similar to: spaghetti plot - categorical variable differentiated by color

Greetings, I am new to R. Right now I'm most interested in the spaghetti plots of achievement over time by student ID associated with longitudinal analysis. How can I do a spaghetti plot of all students, but color the lines by group membership such as gender or race, and indicate this color scheme in the legend? Any advice would be much appreciated. Jack Jack B. Monpas-Huber, Ph.D.

I would like to plat some spaghetti plots from my data , ma data is as follows ak[1:3,] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 0.3211745 0.4132568 0.5649930 0.6920562 0.7760113 0.8118568 0.8609301 0.9088819 0.9326736 [2,] 0.3159234 0.4071270 0.5579212 0.6844584 0.7684690 0.8243702 0.8677043 0.8931288 0.9261926 [3,] 0.3075260 0.3993699

I am attempting to estimate LC50 (analogous to LD50, but uses exposure concentration rather than dose) by fitting a Weibull model; but I can't seem to get it to work. From what I can gather, I should be using survreg() from the survival package. The survreg() function relies on time-to-event data; my data result from 96 h exposures (i.e., dead or alive after a fixed period; 96 h). I've

Hi, I am trying to apply the example at the bottom of the following page to my own data: http://128.97.141.26/stat/R/faq/spagplot.htm http://128.97.141.26/stat/R/faq/spagplot.htm The code from the example is: /tolerance<-read.table("http://www.ats.ucla.edu/stat/R/faq/tolpp.csv",sep=",", header=T) fit <- by(tolerance, tolerance$id,function(x) fitted.values(lm(tolerance

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

Dear R Helpers I am trying to get function smedian.hilow to work using Hmisc summarize on variable conc in dataframe pkindivmtd by time and dose using: attach(pkindivmtd) sconc <- summarize(conc,llist(time,dose),smedian.hilow) I get the error message Erreur dans 1:nrow(X) : argument NA / NaN Contents of pkindivmtd is > contents(pkindivmtd) Data frame:pkindivmtd 1296 observations

All, I have been able to successfully use the optim( ) function with "L-BFGS-B" to find reasonable parameters for a one-compartment open pharmacokinetic model. My loss function in this case was squared error, and I made no assumptions about the distribution of the plasma values. The model appeared to fit pretty well. Out of curiosity, I decided to try to use nlminb( ) applied to a

Hi Rosa, You pass a vector to ggplot, which expects a data.frame. I am sure you meant to do this: point7$y_point7 <- point7$beta0_7 + point7$beta1_7*point7$time + point7 $epsilon_7 ggplot(point7, aes(time, y_point7)) + geom_line() HTH Ulrik On Wed, 19 Jul 2017 at 20:37 Rosa Oliveira <rosita21 at gmail.com> wrote: > Hi everyone, > > I?m trying to do a spaghetti plot and I

Hi everyone, I?m trying to do a spaghetti plot and I know I?m doing all wrong, It must be. What I need: 15 subjects, each with measurements over 5 different times (t1, ..., t5), and the variable that I need to represent in the spaguetti plot is given by: PCR = b0 + b1 * ti + epsilon B0, - baseline of each subject B1 - trajectory of each subject over time (so multiply by t) Epsilon - error

Hi, I am new to R, so I am unsure of the formula to set up this analysis. I would like to run a linear model with a continuous dependent variable (brain volume) and a continuous independent variable (age) while controlling for a categorical nuisance variable (gender). Age and brain volume are correlated. There are no gender differences in age but there are significant gender differences in brain

Hi R-helpers, I was trying to put gender='Male' in newdata to create a expected survival curve for a pseudo cohort by using survfit based on Cox regression. My codes are shown below: fit<- coxph(Surv(end, status2)~gender, data=wlwsn1) Summary(fit) coef exp(coef) se(coef) z p genderMale 0.204 1.23 0.0912 2.23 0.025

A couple of weeks ago there was a question regarding apparent convergence in nls when using the SSfol selfStart model for fitting a first-order pharmacokinetic model. I can't manage to find the original message either in my archive or in the list archives but the data were time conc dose 0.50 5.40 1 0.75 11.10 1 1.00 8.40 1 1.25 13.80 1 1.50 15.50 1

Dear listers, I am trying to fit a model using nlsList() using alternately a SSfol() selfstart function or its developped equivalent formulae. This preliminary trial works well mydata<-groupedData(Conc~Tps|Organ,data=mydata) mymod1<-nls(Conc~SSfol(Dose,Tps,lKe,lKa,lCl),data=mydata) as well as a developped form: mymod2<-nls(Conc~Dose * exp(lKe+lKa-lCl) *

Hello, I am trying to determine LD50 and LD95 using dose.p in MASS however some of the Residual variance is larger than the degrees of freedom. Please can anyone help with any advice as to how i can correct for this? Here is the model as inputted into R y<-cbind(dead,n-dead) model<-glm(y~log(conc),binomial) summary(model) xv<-seq(min(log(conc)-1),max(log(conc)+1),0.01)

Dear R users: I encountered difficulties in michaelis-menten equation. I found that when I use right model definiens, I got wrong Km vlaue, and I got right Km value when i use wrong model definiens. The value of Vd and Vmax are correct in these two models. #-----right model definiens-------- PKindex<-data.frame(time=c(0,1,2,4,6,8,10,12,16,20,24),

Dear All,   I could use a bit of help here, this function is hard to figure out (for me at least) I have the following so far:   PKindex<-data.frame(Subject=c(1),time=c(1,2,3,4,6,10,12),conc=c(32,28,25,22,18,14,11)) Dose<-200 Tinf <-0.5   defun<- function(time, y, parms) {  dCpdt <- -parms["kel"] * y[1]  list(dCpdt)  } modfun <- function(time,kel, Vd) {   out <-

Dear list, I do have a problem with nls. I use the following data: >test time conc dose 0.50 5.40 1 0.75 11.10 1 1.00 8.40 1 1.25 13.80 1 1.50 15.50 1 1.75 18.00 1 2.00 17.00 1 2.50 13.90 1 3.00 11.20 1 3.50 9.90 1 4.00 4.70 1 5.00 5.00 1 6.00 1.90 1 7.00 1.90 1 9.00 1.10 1 12.00 0.95 1 14.00

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

I have a number of x, y observations (Time, Conc) for a number of Subjects (with subject number Subj) and Doses. I can plot the individual points with xyplot fine: xyplot(Conc ~ Time | Subj, Groups=Dose, data=myData, panel = function(x,y) { panel.xyplot(x, y) panel.superpose(???) # Needs more here } ) I also like to plot on

Thank you that is exactly it! The problem was to connect each point of the series 'Conc' with a line. Best regards Luigi On Sat, Jul 1, 2023 at 8:33?PM Chris Evans <chrishold at psyctc.org> wrote: > > > On 01/07/2023 19:20, Luigi Marongiu wrote: > > Hello, > > I have a dataframe with measurements stratified by the concentration > > of a certain substance.