First off, I have a dataset with sparse longitudinal data. There are 30 individuals with 1 sample, 30 individuals with 2 samples, and 5 individuals with 3 samples. Various categorical variables are known for each individual and I want to see if these variables are correlated with a drug level (a continuous variable). Let's just focus on one categorical variable: homelessness. The main issue is that the number of people who are homeless is not equal to those who are not so I cannot perform a simple wilcoxon signed rank test or most other paired tests. As a result, I generated a linear model to see the relationship between homelessness and the drug levels using a random slope/intercept for each individual and another for those who are not homeless. Of course, if I just perform an ANOVA(linearmodel1, linearmodel2) I get the result:

"all fitted objects must use the same number of observations".


As pointed in comment by @Roland (see link below in comments), one approach is to combine the data and make 2 models: 1 with the variable homelessness and 1 without. Using polynomial regression this can be done with:

###Create some example data
mydata1 <- subset(iris, Species == "setosa", select = c(Sepal.Length, Sepal.Width))
mydata2 <- subset(iris, Species == "virginica", select = c(Sepal.Length, Sepal.Width))

#add a grouping variable
mydata1$g <- "a"
mydata2$g <- "b"

#combine the datasets
mydata <- rbind(mydata1, mydata2)

#model without grouping variable
fit0 <- lm(Sepal.Width ~ poly(Sepal.Length, 2), data = mydata)

###model with grouping variable
fit1 <- lm(Sepal.Width ~ poly(Sepal.Length, 2) * g, data = mydata)

#Compare models
anova(fit0, fit1)
enter code here

#But this doesnt work in nlme
fit1 <- lme(Sepal.Width ~ Sepal.Length * g, data=mydata)
#It throws an error:
"invalid formula for groups"

######Not sure if this is the correct way
#model without grouping variable
model0 = gls(Sepal.Width ~ Sepal.Length,data=mydata)
#model with grouping variable
model1 = lme(Sepal.Width ~ Sepal.Length ,random = ~1|g,data=mydata)
#model without grouping variable
fm0 <- lm(Div ~ TimeRaw,ddmerged)
#model with grouping variable
fm1 <- lmer(Sepal.Width ~ Sepal.Length+(1|g),mydata, REML=FALSE)

But how do I create two models with and without a specific group using nlme/lme4?

Thanks in advance


marked as duplicate by Roland, user158565, Siong Thye Goh, Robert Long, mdewey Jun 9 at 14:38

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

  • $\begingroup$ There seems to be some confusion in your question regarding what a paired design (which makes a paired test or mixed-effects model appropriate) actually is. Anyway, you can easily solve this by not creating two separate models but one combined model. The approach would be similar to what I show in this answer. $\endgroup$ – Roland Jun 6 at 6:05
  • $\begingroup$ Ok, so I have to generate 2 models from a merged dataset, one with the grouping and one without. While a polynomial regression is similar, it is not the same and is implemented differently. For example, what is the equivalent of "poly(Sepal.Length, 2) * g" for a linear model, say in the nlme package or the lme4 package? $\endgroup$ – user250071 Jun 6 at 18:50
  • $\begingroup$ I believe lme4::lme has an anova.lme method. $\endgroup$ – AdamO Jun 6 at 20:52
  • $\begingroup$ Yes....of course you can use anovas with lme4. But I do not know how to exclude / include a variable using these packages. $\endgroup$ – user250071 Jun 6 at 20:58
  • $\begingroup$ Show your lme4 model formula and I can explain ... $\endgroup$ – Roland Jun 7 at 6:01

For parametric test, t-test is the one you should use. But you have to make sure the populations are normally distributed. For non parametric it would be Wilcoxon Rank Sum Test on two independent samples.


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