search for: facotr

Hola a todos, Ajusté un modelo lineal mixto en el cual tengo una interacción significativa entre dos facotres, cada uno con 3 niveles (osea un diseño 3x3). Esta es la tabla de medias             P1                P 2             P3 M1      220.66       311.85        260.80 M2      348.57        89.33         191.71 M3      103.57      381.37         511.62 Con el fin de interpretar correctamente el s...

...returned from the lm function. I expected these to be the mean values for each factor in the model. Given this data and model: data<-c(rnorm(10,mean=0,sd=1),rnorm(10,mean=1,sd=1),rnorm(10,mean=-.5,sd=1)) ftr<-as.factor(rep(1:3,each=10)) fit<-lm(data ~ ftr) the mean values of the three facotrs from the data: c(mean(data[1:10]), mean(data[11:20]), mean(data[21:30])) [1] -0.3589049 0.6034931 -0.7256897 are not the same as the coefficients return from fit: fit$coef (Intercept) ftr2 ftr3 -0.3589049 0.9623980 -0.3667847 ftr2 and ftr3 are offset by the value of the inte...

Hi, I am a very occasional user of R, and will be grateful for some help in constructing a regression across groups. Here is an example: library(MASS) attach(cats) Sex[120:144]<-factor(TG) #Renaming some males to transgender, to create 3 groups, male, female and transgender out<-lm(Bwt~Sex/Hwt) #Gives me 3 separate linear regressions for groups M, F and TG What I now want to do

Hello, Does anyone know of a way I can speed this up? Basically I'm attempting to get the data item on the same row as the report date for each report date available. In reality, I have over 11k of columns, not just A, B, C, D and I have to do that over 100 times. My solution is slow, but it works. The loop is slow because of merge. # create sample data z.dates =