search for: beta_3

...lts. I get the following message: BHHH maximisation Number of iterations: 0 Return code: 100 Initial value out of range. I don't know this is so because of the way i have written my loglikelihood or what. The loglikelihood LogLik<-function(param){ beta_1<-param[1] beta_2<-param[2] beta_3<-param[3] beta_4<-param[4] lambda.plus<-beta_1*Iplusless1 + beta_2*Iminusless1 lambda.minus<-beta_3*Iplusless1 + beta_4*Iminusless1 sum(Iplus_new*log(lambda.plus))-sum(log(lambda.plus)*x_new) + sum(Iminus_new*log(lambda.minus))-sum(log(lambda.minus)*x_new) } The gradients gradient&l...

Dear lmer-ers, My thanks for all of you who are sharing your trials and tribulations publicly. I was hoping to elicit some feedback on my thoughts on denominator degrees of freedom for F ratios in mixed models. These thoughts and practices result from my reading of previous postings by Doug Bates and others. - I start by assuming that the appropriate denominator degrees lies between n

...set(final), I repeat the process 100 times and obtain 100 such vectors which I store as 100 rows in a 100X5 matrix: matrix[i,]=coef(reg)[best,] (i varying from 1 to 100) Now my final estimates for the beta's are: Beta_0=median(matrix[,1]) Beta_1=median(matrix[,2]) Beta_2=median(matrix[,3]) Beta_3=median(matrix[,4]) Beta_4=median(matrix[,5]) I want to find the p-values of each of the estimated beta's. I am confused how to extract these p values in R (may be we need to go back to the reg= lm.ridge model corresponding to each final beta estimate, but I am not sure how to do this through c...