similar to: How to calculate relative risk from GAM model in mgcv package?

R Friends, I'm running R2.7.1 on Windows XP. I'm trying to get some lattice functionality which I have not seen previously documented--I'd like to plot the exact same data in multiple panels but changing the grouping variable each time so that each panel highlights a different feature of the data set. The following code does exactly that with a simple and fabricated air quality data

Hi R people, I want to write some functions to automate the plotting of some expressions that use some of the plotmath capabilities. An example is a string supplied to a plot call such as: plot(1, 1, ylab = "pm10 (ug/m3)") This should actually appear like: plot(1, 1, ylab = expression("PM"[10] * " (" * mu * "g m" ^-3 * ")")) i.e. pollutant

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

i read a book from WOOD, there's an example which is talking about the pollutant. library(gamair) library(mgcv) y<-gam(death~s(time,bs="cr",k=200)+s(pm10median,bs="cr")+s(so2median,bs="cr")+s(o3median,bs="cr")+s(tmpd,bs="cr"),data=chicago,family=Possion) lag.sum<-function(a,10,11) {n<-length(a) b<-rep(0,n-11) for(i in 0:(11-10))

Hi Gustav, Try this: lapply(1:length(models),function(i) lapply(models[[i]],function(x) summary(x)$coef[2,]))[[1]] #1st list component [[1]] #??? Estimate?? Std. Error????? z value???? Pr(>|z|) # pm10 #5.999185e-04 1.486195e-04 4.036606e+00 5.423004e-05 #[[2]] #??? Estimate?? Std. Error????? z value???? Pr(>|z|) #ozone #0.0010117294 0.0003792739 2.6675428048 0.0076408155 #[[3]] #???

Dear Experts, I am trying to do a time-stratified case-crossover analysis on air pollution data and number of myocardial infarctions. In order to avoid model selection bias, I started with a simple simulation. I'm still not sure if my simulation is right. But the results I get from the "ts-case-crossover" are much more variable than those from a glm. Is this: a. Due to

Dear Experts, I am trying to do a time-stratified case-crossover analysis on air pollution data and number of myocardial infarctions. In order to avoid model selection bias, I started with a simple simulation. I'm still not sure if my simulation is right. But the results I get from the "ts-case-crossover" are much more variable than those from a glm. Is this: a. Due to the simple

dear R users, my data looks like this PM10 Ref UZ JZ WT RH FT WR 1 10.973195 4.338874 nein Winter Dienstag ja nein West 2 6.381684 2.250446 nein Sommer Sonntag nein ja Süd 3 62.586512 66.304869 ja Sommer Sonntag nein nein Ost 4 5.590101 8.526152 ja Sommer Donnerstag nein nein Nord 5 30.925054 16.073091 nein Winter Sonntag nein

Hello, i would like to import this txt file: Giorno;PM10 2006-01-01 10:10;10.3 2006-02-02 20:22;50.3 2006-03-03 23:33;20.1 ......... As it's an irregular time series i use zoo as follow: require(zoo) z <- read.table("c:\\1.csv", sep=";", na.strings="-999", header=TRUE) q <- zoo(z$PM10, strptime(as.character(z$Giorno),"%Y-%m-%d %H:%M")) At this

Dear everyone, I'm encountering an unknown error message when using gamm function: > fitoutput <- gamm(cvd~as.factor(dow)+pm10+s(time,bs="cr",k=15,fx=TRUE)+s(tmean,bs="cr",k=7,fx=TRUE) + ,correlation=corAR1(form=~1|city),family=poisson,random=list(city=~pm10),data=mimp) Maximum number of PQL iterations: 20 iteration 1 iteration 2 iteration 3 iteration 4

I'm working with data measured in a tunnel to estimate the emission factor of heavy & light vehicles. I tried to use 'gnls' and I get the following Error: >> Error in "coef<-.corARMA"(*tmp*, value = c(174.267493382104, 173.412740072763 : >> Coefficient matrix not invertible Here is my R-code: data <- d.plabutsch.neu # calculating the starting

Hi All, I'm trying to get following but not successfully: # this works okay ylab=expression("PM2.5 Concentration ("*mu*g/m^3*")") # But I need "2.5" as a subscript to PM and below this an additional line saying some text, like. ylab=expression("PM[2.5] Concentration ("*mu*g/m^3*")"\n(random text in brackets)) Square brackets doesn't

Hi - I am trying to find and describe missing data in a time series. For instance, in the library openair, there is a data frame called "mydata": library(openair) head(mydata) date ws wd nox no2 o3 pm10 so2 co pm25 1 1998-01-01 00:00:00 0.60 280 285 39 1 29 4.7225 3.3725 NA 2 1998-01-01 01:00:00 2.16 230 NA NA NA 37 NA NA NA 3 1998-01-01 02:00:00

Dear all, i am trying to redo the 'eyestudy' analysis presented on the site http://www.ats.ucla.edu/stat/stata/faq/relative_risk.htm with R (1.9.0), with special interest in the section on "relative risk estimation by poisson regression with robust error variance". so i guess rlm is the function to use. but what is its equivalent to the glm's argument "family"

OK. (Note: I am ccing this to the list so that others can correct any mistakes, misunderstandings, or misstatements that I may make; and also give you more and better advice about how to proceed. For example, there may be environmental packages ( see the environmetrics task view, https://CRAN.R-project.org/view=Environmetrics) that already do everything you want from your data source that someone

I am analyzing particulate matter data (PM10) on a small data set (147 observations). I fitted a semi-parametric model and am worried about overfitting. How can one check for model fit in GAM? Jean G. Orelien

Ted Harding said: > I can get the estimated RRs from > RRs <- exp(summary(GLM)$coef[,1]) > but do not see how to implement confidence intervals based > on "robust error variances" using the output in GLM. Thanks for the link to the data. Here's my best guess. If you use the following approach, with the HC0 type of robust standard errors in the

my data looks like this: PM10 Ref UZ JZ WT RH FT WR 1 10.973195 4.338874 nein Winter Dienstag ja nein West 2 6.381684 2.250446 nein Sommer Sonntag nein ja Süd 3 62.586512 66.304869 ja Sommer Sonntag nein nein Ost 4 5.590101 8.526152 ja Sommer Donnerstag nein nein Nord 5 30.925054 16.073091 nein Winter Sonntag nein nein Ost 6

Greetings - I am working on some data that contain chemical names with air concentrations, and I am creating a data frame with date/time and each chemical having its own column. However, these are organic chemicals (e.g. 1-butene, 2,3,4-trimethylbenzene etc). The package I am going to be using the data with is openair, and many of the great functions require you to specify a column name which

Dear R community, I have currently some problems with non linear regression analysis in R. My data correspond to the degradation kinetic of a pollutant in two different soil A and B, x data are time in day and y data are pollutant concentration in soil. In a first time, I want to fit the data for the soil A by using the Ct = C0*exp(-k*Tpst) with Ct the concentration of pollutant at time t, C0