similar to: How to work on Timing and Scheduling for a game?

Hi, I have a form that asks users to put in their :first_name and :last_name. How do I validate that their full_name (first_name + last_name) is unique? Thanks! Nguyen Anh Hung Assistant Project Manager/ Consultant/ Business Analyst Tech Propulsion Labs Mobile: +84987 005 477 Twitter: http://www.twitter.com/anhhung Blog: http://www.fresco20.com Skype: anhhung.n | Yahoo: hero.tuxer | Gtalk:

Hi, I was hoping to get some advice regarding the testing of interactions, when one factor is modelled as a random effect... I have a model with binomial error structure where the response variable is the proportion of time spent at the main sett (animals were tracked for 28 consecutive days in each season, and were recorded either at the main sett or an outlier sett, so the response variable is

Hi, I was wondering if someone could please help me. I am doing a logistic regression to compare size at maturity between 3 seasons. My model is: fit <- glm(Mature ~ Season * Size - 1, family = binomial, data=dat) where Mature is a binary response, 0 for immature, 1 for mature. There are 3 Seasons. The Season * Size interaction is significant. I would like to compare the size at 50%

Hi Irucka, No problem. I guess this could be done using: Sample data: Dailydo<- structure(list(Date = structure(c(11231, 11232, 11233, 11234, 11235, 11236, 11207, 11208, 11209, 11179, 11180, 11181, 11151, 11152, 11123, 11093, 11064, 11065, 11035, 11036, 11008, 11009, 10979, 10980, 11347, 11348, 11380, 11381, 11410, 11406, 11438, 11439, 11470, 11502, 11528, 11560, 11561, 11593, 11594,

Many thanks for taking the time to read this! I am looking at the repeatability of behaviour between re-sighted individuals across discrete time periods (annual breeding seasons). My approach was to run a GLM (with a logit link - the data are proportional, presence v. absence of behaviour) for each breeding season. I included the re-sighted individuals as a factor (categorical variable) (i.e.

Dear R list, I have a sequence of weekly observations of number of adults and larvae in various size classes from a butterfly population living in a subtropical area with pronounced wet and dry seasons. Wet and dry seasons are each defined 26 weeks long with fixed start and end dates. The data span 103 weeks (two seasons each of wet and dry) with some missing weeks. What I would like to do is

Hello R-Users I am struggling with this line plot, it might be simple but I am missing something here. First of all I want to make multiple line plots across seasons (DJF,MAM,JJA,SON) for 12 variables (here, called nodes) and fill them with the node. So that season=x-axis, node=line col and freq=y-axis. My plot currently links all DJFs, MAMs, JJAs and SONs, however I will like them to be

I am an R newbie, and I am facing what is for me a challenging problem. I am trying to write functions to solve the following problems. It seems as though R offers so many options I am not sure how to approach the problem. I have a data frame with dates in the first column and returns in the remaining columns. I would like to give a user a variety of options. I designate Series1 as the

Dear All, I am trying to fit a structural time series model using the StructTS function (package stats) with only 2 seasons (summer and winter). More than 2 seasons work fine but with 2 seasons I get this error: > fit <- StructTS(y.ts, type="BSM") Error in T[cbind(ind + 1L, ind)] <- 1 : subscript out of bounds I have looked at Prof. Ripley's 2002 RNews article but cannot

Dear R-help, My dataset (which is a data frame, called 'Calender' here) includes 365 rows representing 365 days for a year. One column ('Season')contains factor data representing seasons, e.g. spring, summer, autumn and winter. Another column (called 'Day') contains data representing wether the day is a working day (I use 'Wd' for short here)or weekend (I use

Hi, I'm from Italy (sorry for my english...). I've two questions about the plot function. I've to create a simple graph for the data set "n_species": species=sqlQuery(dati, "select count(distinct species), season from captures_complete_r group by species, season") n_species=tapply(species$count, species$season, sum) n_species=data.frame(n_species) n_species

Hi all, I have a couple of questions. 1) Is there a more effective way to remove the following pattern (patternrm) from the station_id_Timeseries (see below) patternrm <- c(_2000_4_OND, _2001_1_JFM, _2001_2_AMJ, _2001_3_JAS, _2001_4_OND, _2002_1_JFM, _2002_2_AMJ, _2002_3_JAS, _2002_4_OND, _2003_1_JFM, _2003_2_AMJ, _2003_3_JAS, _2003_4_OND, _2004_1_JFM, _2004_2_AMJ, _2004_3_JAS, _2000_4_OND) #

I have a zooreg object and I want to be able to generate a value for seasons and 11-day composites paste it onto my zoo data frame, along with year, month and days. Right now I have the following to work from: eg. dat.zoo.mdy <- with(month.day.year(time(dat.zoo)), cbind(dat.zoo, year, month, day, quarter = (month - 1) %/% 3 + 1, dow = as.numeric(format(time(dat.zoo), "%w")))) For

Dear mixed-modelers, I have built a mixed model and I'm having serious trouble with interpreting the output. I want to test differences in the coefficient of variation (CV) of light across 3 tree crown exposures (Depth). I have measured direct and diffuse radiation (RF) and I want to test for differences for each kind of radiation between crown exposures, as well as differences between

Hi All, I have data about seed predation (SP) in fruits of three differents colors (yellow, motted, dark) and in two fruiting seasons (2007, 2008). I performed a GLMM (lmer function, lme4 package) and the outcome showed that the interaction term (color:season) was significant, and some combinations of this interaction have significant Pr(>|z|), but I don't think they are the right

Hello all, I cannot seem to figure out this seemingly simple procedure. I want to sort a data frame by a specified character vector. So for : df.. <- data.frame(Season=rep(c("Summer","Fall","Winter","Spring"),4),Obs= runif(length(rep(c("Summer","Fall","Winter","Spring"),4)))) I want to sort the data frame

Dear useRs, I am fitting a mixed model using the function lmer from the package lme4, but I have some problems when I try to test the effect of my factors of interest. First let me explain the structure of the model: I'm measuring animal movements. Explicitly, I am interested in displacement (straight-line distance from an initial point). Displacements are measured longitudinally, with one

Dear R users, I am currently working in subsetting a zooreg() object using either window or subset. I have a solution but it may be a bit cumbersome when I start working with actual data. Your inputs would be greatly appreciated. Example: I have a zooreg() object that starts in 1997 and ends in 2001. This object contains daily data for the 4 years

Dear all, I am trying to fit a generalized linear mixed model to deal with both temporal and spatial pseudorreplication. I have repeated seasonal measurements (3 seasons during 3 years and 2 season during the last year, factor named percod, with 11 levels) of a bird presence/absence (pres.f) on 14 artificial ponds (charca.f). The ponds are integrated in 4 greater spatial units (zepa). I have

Hi, I am trying to fit a non-linear model for a parasite dataset. Initially, I tried log-transforming the data and conducting a 2-way ANCOVA, and found that the equal variance of populations and normality assumptions were violated. Gaba et al. (2005) suggests that the Weibull Distribution is best for highly aggregated parasite distributions, and performs better (lower type 1 and 2 error rates)