search for: invlogit

I have installed the arm package and its dependents (e.g MATRIX, etc), but cannot use the functions "invlogit" and "bayesglm" because it gives me the error message "Error: could not find function "invlogit" or Error: could not find function "invlogit". What could be the problem. Regards Carrington [[alternative HTML version deleted]]

Dear R users: I have run a logistic regression, used Gelman et al.'s car package to simulate the parameter estimates of that model, and have plotted the probability (using Gelman et al.'s invlogit() function) of the dependent variable being 1 given the value of a particular independent variable is at its mean. The plot has probabilities on the y-axis and the number (1-1000) of the simulation run on the x-axis. What I would like to do is to make the points that make up the 95% CI a differ...

Hi folks, Warning: I don't know if the result I am getting makes sense, so this may be a statistics question. The fitted values from my binomial lmer mixed model seem to consistently overestimate the cell means, and I don't know why. I assume I am doing something stupid. Below I include code, and a binary image of the data is available at this link:

...Generic Function for Making Coefficient Plot | contr.bayes.ordered Contrast Matrices | corrplot Correlation Plot | display Functions for Processing lm, glm, mer and polr | Output | fround Formating the Rounding of Numbers | invlogit Inverse logistic function | lalonde Lalonde Dataset | matching Single Nearest Neighborhood Matching | model.matrix.bayes Construct Design Matrices | multicomp.plot Multiple Comparison Plot | rescale Function for Standardizi...

...lt;1243462090.6898.32.camel@yod> Content-Type: text/plain; charset="UTF-8" Le mercredi 27 mai 2009 ? 17:28 +1000, Bill.Venables@csiro.au a ?crit : > You can accommodate the constraints by, e.g., putting > > c2 = pnorm(theta2) > c3 = pnorm(theta3) Nice. I'd have tried invlogit(), but I'm seriously biased... > x1 has a known coefficient (unity) so it becomes an offset. > Essentially your problem can be written > > y1 = y-x1 = c1 + pnorm(theta2)*x2 - pnorm(theta3)*x3 + error > > This is then a (pretty simple) non-linear regression which could be &g...