Sum contrast model intercept for multiple factors You are using contr sum where all levels are compared to the last level, and with the added constraint that all the coefficients (except the intercept) sum up to zero The grand only holds when the number of observations in each category is equal, for example:
linear regression in R: contr. treatment vs contr. sum Following are two linear regression models with the same predictors and response variable, but with different contrast coding methods In the first model, the contrast coding method is quot;contr
Setting contrasts in lme4: contr. treatment vs contr. sdif I am not entirely sure if I should code the variable "Session" using contr treatment or (a modified version of) contr sdif [MASS] For contr treatment(3) (See below), I understand that the first contrast compares the second level against the first level while the second contrast compares the third level against the first level 2 3 1 0 0 2 1 0
r - Confused about sum and treatment contrasts - Cross Validated The short answer to your question is that treatment or 'dummy' variables sum to 1 for each observation row In the sum coding system the variable, representing the same thing, sum to 0 for each observation row
Meaning of Error in contr. treatment(n = 0L) - Cross Validated We are attempting to model and compare logistic growth over time for 6 different treatments using nlme So far, we have successfully added random effects of individuals However, when we try to add
treatment and sum contrasts, inconsistent results I am fitting a linear mixed effect models with two factors (mPair with 6 levels, and spd_des with 3 levels) and their interaction I obtain inconsistent results depending on the contrasts that I ch
Partial eta square values summing to greater than 1 I am trying to run a factorial ANOVA analysis with partial effects, but it seems that my partial eta square values are totalling above 1, particularly when I specify my contrast with Type II or Typ