similar to: acts_as_list sort by multiple items

I'm not sure how to best explain what I am after but here goes. I have a data frame with 2 geographical factors. One is the major region the other is the component regions. I am trying to process all the regions at the same time without using "for". So I need (think, I do) a list of matrices each structured according to the number of subregions within each region. So is there a

Hello, I am attempting to fit a non-linear model (Von Bertalanffy growth model) to fish length-at-age data with the purpose of determining if any of the three parameters differ between male and female fish. I believe that I can successfully accomplish this goal assuming an additive error structure (illustrated in section 1 below). My trouble begins when I attempt this analysis using a model

Hello All, I would like to report the following bug or maybe you can explain if I am wrong. I am sampling from two different populations with weights. The two populations have the same age groups and I want to distinguish where I am sampling from. That is why I am using a matrix such as: matrix age.group Male Females Weight.Males Weight.Females 1 1.1

Am I correct in assuming that the output below essentially translates to "Males have a mean time that is significantly lower than Females"? Is this the correct way to interpret the fact that the coefficient is negative? Assume the variale sex is treated as a factor with Female =0 and Male=1. survmodel<-survreg(survobj~sex,data=data1, dist="weibull")

Hi I'd really like to get a bar plot showing the means of my anova data. I have looked everywhere and can only seem to find instructions for 2 way anova's. I basically want to look at the mean condition of my subjects spilt by age, sex and year (as a factor rather than a continuous variable, hence Anova and not Ancova). and want to show it firstly as a bar graph with standard error. I

Hi, I have recently been attempting to find the LD50 from two predicted fits (For male and females) in a Generalised linear model which models the effect of both sex + logdose (and sex*logdose interaction) on proportion survival (formula = y ~ ldose * sex, family = "binomial", data = dat (y is the survival data)). I can obtain the LD50 for females using the dose.p() command in the MASS

Hello, I am trying to make a barplot with nested count data which is build like this: first there are several birds (n)laying 3 clutches composed of 2 eggs half of the second and third clutch received treatment and this treatment was tested to influence sex of offspring. I want a barplot showing counts for male and female for every egg of every clutch. can someone tell me what to do? I drew a

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =

Hola Alexander. Para filtrar y obtener el subconjunto que necesitas, puedes hacerlo de la siguiente (una de ellas) manera: > Female <- subset(MathAchieve, subset=Sex=="Female") Crear una nueva variable recodificando "Sex" puedes hacerlo así: > Sex_recode <- Recode(MathAchieve$Sex, '"Female"=1; "Male"=0; ;', as.factor.result=TRUE) >

I have run a glm with a final formula of : (dependent variable = parasite load, main effects are sex, month, length and weight, with sex:month and length:weight first order interactions). I am using the summary(mod) command to give me the contrasts, which I believe use the contr.treatment command. I do not have a treatment group as such as I am comparing data from a wild system so I use the

Hi im trying to add error bars to my barplots, there very basic, i have a few grapghs where the y variable is different but on all the X variable is Age (Adult and Juvenile) however this is split into two levels so i have males and females, so my graph basically has four bars on it. I know how to add eror bars for instance when there is only one level eg lookng at the diffrence between male and

Hi, I'm trying to work through a Nested ANOVA for the following scenario: 20 males were used to fertilize eggs of 4 females per male, so that female is nested within male (80 females used total). Spine length was measured on 11 offspring per family, resulting in 880 measurements on 80 families. I used the following two commands: summary(aov(Spinelength ~ Male*Female)) and

Suppose I have a binary outcome (disease/no disease and all subjects had the same period of exposure) and 2 or 3 (categorical) predictors. I can obviously build a logistic regression model which describes the data, possibly including interaction terms, on a relative scale: model<-glm(disease~sex*race*prematurity,family=binomial) 1) Is there any way to extract information on the absolute

Hello all I'm trying to run some path analysis in either sem or lavaan (preferably lavaan because I find its interface easier to use). Most of my variables are continuously distributed and fairly well-behaved but I have a single exogenous variable (sex) which is not continuously distributed. Preliminary model fitting suggests that there aren't any sex by (anything else) interactions. The

Hello everybody, I need an help because I don´t know if the command for the ANOVA analysis I am performing in R is correct. Indeed using the function aov I get the following error:"In aov (......) Error() model is singular" The structure of my table is the following: subject, stimulus, condition, sex, response Example: subject stimulus condition sex response

Dear R-community, I am using R (V 2.14.1) on Windows 7. I have a dataset which consists of 19 variables for 91 individuals or rows. Two of my variables are Age (adult/chick, with no NA values) and Sex (0 for females/1 for females, with quite a few NA values). The sex of many adult birds is unknown (entered as NA in dataframe). At some point of my analyses, I happen to need to need to work with

Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear. My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~WSf*WSM,

Hello R-users/experts, I am new to R- I have a simple question: Let say I have a data set as follows temp:[file attached] the data structure is a follows: sex age female 28 female 53 female 53 female 36 male 42 male 29 male 43 male 36 male 41 Here we are grouping all male value into male and all female value in to female

Dear all, Apologies if this question is bit theoretical and for the longish email. I am meta-analyzing the coefficients and standard errors from multiple studies where the raw data is not available. Each study analyst runs a model that includes an interaction term for, say, between sex and smoking and age. Here is an illustrative example example for one study: set.seed(1066) status

Dear R Users, I am having trouble with lmer. I am looking at recombinant versus non recombinant individuals. In the response variable recombinant individuals are coded as 1's and non-recombinant as 0's. I built a model with 2 fixed factors and 1 random effect. Sex (males/females) is the first fixed effect and sexual genotype (XY, YY, WX and WY) the second one. Sexual Genotype is