search for: indep2

...bbr, data = data.sub, method = "ML") #Get the coefficients for the fixed effects summary(F)$coefficients$fixed THE OUTCOME: > i.levels<-c("Tall","Short","Mixed","NoType") > summary(F)$coefficients$fixed (Intercept) indep1 indep2 PDSI PDSI2 indep1:PDSI 115.20850196 2.13074177 -18.64650516 2.32042307 -0.46510460 0.09965080 indep2:PDSI indep1:PDSI2 indep2:PDSI2 0.90607575 0.01106839 -0.28827352 Other order: > i.levels<-c("Mixed","Short","Tall","NoT...

Dear subscribers, I am looking for a function which would allow me to model the dependent variable as the number of successes in a series of Bernoulli trials. My data looks like this ID TRIALS SUCCESSESS INDEP1 INDEP2 INDEP3 1 4444 0 0.273 0.055 0.156 2 98170 74 0.123 0.456 0.789 3 145486 30 0.124 0.235 0.007 4 147149 49 0.888 0.357 0.321 5 60585 11 0.484 0.235 0.235 6...

...I have a matrix of observations and a matrix of independant variables - examples would trying to predict countries's GDP with their data on education, FDI, tax rates, over time. For the purpose of simplicity, my data would be: dep = matrix(rnorm(50),ncol=5) indep1 = matrix(rnorm(50),ncol=5) indep2 = matrix(rnorm(50),ncol=5) >From what I could find, the lme{nlme} function would be the function. However, after I installed the package and I type in: > lme(dep~indep1, indep2) Error in model.frame.default(formula = ~indep1 + dep, data = c(-0.63665929869261, : 'data' must be a...

Hello, could anyone explain what am I doing wrong. When I use colldiag function from package perturb I get different Variance Decomposition Proportions matrix in R than in SAS, although the eigenvalues and indexes are the same. Thanks for your attention. Results: in R: eigen(cor(indep2)) $values [1] 4.197131e+00 6.674837e-01 9.462858e-02 4.070314e-02 5.323022e-05 colldiag(indep2,c=T) Condition Index Variance Decomposition Proportions a b c d e 1 1.000 0.236 0.205 0.235 0.212 0.111 2 2.508 0.000 0.113 0.000 0.097 0.791 3 6.660 0....