These factor variables are nice but make sure you understand whatparameterization you are getting and how to interpret it!!!Personally I think there is much to be said for explicitly includingthe main effects so
Subject Re-Post: Stata 11 - Factor variables in a regression command Date Sat, 1 May 2010 01:38:52 -0400I apologize for the re-posting. My original message was split in two and parts of it were cut out. I hope submitting directly from within Gmail will solve the problem. Original mes...
First, we need to create some example data that we can use in our linear regression:set.seed(2580) # Create random example data N <- 1000 x <- sample(1:5, N, replace = TRUE) y <- round(x + rnorm(N), 2) x <- as.factor(x) data <- data.frame(x, y) head(data) # x ...
factor score regressionStructural equation models with latent variables are sometimes estimated using an intuitive three-step approach, here denoted factor score regression. Consider a structural equation model composed of an explanatory latent variable and a response latent variable related by a structural...
For example, for a colour variable with three levels (blue, red, green), we create two dummy variables. X1X2=={10 if the colour is blue if the colour is not blue{10 if the colour is red if the colour is not red Then both of these variables can be used in the regression equati...
The determinacy of the regression factor score predictor based on continuous parameter estimates from categorical variablesCategorical variablesDeterminacyFactor score predictorThe estimation of population parameters of the conHilgerUnivNorbertUnivBeauducel
3. There are NO assumptions about the distribution of the predictor (independent) variables in any regression. However, parameter estimates generally are only interpretable for nominal categories or numerical quantities. The coefficient is interpreted as the difference in the mean of Y, the outcome, ...
Theloading plotis a plot of the relationship between the original variables and the subspace dimension. It is used to interpret relationships between variables.载荷图解释原始变量和主成分关系 横坐标表示第一主成分与原始变量之间的相关系数;纵轴表示第二主成分与原始变量之间相关系数。
当模型中存在过多的国家和行业因子, 即大量虚拟变量 (Dummy Variables)时, 此现象则会影响整体因子收益估算的稳定性. 所以业界给出的普遍解决方案是在线性模型的基础上, 给国家和行业的因子收益施加额外的约束条件, 即 r = Bf+ \epsilon \sum_i w_if_i=0,i\in 国家或行业 即在模型成立的条件下额外使...
Abstract Structural equation models with latent variables are sometimes estimated using an intuitive three-step approach, here denoted factor score regression. Consider a structural equation model composed of an explanatory latent variable and a response latent variable related by a structural parameter of...