search for: independent1

Hi, I have created a dataframe (lets call is df) that contains the following variables "Dependent1" "Dependent2" Dependent3" "Independent1" I want to do the following regressions: z<- glm( df$Dependent1 ~ df$Independent1) z<- glm( df$Dependent2 ~ df$Independent1) z<- glm( df$Dependent3 ~ df$Independent1) and so on I wanted to put this in a for loop e.g. for (i in 1:3) { z<- glm( df$ColumnName_i ~ df$Independen...

...column4", "Select random variable", choices = names(data())) }) # Assigning user inputs to correct variables selected_columns <- reactive({ req(data(), input$column1, input$column2, input$column3, input$column4) list( dependent = data()[[input$column1]], independent1 = as.factor(data()[[input$column2]]), independent2 = as.factor(data()[[input$column3]]), random = as.factor(data()[[input$column4]]), # Define column names for later use column1 <- input$column1, column2 <- input$column2, column3 <- input$column3,...

Why would someone use lm and ANOVA (anova(lm(x))) instead of AOV (or the other way around)? The mean squares and sum of squares are the same, but the F values and p-values are slightly different. I am modeling a dependent~independent1*independent2. Thanks, Matt Bridgman [[alternative HTML version deleted]]

...dle. What about a bootstrapping algorithm than handles populations as the sample unit instead of individual data points? I bet mixed models could handle this problem somehow, but I'd like to stick to the theme of a mantel test. The data look like this pop1 pop2 genetic_distance(dependent) (independent1) popa popb data data popb popd data data popc pope data data popf popg data data popg poph data data So for example, popb appears twice, as does popg. Clearly each row is not independent. Thanks for any insight Rob. -- View this message in context: http://r.789695.n...