similar to: Count variables by ID

I can't seem to find just what I'm looking for in R help, Everitt and Hothorn HSAUR, Murrell's book, or the R graphics gallery at http://addictedtor.free.fr/graphiques/. Probably not looking efficiently, but anyway, If my data look like this: > head(data) cat startyear studentid 1 other 2001 12 2 UHS 2001 17 3 Lourdes 2001 10 4

Hey- I''m using ROR 1.0 and script.aculo.us 1.5.1. I''ve been trying unsuccessfully to render a partial in a DIV, but can''t seem to get the right combination of stuff to make it work. There seems to be a few different ways of going about it. Right now I can render a partial. What do I need to to render the partial in a div? Here is what it looks like so far: #

I''m trying to create a function that will take input in the form "a12345678" and output text in the form "a12-34-5678" if and only if the input text is * not* in the form "adam smith" The goal is to allow my users to enter either names separated by a space, or the student id number into a single search field and have the results be returned based on the

Hello, I have a dataset with a continuous independent variable (fish length, range: 30-150 mm) and a binary response (foraging success, 0 or 1). I want to discretize fish length into 5 mm bins and give the proportion of individuals who successfully foraged in each each size bin. I have used the cut function to discretize the length values into my desired bins, but I can't figure out how to

I currently have a data frame whose rows correspond to each student and whose columns are different variables for the student, as shown below: Lastname Firstname CATALOG_NBR Email StudentID EMPLID Start 1 alastname afirstname 1213 *@uark.edu 10295236 # 12/2/2008 2 anotherlastname anotherfirstname 1213 **@uark.edu ## 10295236 9/3/2008 Xattempts Q1

Hello there, I am using nlme() to fit a logistic mixed effect model on our data. The outcome variable is binary. I got the error when I wanted to add a group factor to my model. My initial model is as below: model.a <- nlme(response~ 1/(1 + exp( -intercept- u0 - slope*TIME - u1*TIME)), + fixed=intercept+slope~1, random= u0+u1~1 |studentID,

I am attempting to produce crosstabulations between two variables for subgroups defined by a third factor variable. I'm using by() and CrossTable() in package gmodels. I get the printing of the tables first and then a printing of each level of the INDICES. For example: library(gmodels) by(warpbreaks, warpbreaks$tension, function(x){CrossTable(x$wool, x$breaks > 30,

Hello all, Simple version of my problem: I've got a list of data frames, where each data frame has the same number of columns and the same column names. I'd like to flatten the list into one large data frame. Is there an easy way to do this? Quick example code: a <- data.frame(x=c(1,2,3),y=c(5,7,9) b <- data.frame(x=c(2,4,7,9),y=c(2,3,5,4)) z <- list(a,b) # Do

Hello lmer() - users, A call to the lmer() function causes my installation of R (2.11.1 on Mac OS X 10.5.8) to crash and I am trying to figure out the problem. I have a data set with longitudinal data of four subsequent performance measures of 1133 individuals nested in 88 groups. The data is in long format. I hypothesize a performance increase for each individual over time and intend to

-- apologies if this is a dup - i got a bounce saying the message was unprocessed. Is there a straightforward way to get a table with percents in the cells rather than counts? I've looked at table, ftable, xtabs, and ctab, which did the conversion but returned the results in a single row without labels. any suggestions are appreciated. thank you.

Hi, I am trying to find the min of day for each student in each year. Here is the dataset: date studentid year 1/1/05 6:07 AM 236 20082009 3/27/09 9:45 AM 236 20082009 4/29/09 8:44 AM 236 20082009 3/27/09 11:36 AM 310 20082009 4/1/09 10:43 AM 310 20082009 10/15/09 8:54 AM 310

Hi everyone. I''m new to this list and I have some questions regarding HTB class. I was trying to see how tc sets the parameters for HTB, but really couldnt understand them. So, I thought that I might ask the wizards. It''s mainly from the source code. 1. What is the cell_log ? (its everywhere in the code) 2. Why does htb calculate its burst(buffer) with tc_xmit_time() ? Is

Dear Sir, As per request below, I want to use your LLVM project in my thesis and I have no idea where to start and I couldn't find any information about how to build my test programs with your infrastructure and get my required information. Is your tool is a testing tool? Can it accept program and test case as input and output like the input test case is cover which branch and which

dear all, i'm just about to do some straightforward contingency tables using ftables (and ctab() for percents). the problem: factor "a" are regions, factor "b" are subregions. every region "a" consists of some subregions "b", but obviously not every subregion "b" is part of every region "a". if i use the ftable() function, the

Dear All, I want to create a table for several variables. As example. I have a dataframe with following data: Gender transport driving 1 0 1 0 1 0 1 0 1 Now I want to create a table in the following form: Gender

Hi All, The data set that I have is a cluster data, and I want to run a HLM mixed model with multi-level response. Here is my data set: response: - Level (num: 1, 2, 3, 4, 5 - 5 levels) Covariates: - Type (Factor: A, B, C - 3 levels) - yr (num: 2006, 2007, ...) - Male (num: 0=not Male, 1=Male - 2 levels) - Ethnicity (Factor: A, B, H, ..., - 7 levels) - ELL (num: 0, 1, - 2

Hi I am trying to convert SAS codes to R, but some of the result are quite different from SAS. When I ran proc mixed, I have an option ddfm=bw followed by the model. How can I show this method in R?(I am thinking that this maybe the reason that I can't get the similar results) below is my SAS codes: proc mixed data=test covtest empirical; class pair grade team school; model score = trt

has_many.count does not seem to support an options hash like ActiveRecord::Base.count does. Example: >> AcademicProgram.find(:first).course_requirements.count :conditions => "courses.name LIKE ''a%''", :include => :course => 40 >> ap.ap_requirements.count :conditions => "courses.name LIKE ''a%''", :include =>

When playing around fitting unconditional growth models using R and MlwiN today, I produced two different sets of estimates that I can't reconcile and wondered if anyone here has an idea: The data is two-level repeated measures data with measures nested within child. There are two measures per child. I've fit an unconditional growth model as in Singer and Willet (2003) that allows for

Hello. I am new to R and new to linear mixed effects modeling. I am trying to model some data which has two factors. Each factor has three levels rather than continuous data. Specifically, we measured speech at Test 1, Test 2 and Test 3. We also had three groups of subjects: RepTP, RepNTP and NoRepNTP. I am having a really hard time interpreting this data since all the examples I have seen