Trying to run an ANOVA for fields in a dataframe [R] - r

Question

So far my code looks like this:
Points = readOGR(dsn = "./Data/filename.shp",layer = "layername",stringsAsFactors = FALSE)
Points$LDI = extract(LDI, Points)
LDI = raster("./Data/filename2.tif")
Points$LDI = extract(LDI, Points)
PointsDF = Points#data
for(i in PointsDF) {
Mod1 = lm(LDI ~ i, data = PointsDF)
Mod2 = lm(LDI ~ 1, data = PointsDF)
anova(Mod1, Mod2)
}
This last part is where I know I'm doing everything wrong. I want to run the anova on every numerical field in the data frame.

Answer 1

You're close. A natural way is to loop over the field names. Although there are many ways to do this, lapply is perhaps the most idiomatic because (a) it uses the field names (rather than field indexes, which can be dangerous) and (b) does not require pre-allocating any structures for the output. The trick is to convert field names into formulas. Again, there are many ways to do this, but a direct way is to assemble the formula as a string.
Here is working code as an example. It produces a list of anova objects.
#
# Create some random data.
#
n <- 20
set.seed(17)
X <- data.frame(Y=rnorm(n), X1=runif(n), X2=1:n, X3=rexp(n))
#
# Loop over the regressors.
# (The base model can be precomputed.)
#
mod.0 <- lm(Y ~ 1, X)
models <- lapply(setdiff(names(X), "Y"), function(s) {
mod.1 <- lm(as.formula(paste("Y ~", s)), X)
anova(mod.0, mod.1)
})
print(models)
Here's the output, displaying this list of three anova results.
[[1]]
Analysis of Variance Table
Model 1: Y ~ 1
Model 2: Y ~ X1
Res.Df RSS Df Sum of Sq F Pr(>F)
1 19 10.1157
2 18 9.6719 1 0.44385 0.826 0.3754
[[2]]
Analysis of Variance Table
Model 1: Y ~ 1
Model 2: Y ~ X2
Res.Df RSS Df Sum of Sq F Pr(>F)
1 19 10.1157
2 18 8.1768 1 1.939 4.2684 0.05353 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[3]]
Analysis of Variance Table
Model 1: Y ~ 1
Model 2: Y ~ X3
Res.Df RSS Df Sum of Sq F Pr(>F)
1 19 10.116
2 18 10.081 1 0.034925 0.0624 0.8056
As another example of working with what you have produced, here is sapply being used to print out their p-values:
sapply(models, function(m) m[["Pr(>F)"]][2])
[1] 0.37542968 0.05352883 0.80562894

Answer 2

The issue is that you are not telling the loop what it is iterating on, defing a formula object in the anova call nor creating an object to store results.
In this example the "ij" variable is assigning to the list object and storing the anova models, "y" defined as a variable indicating the left-hand side of the model. The list object "anova.results" is storing each model. The index in the loop definition is using "which" to assign which column contains "y" and as such, drops it from the iterator. I am using the R "iris" dataset for the example.
data(iris)
iris <- iris[,-5]
y = "Sepal.Length"
anova.results <- list()
ij=0
for(i in names(iris)[-which(names(iris) == y)]) {
ij = ij+1
Mod = lm(stats::as.formula(paste(y, i, sep = "~")), data = iris)
anova.results[[ij]] <- anova(Mod, Mod)
}
anova.results