search for: mortales

i am trying to replicate the following graph using xyplot : attach(x) plot ( jitter(type), mortality, pch=16, xlim = c(0.25, 3.75)) lines ( c(1-0.375,1.375) , c ( median(mortality[type==1]), median(mortality[type==1])), lwd=5,col=2) lines ( c(2-0.375,2.375) , c ( median(mortality[type==2]), median(mortality[type==2])), lwd=5,col=2) lines ( c(3-0.375,3.375) , c ( median(mortality[type==3]),

Hello R Community, I have the following design question. I have a data set that looks like this (shortened for the sake of example). Gender Age M 70 F 65 M 70 Each row represents a person with an age/gender combination. We could put this data into a data frame. Now, I would like to do some actuarial analysis on this data set. To do so, I need to create and store

Estimados Queremos con el paquete FactoMineR hacer este tipo de tabla de mortalidad que lea los datos desde de una tabla csv Realizamos lo que viene en la ayuda y es muy interesante, sin embargo cuando mandamos a leer desde la tabla csv original de los autores no hace el análisis porque algo falta y no nos percatamos de que es. Adjunto tabla original Saludos cordiales #ESTO ES LO QUE

Hola. Como te dijo Carlos, el problema está en los nombres de las columnas y en los nombres de las filas. Cuando hice la importación (con dd<-read.csv('mortality.csv'), tuve problemas con las filas de nombre: - Malignant tumour of the larynx trachea bronchus and lungs - Malignant tumour of the lip pharynx and mouth - Other endocrinological metabolic and nutritional conditions

Dear all, I'm trying reproduce an example of bioassays Yielding Concentration-Mortality Data particularly control - adjustment model from book Bioassay of Entomopathogenic Microbes and Nematodes chapter 7 with R. I used glm with family=binomial and link=probit, but I do not know how to implement parameter gamma (control mortality - mortality of the untreated control insect in this exaple)

Dear R Users: I have the individual mortality rate and 95% CI of 100 hospitals, how to do the plot with the individual hospital in the Yaxis, and the mortality rate and 95% CI in the Xais and a overall mean as a reference line? Thanks and regards, Xin [[alternative HTML version deleted]]

Hola, El problema está con el nombre de las columnas y las filas del fichero que importas. Mira este detalle tras hacer la importación: > names(mort) [1] "X" "X15.24..79." "X25.34..79." [4] "X35.44..79." "X45.54..79." "X55.64..79." [7] "X65.74..79." "X75.84..79."

Hi all, I am not sure how to handle interactions with categorical predictors in the GAM models. For example what is the different between these bellow two models. Tests are indicating that they are different but their predictions are essentially the same. Thanks a bunch, > gam.1 <- gam(mortality.under.2~ maternal_age_c+ I(maternal_age_c^2)+ + s(birth_year,by=wealth) + +

Hello, all. I have co-opted a number of functions that can be used to plot the hazard/survival functions and associated density distribution for a Gompertz mortality model, given known parameters. The Gompertz hazard model has been shown to fit relatively well to the human adult lifespan. For example, if I wanted to plot the hazard (i.e., mortality) functions: pop1 <- function (t) {

Hi i am doing an environmental research The equation is as follow: gam(y1 ~ x1 + s(x2) + s(x3) + s(x4), family = gaussian, fit = true) I would like to obtain the beta coefficient and 95CI of x4 (or s(x4)), what should I do? Thanks, Lung -- View this message in context: http://r.789695.n4.nabble.com/mgcv-beta-coefficient-and-95-CI-tp3320491p3320491.html Sent from the R help mailing list

Dear R-users, I computed a simple coxph model and plotted survival over time with associated confidence intervals for 2 covariate levels (males and females). M1 <- coxph(survobject~sex, data=surv) M1 survsex <- survfit(survobject~sex,data=surv) summary(survsex) plot(survsex, conf.int=T, col=c("black","red"), lty = c(1,2), lwd=c(1,2), xlab="Time",

Hi All, I am relatively new to R (took a class 2 yrs ago ...), and am hoping someone can point me in the right direction for a problem I'd like to solve in R : I would like to calculate the relative mortality of a particular impairment, relative to the standard population. I.e. I'm trying to find S_relative(t) in the eqn below : S_impar(t) = S_standard(t) * S_relative(t) where

Hallo Everybody How do you specify arguments for a function used within another function? Here is my problem: I am reconstructing a calculator for the burden of disease due to air pollution from publications and tools published by the WHO. The calculations make use of published dose-response relationships for particular health end-points. This is then applied to populations with known or

Hi I am trying to carry out some maximum likelihood estimation and I'm not making much headway, and I'm hoping that someone will be able to point me in the right direction. I am modelling mortality statistics. One way to do this is to model the mortality rate (or, more accurately, log of the mortality rate, log_m) as (say) a constant plus a proportion of age, plus time, so: r_1 <-

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

Hello, I have placed a package on CRAN about two surveys of mortality in Iraq that were published in the Lancet. http://cran.at.r-project.org/src/contrib/Descriptions/lancet.iraqmortality.html > install.packages("lancet.iraqmortality") ... > library(lancet.iraqmortality) Loading required package: foreign > ?lancet.iraqmortality > vignette("mortality") This is

Hello, I have placed a package on CRAN about two surveys of mortality in Iraq that were published in the Lancet. http://cran.at.r-project.org/src/contrib/Descriptions/lancet.iraqmortality.html > install.packages("lancet.iraqmortality") ... > library(lancet.iraqmortality) Loading required package: foreign > ?lancet.iraqmortality > vignette("mortality") This is

Hi everyone, We are looking for some data sets working with relative risk mortality. so, someone know where can I find the data.mgus dataset and the data.mgus? Using 1384 records from Minnesota. This data set are used in the : Robert A. Kyle, Terry M. Therneau, S. Vincent Rajkumar, Janice R. Offord, Dirk R. Larson, Matthew F. Plevak, and L. Joseph Melton III. A long-term study of prognosis in

I have a mortality data over many years and I wish to plot the data and also add some smoother to clearly highlight the trend. How could I do that in R with base graphics or ggplot? I have the following sample data: require(lubridate) mdate<-seq(ymd('2000-01-01'),ymd('2010-12-31'), by = '1 day') death<- rnorm(4018, 80, 45) df<-cbind(mdate,death)

V&R describes binomial GLMs with mortality out of 20 budworms. Is it appropriate to use the same approach with mortality out of numbers as low as 3? I feel reticent to do so with data that is not very continuous. There are one continuous and one categorical independent variables. Would it be more appropriate to treat the response as an ordered factor with four levels? If so, what family