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- 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
- references - ANOVA Type III understanding - Cross Validated
Contr treatment (Default in R and several other statistics systems): Compares each level to a reference level, which does not ensure orthogonality and can lead to non-independence in the presence of interactions, making it less suitable for Type III tests
- r - Multiple Factor Analysis with FactoMineR: error with categorical . . .
contrasts<-(*tmp*, value = contr funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels Finally the MFA ran (apparently) well After all, do you think MFA is actually the appropriate analysis for my data? I choose MFA because of the data involves continuous and categorical variables
- R gives me the error contrasts can be applied only to factors with 2 . . .
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- Sum contrast model intercept for multiple factors
How is the intercept calculated for a linear model with multiple factors using contr sum From what I've read the intercept is equal to the "grand mean", which as I can understand it is essentially the mean of the mean for each level
- r - Polynomial contrasts for regression - Cross Validated
I cannot understand the usage of polynomial contrasts in regression fitting In particular, I am referring to an encoding used by R in order to express an interval variable (ordinal variable with e
- Confused about sum and treatment contrasts - Cross Validated
Thanks I worked through the first three examples there, but I don't really have a problem with understanding the contrasts and their interpretation when doing a lm () I'm more confused about the relation between the coding matrix and resulting contrast matrix (see footnote [1] in my question) Perhaps it's more the linear algebra that's eluding me?
- r - Why do sum and treatment contrasts give the same coefficients in . . .
I have been given a dummy dataset upon which linear regression is performed and treatment and sum contrasts outputs are compared In this scenario the coefficients are exactly the same and I don't
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