search for: genderm

Hi, As far as I am aware, the model.matrix function does not return perfect metadata on what each column of the model matrix "means". The columns are named (e.g. age:genderM), but encoding the metadata as strings can result in ambiguity. For example, the dummy variables created when the factors var0 = 0 and var = 00 both are named var00. Additionally, if a level of a factor variable contains a colon, this could be confused for an interaction. While a human can general...

Hi R-users, I would like to calculate weighted mean of several variables by two factors where the weight vector is the same for all variables. Below, there is a simple example where I have only two variables: "v1","v2" both weighted by "wt" and my factors are "gender" and "year". set.seed(1) df <- data.frame(gender = rep(c("M",

...AgeGroup, family = binomial, data = mydata1, na.action = na.pass) Deviance Residuals: Min 1Q Median 3Q Max -1.828 -0.973 -0.709 1.087 1.734 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.2939 0.3180 4.069 4.73e-05 *** GenderM -0.8794 0.1637 -5.371 7.85e-08 *** GenderNa -1.4407 0.2749 -5.240 1.60e-07 *** AgeGroup2 -1.2053 0.3971 -3.035 0.00240 ** AgeGroup3 -1.6670 0.3262 -5.110 3.21e-07 *** AgeGroup4 -1.0786 0.3714 -2.904 0.00368 ** AgeGroup5 -0.8232 0.3829 -2.150 0...

...Pr(>|z|) (Intercept) 2.63826 0.97870 2.69568 0.00702 cao -2.08963 0.11987 -17.43314 0.00000 subj1 0.02608 0.23573 0.11064 0.91190 subj2 -0.55668 0.32759 -1.69932 0.08926 subj3 -1.57120 0.30664 -5.12400 0.00000 genderM 0.36368 0.09188 3.95845 0.00008 yr 0.06067 0.01658 3.65996 0.00025 ageentry -0.00720 0.04338 -0.16598 0.86817 as.factor(yrs5)1 -0.25181 0.05712 -4.40806 0.00001 as.factor(yrs5)2 -0.54725 0.07601 -7.20005 0.00000 as.factor(yrs5)3 -1.0...