This is what I just got, and now the dwt() returns with no errors. I am even more confused.
gfr <- read.delim("GlastonburyFestivalRegression.dat", header = TRUE)
gfr$crusty <- gfr$music == "Crusty"
gfr$metaller <- gfr$music == "Metaller"
gfr$indie.kid <- gfr$music == "Indie Kid"
gfr.1 <- lm(gfr$change ~ gfr$crusty + gfr$metaller + gfr$indie.kid, data = gfr)
summary(gfr.1)
#>
#> Call:
#> lm(formula = gfr$change ~ gfr$crusty + gfr$metaller + gfr$indie.kid,
#> data = gfr)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1.82569 -0.50489 0.05593 0.42430 1.59431
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.55431 0.09036 -6.134 1.15e-08 ***
#> gfr$crustyTRUE -0.41152 0.16703 -2.464 0.0152 *
#> gfr$metallerTRUE 0.02838 0.16033 0.177 0.8598
#> gfr$indie.kidTRUE -0.40998 0.20492 -2.001 0.0477 *
#> ---
#> Signif. codes: 0 '' 0.001 '' 0.01 '' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.6882 on 119 degrees of freedom
#> (687 observations deleted due to missingness)
#> Multiple R-squared: 0.07617, Adjusted R-squared: 0.05288
#> F-statistic: 3.27 on 3 and 119 DF, p-value: 0.02369
library(car)
#> Warning: package 'car' was built under R version 3.4.4
#> Loading required package: carData
library(carData)
vif(gfr.1)
#> gfr$crusty gfr$metaller gfr$indie.kid
#> 1.137931 1.143818 1.100084
1/vif(gfr.1)
#> gfr$crusty gfr$metaller gfr$indie.kid
#> 0.8787879 0.8742647 0.9090214
dwt(gfr.1)
#> lag Autocorrelation D-W Statistic p-value
#> 1 0.04948997 1.893407 0.534
#> Alternative hypothesis: rho != 0
reprex::reprex(input = "dwt_Question.R", outfile = "dwt_Question.md")