search for: 0.06667

I have the joint distribution of three discrete random variables z1, z2 and z3 which is captured by "z" and "prob" as described below. For example, the probability for z1=0.46667, z2=-1 and z3=-1 is 2.752e-13. Also, the probability adds up to 1. > head(z) z1 z2 z3 [1,] -0.46667 -1.0000 -1.0000 [2,] -0.33333 -0.9333 -0.9333 [3,] -0.20000 -0.8667 -0.8667

Most likely, previous computations have ended up giving slightly different values of say 0.13333. A pragmatic way out is to round to, say, 5 digits before applying unique. In this particular case, it seems like all numbers are multiples of 1/30, so another idea could be to multiply by 30, round, and divide by 30. -pd > On 28 Jul 2017, at 17:17 , li li <hannah.hlx at gmail.com> wrote:

Estimada comunidad, tengo unas dudas que son muy básicas creo, pero es mi primera incursión en GLM. Estoy ajustando un modelo binomial a unos datos de germinación. El modelo es muy sencillo, tengo un factor "Condicion" con dos niveles: "a" y "b" (nivel de humedad en suelo). Por otro lado, tengo una variable explicativa "HF" (horas frío=estratificación) que

I'd like to use linear and quadratic trend analysis in order to find out a change in slope. Basically, I need to solve a similar problem as discussed in http://www.gseis.ucla.edu/courses/ed230bc1/cnotes4/trend1.html My subjects have counted dots: one dot, two dots, etc. up to 9 dots. The reaction time increases with increasing dots. The theory is that 1 up to 3 or 4 points can be counted