search for: cohorts

...74 19 Cohort 3 2007 79 80 20 Cohort 4 2007 75 77 21 Never RF 2008 83 84 22 Cohort 1 2008 81 81 23 Cohort 2 2008 81 81 24 Cohort 3 2008 76 77 25 Cohort 4 2008 62 63 ... which I subsetted to omit all cases for Cohort 4 and some cases for Cohorts 2 & 3 ... df2 <- subset(df1, ((Group == "Cohort 1" | Group == "Never RF") | (Group == "Cohort 2" & Year != 2004) | (Group == "Cohort 3" & Year > 2006))) > df2 Group Year PctProf FullYr 1 Never RF 2004 87 88 2...

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

...s not reflected in the results of GLM. Here there are the results of chisq.test and the GLM: 1) /> age_cohort<-as.table(rbind(c(142,95,46,33,14,59,18,12,7,1,0),c(258,144,54,70,20,11,6,8,2,3,1))) > dimnames(age_cohort)<-list(site=c("M","D"), + cohorts=c(2010,2009,2008,2007,2006,2004,2003,2002,2001,2000,1996)) > age_cohort cohorts site 2010 2009 2008 2007 2006 2004 2003 2002 2001 2000 1996 M 142 95 46 33 14 59 18 12 7 1 0 D 258 144 54 70 20 11 6 8 2 3 1 > (Xsqagec <- chisq.test(a...

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

...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 cohorts. ## Code to generate dt library(data.table) ## cohorts: list of cohorts with number of students in each cohorts <- data.table(name=toupper(letters[1:8]),size=as.numeric(c(8,25,16,30,10,27,13,32))) ## base: assign students to cohorts base <- data.table(student=c(1:sum(cohorts$size)...

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

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

...Model 2: site ~ 1 Resid. Df Resid. Dev Df Deviance P(>|Chi|) 1 993 1283.7 2 1002 1368.2 -9 -84.554 2.002e-14 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 My question is: When I report this result, I would say /"cohorts were unevenly distributed between sites ( Chi2=84.5, df=9, p < 0.001)"/, is that correct? is the Chi2 value the difference of deviance between model with cohort effect and null model? -- View this message in context: http://r.789695.n4.nabble.com/Chi-square-value-of-anova-binomialglmnull-b...

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 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

--------- 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

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

----- begin included message In case cohort study, we can fit proportional hazard regression model to case-cohort data. In R, the function is cch() in Survival package Now I am working on case cohort analysis with time dependent covariates using cch() of "Survival" R package. I wonder if cch() provide this utility or not? The cch() manual does not say if time dependent covariate is

Hi All, I am in the middle of doing an analysis of a Case-Cohort design. I had three questions about the analysis: a) Does any one know of some public code for developing the patient risk sets (indexed by failure time) or is there a better way to organize the data? b) I was planning to use the Barlow weighting method. Has this or any other weighting method (Prentice, Self-Prentice) been

Dear R-users: How does one calculate in R the odds ratios for a CATEGORICAL predictor variable that has 4 levels. I see r-help inquiries regarding odds ratios for what looked like a continuous predictor variable. I was wondering how to get the pairwise odds ratios for comparisons of levels of a categorical predictor variable. I can't seem to get the correct output using: >

Buenos días, Necesito calcular la vida media de cada uno de nuestros modelos de producto pero tengo un histórico incompleto (para los productos más nuevos tengo la vida completa de las cohortes, pero para los más antiguos tengo sólo la parte final de la vida de las cohortes). He decidido calcular la esperanza de vida dinámica con un modelo actuarial, concretamente la librería 'demography'

Hi. I've a dataframe with about 46000 women with about 500 cases (cancers). I want to define a case-cohort sampling scheme, matching by age and hospital centre. Is there anyone who has already written a code for that? It should be something similar to the stcacoh macro in Stata. TIA, Stefano -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list