Michael Cohn
2023-Sep-26 05:12 UTC
[R] car::deltaMethod() fails when a particular combination of categorical variables is not present
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]]
John Fox
2023-Sep-26 13:49 UTC
[R] car::deltaMethod() fails when a particular combination of categorical variables is not present
Dear Michael, You're testing a linear hypothesis, so there's no need to use the delta method, but the linearHypothesis() function in the car package also fails in your case: > linearHypothesis(minimal_model, "bt2 + csent + bt2:csent = 0") Error in linearHypothesis.lm(minimal_model, "bt2 + csent + bt2:csent = 0") : there are aliased coefficients in the model. One work-around is to ravel the two factors into a single factor with 5 levels: > df$bc <- factor(with(df, paste(b, c, sep=":"))) > df$bc [1] t2:unsent t2:unsent t2:unsent t2:unsent t2:sent t2:unsent [7] t2:unsent t1:sent t2:unsent t2:unsent t2:other t2:unsent [13] t1:unsent t1:sent t2:unsent t2:other t1:unsent t2:sent [19] t2:sent t2:unsent Levels: t1:sent t1:unsent t2:other t2:sent t2:unsent > m <- lm(a ~ bc, data=df) > summary(m) Call: lm(formula = a ~ bc, data = df) Residuals: Min 1Q Median 3Q Max -57.455 -11.750 0.439 14.011 37.545 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 20.50 17.57 1.166 0.2617 bct1:unsent 37.50 24.85 1.509 0.1521 bct2:other 32.00 24.85 1.287 0.2174 bct2:sent 17.17 22.69 0.757 0.4610 bct2:unsent 38.95 19.11 2.039 0.0595 Residual standard error: 24.85 on 15 degrees of freedom Multiple R-squared: 0.2613, Adjusted R-squared: 0.06437 F-statistic: 1.327 on 4 and 15 DF, p-value: 0.3052 Then the hypothesis is tested directly by the t-value for the coefficient bct2:sent. I hope that this helps, John -- John Fox, Professor Emeritus McMaster University Hamilton, Ontario, Canada web: https://www.john-fox.ca/ On 2023-09-26 1:12 a.m., Michael Cohn wrote:> Caution: External email. > > > 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 at 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.