similar to: Drawing a line in xyplot

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

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

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

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 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) + +

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)

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

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]]

I'm trying to do a linear regression between the columns of matrices. In example below I want to regress column 1 of matrix xdat with column1 of ydat and do a separate regression between the column 2s of each matrix. But the output I get seems to give correct slopes but incorrect intercepts and another set of slopes with value NA. How do I do this correctly? I'm after the slope and

Hi guys, How could I specify minimum and maximum x-axis values in logi.hist.plot? My code is something like this: plot(mydata$Temperature,mydata$Mortality, ,xlab="Temperature",ylab="Probability of mortality") curve(predict(temp.glm,data.frame(Temperature=x),type="resp"),add=TRUE, col="red") points(mydata$Temperature,fitted(temp.glm),pch=20)

Hi... I'm trying to understand the following syntax: cor.test(~mortality + hardness,data=water,method="pearson") which is the same as: cor.test(water$mortality,water$hardness,data=water,method="pearson") Can anyone point me to the correct doc or explain to me how to interpret "~mortality + hardness"? Thanks, Joe

Dear listeRs; I am hoping to provide myself the ability to interpolate mortality estimates within intervals for a range of conditions and ages. I have four mortality tables derived from the Society of Actuaries 2001 VBT tables that contains interval mortality rates by starting age and year of duration. I would like to flexibly interpolate using a large set (~4MM) of ages and conditions

Can anyone explain to me what this error message means, why I'm getting it, and how to fix it? lines(lowess(xdat, ydat, f=.5), col=3) Error: NAs in foreign function call (arg 1) ______________________________________________________________________ Stuart Luppescu -=-=- University of Chicago ºÍʸ ¤ÈÃÒÆàÈþ¤ÎÉã(EUC) -=-=- s-luppescu at uchicago.edu

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

David Flynn has proposed a new Ogg Dirac mapping. The draft is here: http://davidf.woaf.net/dirac-mapping-ogg.pdf This is a much bigger break from other codecs than my draft (at http://wiki.xiph.org/index.php/OggDirac). We talked a bit about it on IRC today. Below is my summary; hopefully David can correct anything I got wrong or misleading. Comments? There are two main differences

Dear All, I hope to run some simple survival analysis using the cox-proportional hazard models in R, my command will look like below: cox <- summary( coxph( Surv( mortality , TIME ) ~ Independent variables ) ) My query is about specifying a range of independnt variables in R, such that each independent variable is included as the main defining variable independently of other variables in the

Hello. I am trying to write an R function to plot the survival function (and associated hazard and density) for a Siler competing hazards model. This model is similar to the Gompertz-Makeham, with the addition of a juvenile component that includes two parameters---one that describes the initial infant mortality rate, and a negative exponential that describes typical mortality decline over the

Hi List, The following code returns an "Error in as.POSIXlt.character(x, tz, ...) : character string is not in a standard unambiguous format" require(raster) require(rts) require(stringi) r <- raster(ncol=100, nrow=100) values(r) <- runif(ncell(r)) list(ID=seq(1:24),month=rep(str_pad(1:12, pad = 0,width = 2 , "left"),2),year=sort(rep(2016:2017,12)))->dt