I'm running a linear regression with two categorical predictors and their interaction. One combination of levels does not occur in the data, and as expected, no parameter is estimated for it. I now want to significance test a particular combination of levels that does occur in the data (ie, I want to get a confidence interval for the total prediction at given levels of each variable).
In the past I've done this using car::deltaMethod() but in this dataset that does not work, as shown in the example below: The regression model gives the expected output, but deltaMethod() gives this error: error in t(gd) %*% vcov. : non-conformable arguments I believe this is because there is no parameter estimate for when the predictors have the values 't1' and 'other'. In the df_fixed dataframe, putting one person into that combination of categories causes deltaMethod() to work as expected. I don't know of any theoretical reason that missing one interaction parameter estimate should prevent getting a confidence interval for a different combination of predictors. Is there a way to use deltaMethod() or some other function to do this without changing my data? Thank you, - Michael Cohn Vote Rev (http://voterev.org) Demonstration: ------ library(car) # create dataset with outcome and two categorical predictors outcomes <- c(91,2,60,53,38,78,48,33,97,41,64,84,64,8,66,41,52,18,57,34) persontype <- c("t2","t2","t2","t2","t2","t2","t2","t1","t2","t2","t2","t2","t1","t1","t2","t2","t1","t2","t2","t2") arm_letter <- c("unsent","unsent","unsent","unsent","sent","unsent","unsent","sent","unsent","unsent","other","unsent","unsent","sent","unsent","other","unsent","sent","sent","unsent") df <- data.frame(a = outcomes, b=persontype, c=arm_letter) # note: there are no records with the combination 't1' + 'other' table(df$b,df$c) #regression works as expected minimal_formula <- formula("a ~ b*c") minimal_model <- lm(minimal_formula, data=df) summary(minimal_model) #use deltaMethod() to get a prediction for individuals with the combination 'b2' and 'sent' # deltaMethod() fails with "error in t(gd) %*% vcov. : non-conformable arguments." deltaMethod(minimal_model, "bt2 + csent + `bt2:csent`", rhs=0) # duplicate the dataset and change one record to be in the previously empty cell df_fixed <- df df_fixed[c(13),"c"] <- 'other' table(df_fixed$b,df_fixed$c) #deltaMethod() now works minimal_model_fixed <- lm(minimal_formula, data=df_fixed) deltaMethod(minimal_model_fixed, "bt2 + csent + `bt2:csent`", rhs=0) [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
