similar to: lattice: plotting an arbitrary number of panels, defining arbitrary groups

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 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

Hi R users,I am a beginner in the use of R. I need urgent help for my thesis study. <https://stats.stackexchange.com/posts/633206/timeline> I have daily air pollution parameters PM10, PM2.5 CO, NO2, SO2, and O3. I also have daily hospital admission numbers. Taking into account the effect of weekends and holidays, I would like to used generalised additive model (GAM) to explore the

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

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 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

Hi R Folks, I need to plot a stacked bar plot with row labels as A,B,C,... and each bar divided by x,y,.... Here is the data: x y A 1 .5 B -.2 .6 C .3 -.7 D .4 .8 (for e.g. x,y,... could be air pollutants and A,B,C,... could be months) Issue 1) It has negative values. Issue 2) It contain two categorical variables. Can anyone help me? Thanks, Gary [[alternative HTML version deleted]]

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 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

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

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

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

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

I need to split a data frame into 3 columns. The column I want to split contains indices of lag (prefix L1 or L2 and suffix 01, 03, 04), station name (shown in the sample data as capitalized G, P and S) and pollutant name. Names with no ?L? prefix or 01/04 suffix are lag 0. Lag 01 is average of lag 0 and 1, and 04 is average of 0 to 4 days. How can one do that in R? I will ignore the other

Could you help me with a problem? I should put non-linear variables into zelig-model, how can that be done? I'm dealing with air pollution data, trying to find out daily associations between mortality and air pollutants. Weather variables used as confounders are in some cases non-linear. Since smoothing is not an option I don't know how to proceed. Thanks, Jaana

I'm trying to plot smooth trend using smoothTrend in OpenAir but I'm having problems. I used the following code. --------------------------------------------------------------------------------- #Set my working dir to the dir with my files setwd("c:/R") #Load the openair library library(openair) #Load the data mydata <- read.table("MCNP-pH.csv", header=TRUE,

Funciona perfecto! Gracias Carlos. Atte. Novvier Uscuchagua De: Carlos Ortega [mailto:cof en qualityexcellence.es] Enviado el: viernes, 16 de junio de 2017 13:46 Para: Novvier Marco Uscuchagua Cornelio <novvier en outlook.com.pe> CC: r-help-es en r-project.org Asunto: Re: Varios gráficos en una vista en OpenAir Hola, Prueba entonces esto, que sí que funciona... #-----------------------

Hi, I'd like to know whether R is capable to assess parameters in a model describing the kinetic of a pollutant adsorption onto activated carbon. A common relation is for instance the Adam-Bohart-Thomas' one: dx/dt = K1 * (qm-x)*C - K2x where {K1,K2} are the unknown paramters and {qm,C} are known parameters Of course I get experimental data sets of measured x as a function of time.