search for: chicks

Dear R-users, I did some more research and I'm still not sure how to set up an ANCOVA 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 betwe...

Hi, In the examples from the ReShape package there is a simple example of using melt followed by cast that produces a smallish amount of output about the chicks database. Here's the code: library(reshape) names(ChickWeight) <- tolower(names(ChickWeight)) chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE) DietResults <- cast(chick_m, diet + chick ~ time) DietResults My challenge is to extract an plot only a portion of this data. I would...

Hi R experts, I am interested on the effects of two dietry compunds on the growth of chicks. Rather than extracting linear growth functions for each chick and using these in an analysis I thought using ReML might provide a neater and better way of doing this. (I have read the pdf vignette("MlmSoftRev") and "Fitting linear mixed models in R" by Douglas Bates but I am no...

according to the documentation of the cast function in the reshape function, I would expect this bit of code from the examples to calculate marginal means over only the 'diet' variable. #Chick weight example names(ChickWeight) <- tolower(names(ChickWeight)) chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE) cast(chick_m, diet + chick ~ time, mean, margins="diet") But,

...0 0 7 11 2 3 1 0 0 0 1 7 11 2 3 2 0 0 0 0 7 11 2 3 3 1 1 1 1 ............ In order to account for lack of independence at the nest level (many chicks are siblings), I'd like to run a GLMM with random slopes and intercepts for nests. Using lmer, my model for survival at 10 days, for example, would read as follows (or not!): > model <- lmer(Death10 ~ HO + ClutchSize + SibComp + Year + (1|NestID), family=binomial, 1) > summary(mode...

I've noticed that R has a number of very useful functions for obtaining descriptive statistics on groups of variables, including summary {stats}, describe {Hmisc}, and describe {psych}, but none that I have found is able to provided weighted descriptives of subsets of a data set (ex. descriptives for both males and females for age, where accurate results require use of sampling

...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 tried type = "p" but this has no effect....

...y="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 seperated males and female chicks. I've asked about nestedness before and I was given code that included "|" to indicate nestedness but this indicates a grouping does it not? I suspect that there is something wrong. In the summary I get Coefficients: Estimate Std. Error t value Pr(>|t|) (...

...rd 0 9 1 First 0 10 1 First 1 11 1 Third 0 12 1 First 0 13 1 First 0 14 1 First 0 15 1 First 0 17 1 First 1 19 1 First 1 20 1 First ............ n ni nj nk I need to run a GLMM using Year (Yr) and chick ID (ID) as random effects in order to account for lack of independence at the nest level (many chicks are siblings) and eliminate ?year effect? incase a significant difference exists between years. Using lmer, I specified my model as follows: model1 <- lmer(y~HO+(Yr|ID),family=binomial,1) summary(model1) Output: Error en model.frame.default(data = 1, formula = y ~ HO + (Yr + ID), drop.unu...

Estimado Oliver Nuñez Envío un ejemplo reproducible. Javier Marcuzzi # de donde tomo datos, y tiene el modelo (en el pdf) library(MCMCglmm) # librería con las funciónes que voy a usar library(nlme) datos0<-ChickWeight # creo algunos datos que agrego a los origonales Factor<-as.numeric(datos0$Chick) Factor[Factor > 0 & Factor <= 10] <- 'A' Factor[Factor > 10

HI Folks, I'm trying to create a list with named elements. Only, I don't know the names of the elements a priori (they come from the data being calculated). Currently, my approach is to create an environment, then assign things to the environement, then as.list the environment to get a list. Running the code gives, for example: > e2 <- new.env(FALSE, NULL) >

i want to tag categories to its menuname. i have a csv containing menu item name and in other csv i have a column containing some strings, i want to pick that strings from categories and look into menu items if any menu item containing that string i want to create a new column next to menu item name flagged as 1 otherwise 0 and the only condition is once a menu item flagged as 1 i don't need

This patch implements persistent grants for the xen-blk{front,back} mechanism. The effect of this change is to reduce the number of unmap operations performed, since they cause a (costly) TLB shootdown. This allows the I/O performance to scale better when a large number of VMs are performing I/O. Previously, the blkfront driver was supplied a bvec[] from the request queue. This was granted to

Dear R crew: I think I am in the right mailing list. I have a very simple dataset consisting of two variables: cestode intensity and chick size (defined as CAPI). Intensity is clearly overdispersed, with way too many zeroes. I'm interested in looking at the association between these two variables, i.e. how well does chick size predict tape intensity? I fit a zero inflated negat. binomial

...seen below in the code. > I have also tried to use predict, with exactly the same result: the > offset is ignored. This applies to both lms and glms. > > Am I missing something here? > Thank you > Samuel Riou > > CODE > #no offset lmA<-lm(MassChange24h~DATEN1, subset(Chicks1, Year==2007? & > AGE>10), na.action=na.exclude) > summary(lmA) > > #linear offset > lmAO<-lm(MassChange24h~DATEN1+offset(-0.37356*AGE), subset(Chicks1, > Year==2007? & AGE>10), na.action=na.exclude) > summary(lmAO) > > > print(Chicks$DATEN1[Year==20...

An excerpt from dataset ChickWeight: ??? weight Time Chick Diet 1?????? 42??? 0???? 1??? 1 2?????? 51??? 2???? 1??? 1 3?????? 59??? 4???? 1??? 1 I am interested in the residuals of the dataset.? Specifically in saving them to another format. I have been creating text files with sink. CW.lm <- lm(weight ~ Diet, ChickWeight) resid.CW.lm <- resid(CW.lm) But when I call: resid.CW.lm The data

Dear R crew: I am sorry this question has been posted before, but I can't seem to solve this problem yet. I have a simple dataset consisting of two variables: cestode intensity and chick size (defined as CAPI). Intensity is a count and clearly overdispersed, with way too many zeroes. I'm interested in looking at the association between these two variables, i.e. how well does chick

...SMTP; 16 Jan 1997 19:48:39 -0000 Received: from dhp.com (dhp.com [199.245.105.1]) by dhp.com (8.8.4/8.6.12) with SMTP id OAA18650; Thu, 16 Jan 1997 14:48:14 -0500 Date: Thu, 16 Jan 1997 14:48:13 -0500 (EST) From: Matt <panzer@dhp.com> Approved: R.E.Wolff@BitWizard.nl To: Christopher Hicks <chicks@chicks.net> cc: linux-security@redhat.com Subject: Re: [linux-security] Re: logwatching In-Reply-To: <Pine.LNX.3.94.970116124053.13190B-100000@yakko.chicks.net> Message-ID: <Pine.LNX.3.95.970116144453.18239A-100000@dhp.com> MIME-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII...

Dear R-help subscribers Would you tell me how to apply SSfpl with binary data as below? Unfortunately, there is not the EXAMPLE in help(SSfpl) for binary data but for quantitative data(Chick). V1: dose V2: log-transformed dose V3: response (rate) V1 V2 V3 1 0.775 -0.2548922 0.1666667 2 5.000 1.6094379 0.8148148 3 10.000 2.3025851 0.5000000 4 20.000 2.9957323

Estimados Tengo un error que me desconcierta, es un código que simplifiqué de otro trabajo donde no hay problemas, sin embargo me da un error. Una diferencia es que en mi otro trabajo uso spline y ahora polinomio, este es de segundo grado y se encuentra tanto en efectos fijos como aleatorios, el modelo es correcto, corre con MCMCglmm pero no con nlme. grid <- expand.grid(Tiempo=4:6,