similar to: Plot fitted vs observed values

Dear WizaRds, Given the experimental data, csdata<-data.frame( time=c(0,1,3,9,20), conc=c(638.697,395.69,199.00,141.58,112.16) ) weighted nls is applied, wt.MM<- function(resp, time,A1,a1,A2,a2) { pred <- A1*exp(-a1*time)+A2*exp(-a2*time) (resp - pred) / sqrt(pred) } # cs.wt <- nls( ~ wt.MM(conc, time,A1,a1,A2,a2), data=csdata,

Dear Wiza[R]ds, I have a data.frame header that looks like this: v2FfaPre15 v2FfaPre10 v2FfaPre5 v2Ffa2 v2Ffa3 v2Ffa4 I need it to look like this, 15 10 5 2 3 4 i.e., with v2FfaPre and v2Ffa stripped off Any suggestions, Thanks in advance! -- Oscar Oscar A. Linares, MD Translational Medicine Unit LaPlaisance Bay, Bolles Harbor Monroe, Michigan 48161

Dear Wiza[R]ds, I am very grateful to Duncan Murdoch for his assistance with this problem. His help was invaluable. However, the problem has become a little more complicated for me. Now, in each plot, I need to plot the observed and fitted values of a supine and upright posture experiment. Here is what I have and how far I got. # tritiated (3H)-Norepinephrine(NE) disappearance from plasma #

Dear Wiza[R]ds, I have the following data in a data.frame. I need to do an anova with multiple comparison but I don't know how to factor the groups for analysis. There are 3 groups, 1,2 and 3 labelled in column 1. Help appreciated with thanks in advance. Group SI Sif SG Io I2 lol2 1 9.08e-05 9.08e+00 0.060842287 1.798556446 32.574500

Dear Rexperts, I am trying to add a '+' identifying the mean in a boxplot using the following sizelist <- split(size, grp) centers <- boxplot(sizelist, style.bxp = "att", medpch = "o", ylab = "Prostate Volume (cm3)") points(centers, unlist(lapply(sizelist, mean)), pch = "+") But, I get error Error in xy.coords(x, y) :

Dear Wiz[R]ds, I am deeply grateful for the help from Duncan Murdoch, Gray Calhoun, and others. We are almost there. For whatever reason, I can't change the symbol from a circle to a triangle in the upright posture plots. Any ideas? I have included the problem in full. # tritiated (3H)-Norepinephrine(NE) disappearance from plasma # concentrations supine and upright # supine datasu <-

Dear Rexperts, Given, aar <-function(command) { switch(command, {scrn = cat("scrn :Screening","\n")} {dx = cat("dx :Diagnosis","\n")} {df = cat("df :Don't Forget","\n")} ) } I want to be able to do: aar("dx") # function does cat("dx :Diagnosis","\n")

After some processing... ct.df <- data.frame(time,conc) ct.df gives time conc 1 0 164.495456 2 1 133.671185 3 2 108.622975 4 3 88.268468 5 4 71.728126 6 5 58.287225 7 6 47.364971 8 7 38.489403 9 8 31.276998 10 9 25.416103 11 10 20.653462 12 11 16.783276 13 12 13.638313 14

Dear Rxperts, Running the following code: ======================================================= twlo=10; twhi=20; wt=154; vd=0.5; cl=0.046; tau=6; t=3; F=1; wtkg <- wt/2.2 # convert lbs to kg vd.pt <- wtkg * vd # compute weight-based vd (L) cl.pt <- wtkg * cl # compute CL (L/hr) k <- cl.pt/vd.pt # compute k (hr^-1) cave <-

Hello everyone, I am trying to solve 2D differential equations using finite difference scheme in R. I have been able to work with the equations with only one spatial dimensions but I want to extend it to the two dimensional problem. For example i can simulate one dimensional diffusion using a code like the following. But I want to write a similar code for,say, a two dimensional diffusion

Dear WizaRds, A run of nls produces the following concise summary: > summary(cs.wt) Formula: 0 ~ wt.MM(conc, time, A1, a1, A2, a2) Parameters: Estimate Std. Error t value Pr(>|t|) A1 4.814e+02 2.240e+01 21.495 0.0296 * a1 7.401e-01 7.435e-02 9.956 0.0637 . A2 1.613e+02 1.738e+01 9.280 0.0683 . a2 1.770e-02 7.324e-03 2.417 0.2497

Hi all R users, I have problems with my second loop for drawing the three curves in the same graphic. I need help please Thank you in advance ######################################################################### simulation <- function(k, n){ conc <- seq(0,100,by=0.5) #choixg <- seq(1, length(conc)) choixg <- rep(0,length(conc)) for (i in 1:length(conc)){ choixg[i] <- (k

Hello all R users, My simulation function works correctly, but I have problems with plot function. You will find the following code using it. Thank you for your help ##################################################" simulation <- function(k, n){ conc <- seq(0,10,by=0.5) #choixg <- seq(1, length(conc)) choixg <- rep(0,length(conc)) for (i in 1:length(conc)){ choixg[i]

Hi, I think you're misunderstanding which set of variables go on either side of the formula. Is this what you're looking for? > aggregate(OD ~ Time + Target + Conc, data = df, FUN = "mean") Time Target Conc OD 1 1 BACT 1 765.3333 2 1 BACT 2 745.3333 3 1 BACT 3 675.0000 > aggregate(ODnorm ~ Time + Target + Conc, data = df, FUN =

#how do I break these up into first two letters (RM), number, and then the last part #is there an easily accessible regex tutorial on the internet? v = (structure(1:122, .Label = c("RM215Temp", "RM215SpCond", "RM215DO.Conc", "RM215Depth", "RM215pH", "RM215ORP", "RM215Turbidity.", "RM215Battery", "RM215DO.",

Hi I have measured the UV absorbance (abs) of 10 solutions of a substance at known concentrations (conc) and have used a linear model to plot a calibration graph with confidence limits. I now want to predict the concentration of solutions with UV absorbance results given in the new.abs data.frame, however predict.lm only appears to work for new "conc" variables not new "abs"

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 am having trouble fitting correlation structures within nlme. I would like to fit corCAR1, corGaus and corExp correlation structures to my data. I either get the error "step halving reduced below minimum in pnls step" or alternatively R crashes. My dataset is similar to the CO2 example in the nlme package. The one major difference is that in my case the 'conc' steps are

Also, > aggregate(cbind(OD, ODnorm) ~ Time + Target + Conc, data = df, FUN = "mean") Time Target Conc OD ODnorm 1 1 BACT 1 765.3333 108.33333 2 1 BACT 2 745.3333 88.33333 3 1 BACT 3 675.0000 18.00000 (You might wish for "cbind(OD,ODnorm) ~ . - Well", but aggregate.formula is not smart enough for that.) -pd > On 24 Oct 2023, at

I have used Sweave a lot the latest year, but never really used any long function calls. If I have code which look like this ------------------------------------------------------------- gof <- benthic.flux(ID="Gulf of Finland", meas.conc=conc, bw.conc=bw.conc, time=times,