban

...OVA with nestedness. Specifically I'm not sure how to express chicks nested within boxes. I will be getting Pinheiro & Bates (Mixed Effects Models in S and S-Plus) but it will not arrive for another two weeks from our interlibrary loan. The goal is to determine if there are urbanization (purban) effects on chick health (rtot) and if there are differences between sexes (sex) and the effect of being in the same clutch (box). The model is rtot = sex + purban + (chick)box. I've loaded the package lme4. And the code I have so far is bb <- read.csv("C:\\eabl\\eabl_feather04.csv&...

...dient. I monitored these boxes and at some point I pulled a feather from a chick and a friend used spectral properties (rtot, a continuous var) to index chick health. There is an effect of sex that I would like to include but how would I set up a regression and look at the effect of urbanization (purban, a continuous var)) on feather properties of chicks within boxes. So the model should look something like rtot = sex + purban + (chick)clutch Also, when I plot purban against rtot using the plot function I get boxplots but I would like to ignore the clutch and just plot each point. I've tr...

...ain aspects of the color of those feathers. Since I often have more than one sample from a nest, I thought I should use a nested design. Here's the code I've been using and I'd appreciate if someone could look it over and see if it was correct. bb.glm1 <- glm(rtot ~ box/(julian +purbank), data=bbmale, family="gaussian", na.action=na.omit) where rtot = total reflectance, box = nest box (i.e., birdhouse), julian = day of the year and purbank = the proportion of urban cover in a 1 km buffer around the nest box. I'm not interested in the box effect and I've seper...

...Sc_ex_pri sc_ec_p1234 PD LPI ED LSI 1 3 25 1 1 3251 251 1 1 26 125 1125 1125 21.6565 62.6602 82.0769 15.8792 2 3 25 1 1 3251 251 1 1 26 125 1125 1125 19.3076 27.6264 111.2014 20.7889 PAFRAC PROX_MN ENN_MN CONTAG pfor purban 1 1.440 319.6529 114.8314 62.0965 69.4891 12.3124 2 1.467 396.1949 105.3712 52.9186 38.1179 15.1906 I tried using: all.lm <- (pfor~PD, data = all, subset=(ex_bin==250)) but this resulted in a bogus analysis filed with 'NAs'. I then tried to use getGroups. > all.group <- getGr...