similar to: Run R script automatically each morning and email results?

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)

I've written a simple script that does some surveillance analysis on daily counts of walk-in clinic visits, for our county health department. (Actually, Michael Hohle's surveillance package does all the work; I just customized it a little to work with the way our data are recorded.) The output consists of a graph and a little table. My colleague will run it once in a while, on

Changing the vapply() behavior makes sense in principle. I analyzed the CRAN code base using the R parser and found 143 instances of calling vapply with USE.NAMES=FALSE. These would need to be inspected to understand the consequences of the change. For reference: /AzureML/R/datasets.R:226 /BBmisc/R/toRangeStr.R:33 /DBI/R/DBDriver.R:205 /Kmisc/R/str_rev.R:37 /Matrix/R/diagMatrix.R:98

Dear I hope you are wery well when read this e-mail.My name is Firas ALSHAWY and I am a Phd student in the high Institute of marine research - tishreen university- Syria I would like to want some help my in my research, I have data ( length - weight - gonad weight - fishing mortality - natural mortality - the number of fish-temperature of water, maximum age ) , I would like to use the package (

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

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]),

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

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

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 just got a spam from telephonydepot (which I invited to spam me, so I guess I have to call it legit marketing :) ), and they have some new device that is meant to be a surveillance camera with audio, but the interface is POE and SIP! A cool idea. Anyone playing with this toy yet? I am trying to wrap my head around how asterisk might fit with the model of this camera being

Hello, I am interested in using Morans I for different time intervals to detect disease clusters. Ultimately I would like to use CUSUM - or similar monitoring statistic to monitor the results of Morans I - similar to the work by Rogerson (2005) Spatial Surveillance and Cummulative Sum Methods in Spatial and Syndromic Surveillance for Public Health. Thus far - thanks to the list I have

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

Please let me know if this is or is not the right place to ask these types of questions. Warning: I am new to R by two days. I have a simple dataset. I have loaded the dataset successfully using the following code: Filepath=(C:\temp\\pilot\dataset1.txt") Pilot=read.table(filepath, header=TRUE) Dataset1.txt is delimited and looks like this: Date illness count 2006/01/01 derm 17 2006/01/01