similar to: Package or procedure recommendations for analysis of repeated cross-sections?

Consider this data structure (df1) ... Group Year PctProf FullYr 1 Never RF 2004 87 88 2 Cohort 1 2004 83 84 3 Cohort 2 2004 84 86 4 Cohort 3 2004 87 87 5 Cohort 4 2004 73 74 6 Never RF 2005 85 86 7 Cohort 1 2005 81 82 8 Cohort 2 2005 81 81 9 Cohort 3 2005 78 79 10 Cohort 4 2005 72 74 11

Dear R help gurus, I have the following problem and I would be delighted if you could help me. >From a large (1500) cohort of patients we have been taking some measurements (ECG measurements, but its not important). The measurements are ordinal in 4 grades (Grade I-IV, grade IV being the most severe form). Every patients has been measured several times (usually once per year). The

Hi Arthur: # Just move size outside 'aes' like this: p <- ggplot(df1, aes(Year, PctProf, group = Group)) p + geom_line(aes(color = Group),size=1) # to make the background white just use the black and white theme: p <- ggplot(df1, aes(Year, PctProf, group = Group)) p + geom_line(aes(color = Group),size=1) + theme_bw() Felipe D. Carrillo Supervisory Fishery Biologist Department

Consider an analysis of covariance involving age and cohort. The goal is to assess whether the influence of cohort depends upon the age. The simplest case involves data as follows value Age Cohort x1 ????? 1?????? 3 x2?????? 1?????? 4 x3?????? 1?????? 5 x4 ????? 2 ????? 3 x5 ????? 2 ????? 4 x6 ????? 2 ????? 5 etc. Age is a factor. The numeric response variable is value and Cohort is a

Hi all, I can't find the error in the binomial GLM I have done. I want to use that because there are more than one explanatory variables (all categorical) and a binary response variable. This is how my data set looks like: > str(data) 'data.frame': 1004 obs. of 5 variables: $ site : int 0 0 0 0 0 0 0 0 0 0 ... $ sex : Factor w/ 2 levels "0","1": NA NA NA

I have a question about stepAIC and extractAIC and why they can produce different answers. Here's a stepAIC result (slightly edited - I removed the warning about noninteger #successes): stepAIC(glm(formula = (Morbid_70_79/Present_70_79) ~ 1 + Cohort + Cohort2, family = binomial, data = ghs_70_79, subset = ghs_70_full),direction = c("backward")) Start: AIC=3151.41

Now that we have case cohort model , we have 1000 people and 50 cases Let the first 10 cases occur at the same time second 10 " third 10 " fourth 10 " fifth 10 " How easy is it to randomly sample 50 different cohort controls for each group? That is: randomly sample 50 cohort

Dear list, I'm currently trying to use the rms package to get predicted ordinal responses from a conditional ratio model. As you will see below, my model seems to fit well to the data, however, I'm having trouble getting predicted mean (or fitted) ordinal response values using the predict function. I have a feeling I'm missing something simple, however I haven't been able to

Hi all, I have done a backward stepwise selection on a full binomial GLM where the response variable is gender. At the end of the selection I have found one model with only one explanatory variable (cohort, factor variable with 10 levels). I want to test the significance of the variable "cohort" that, I believe, is the same as the significance of this selected model: >

Just trying to understand how geom_abline works with facets in ggplot. By way of example, I have a dataset of student test scores. These are in a data table dt with 4 columns: student: unique student ID cohort: grouping factor for students (A, B, . H) subject: subject of the test (English, Math, Science) score: the test score for that student in that subject The goal is to compare

I tried to compare if cch() and coxph() can generate same result for same case cohort data Use the standard data in cch(): nwtco Since in cch contains the cohort size=4028, while ccoh.data size =1154 after selection, but coxph does not contain info of cohort size=4028. The rough estimate between coxph() and cch() is same, but the lower and upper CI and P-value are a little different. Can we

Dear Folks-- I'm trying to make a function that takes the columns I select from a data frame and then uses a for loop to print some information about each one, starting with the column name. I succeed in returning the column name, but nothing else I have tried using the variable colName, containing the name of the column, to refer to the column itself has worked. Below I show my

--------- begin included message --------- I tried to compare if cch() and coxph() can generate same result for same case cohort data Use the standard data in cch(): nwtco Since in cch contains the cohort size=4028, while ccoh.data size =1154 after selection, but coxph does not contain info of cohort size=4028. The rough estimate between coxph() and cch() is same, but the lower and upper CI

> Now that we have case cohort model , we have 1000 people and 50 cases > Let the first 10 cases occur at the same time > second 10 " > third 10 " > fourth 10 " > fifth 10 " > How easy is it to randomly sample 50 different > cohort controls for each group? >That

Hello, I have written code to plot an xyplot as follows: library(lattice) xyplot(len~ageJan1|as.factor(cohort),groups=sex,as.table=T,strip=strip.c ustom(bg='white',fg='white'),data=dat, xlab="Age (January 1st)",ylab="Length (cm)",main="Linear models for male and female cod, by cohort",type='p',

Hi, I am working on a population of an invasive clam. The data are the size of each clam per station (2mm on average). Each station is found at a different distance from a power nuclear station, so at different water temperatures. The fist step I want to do is to identify cohort size at each station or (zone of water temperature). The second step will be to see whether the size or number of

Howdy, Last week I got some great help on why I was getting an error code when trying to run this model, thanks everyone!  I was able to get the code up and running beautifully for several data sets.  Now I am getting different errors with this new data set.  I can't figure out why, I have more data points with this species, and it is ordered exactly the same as the other species I have been

Hi, I ran the example on pp. 799-800 from Machael Crawley's "The R Book" using package survival v. 2.36-5, R 2.13.0 and RStudio 0.94.83. The model is a Cox's Proportional Hazards model. The result was quite different compared to the R Book. I have compared my code to the code in the book but can not find any differences in the function call. My results are attached as well as a

Hi, I have a data set with 999 observations, for each of them I have data on four variables: site, colony, gender (quite a few NA values), and cohort. This is how the data set looks like: > str(dispersal) 'data.frame': 999 obs. of 4 variables: $ site : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 2 2 ... $ gender: Factor w/ 2 levels "0","1":

I have fitted a proportional odds model, but would like to compare it to a continuation ratio model. However, I am unable to fit the CR model _including_ imputated data. I guess my troubles start with settuping the data for the CR model. Any hint is appreciated! Christian library(Hmisc) library(rms) library(mice) ## simulating data (taken from rms::residuals.lrm) set.seed(1) n <- 400 age