Flush warnings() cache and convert warnings to error for debugging

Flush warnings
assign("last.warning", NULL, envir = baseenv())
Convert warnings to error
options(warn = 2)

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Standard error from Hessian Matrix... what can be done when problem occurs

An Hessian matrix is a square matrix of partial second order derivative. From the square-root of the inverse of the diagonal, it is possible to estimate the standard error of parameters. Let take an example: val=rnorm(100, mean=20, sd=5) # Return -ln L of values in val in Gaussian distribution with mean and sd in par fitnorm<-function(par, val) {   -sum(dnorm(val, par["mean"], par["sd"], log = TRUE)) } # Initial values for search p<-c(mean=20, sd=5) # fit the model result <- optim(par=p, fn=fitnorm, val=val, method="BFGS", hessian=TRUE) # Inverse the hessian matrix to get SE for each parameters mathessian <- result$hessian inversemathessian <- solve(mathessian) res <- sqrt(diag(inversemathessian)) # results data.frame(Mean=result$par, SE=res)   library(MASS) (r<-fitdistr(val, "normal")) It works well. However, the inverse of the matrix cannot be calculated if the second order derivative for some paramete...
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Multivariable analysis and correlation of iconography

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Introduction Correlation iconography is a not very well known method to study multivariate data. It was developed long-time ago by Michel Lesty: Lesty M (1999) Une nouvelle approche dans le choix des régresseurs de la régression multiple en présence d'intéractions et de colinearités. La revue de Modulad 22:41-77 It is also well described in a French Wikipedia page: https://fr.wikipedia.org/wiki/Iconographie_des_corrélations After checking the possibilities of this method, I think that it deserves more attention. Let take the example of the wikipedia page: dta <- read.table(text=gsub(",", ".", "Élève Poids Âge Assiduité Note e1 52 12 12 5 e2 59 12,5 9 5 e3 55 13 15 9 e4 58 14,5 5 5 e5 66 15,5 11 13,5 e6 62 16 15 18 e7 63 17 12 18 e8 69 18 9 18"), header=TRUE) > dta   Élève Poids  Âge Assiduité Note 1    e1    52 12.0        12  5.0 2    e...
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stepAIC from package MASS with AICc

Due to a bug in dropterm() and addterm() in package MASS, it is impossible to use AICc with stepAIC() . Here is a solution. Enter these commands and now you can use: stepAIC(g0, criteria="AICc") or stepAIC(g0, criteria="BIC") For example: datax <- data.frame(y=rnorm(100), x1=rnorm(100),                     x2=rnorm(100), x3=rnorm(100), x4=rnorm(100)) g0 <- glm(y ~ x1+x2+x3+x4, data=datax) gx_AICc <- stepAIC(g0, criteria="AICc") gx_AIC <- stepAIC(g0, criteria="AIC") gx_AIC <- stepAIC(g0, criteria="BIC")
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