When running previously working code with new data I get the following error:
res.aov <- anova_test(
data = c1_l1, dv = value, wid = Month,
within = c(buffer, TimePeriod),
effect.size = "ges",
detailed = TRUE,
)
# Error in str2lang(x) : <text>:1:10: unexpected input
# 1: cbind(100_
Obviously, this suggests the data is the problem, but I can't see why that might be. The data have been recreated using the same process as previously, prepared in the same way, and as far as I can tell are in the same format and syntax.
Data:
> dput(head(c1_l1))
structure(list(buffer = c(2, 3, 4, 5, 6, 7), date = structure(c(18270,
18270, 18270, 18270, 18270, 18270), class = "Date"), Year = structure(c(3L,
3L, 3L, 3L, 3L, 3L), levels = c("2016", "2017", "2020"), class = "factor"),
Month = c(1, 1, 1, 1, 1, 1), TimePeriod = structure(c(2L,
2L, 2L, 2L, 2L, 2L), levels = c("1", "2"), class = "factor"),
variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L), levels = c("lst_c1_l1_lst",
"lst_c1_l1_single_lst", "lst_c2_l1_lst", "lst_c2_l1_single_lst",
"lst_c2_l2_lst"), class = "factor"), value = c(39.42118494,
39.46416636, 39.48439384, 39.49212885, 39.23692955, 39.2688776
)), row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"
))
> str(lst)
'data.frame': 2400 obs. of 7 variables:
$ buffer : num 200 200 200 200 200 300 300 300 300 300 ...
$ date : Date, format: "2020-01-09" "2020-01-09" "2020-01-09" ...
$ Year : Factor w/ 3 levels "2016","2017",..: 3 3 3 3 3 3 3 3 3 3 ...
$ Month : num 1 1 1 1 1 1 1 1 1 1 ...
$ TimePeriod: Factor w/ 2 levels "1","2": 2 2 2 2 2 2 2 2 2 2 ...
$ variable : Factor w/ 5 levels "lst_c1_l1_lst",..: 1 2 3 4 5 1 2 3 4 5 ...
$ value : num 39.4 39.4 39.7 39.7 39.7 ...
Code:
# Some of the below are not used - subsetted from larger R file
library(ggplot2)
library(ggpubr)
library(tidyverse)
library(rstatix)
library(ez)
library(dplyr)
lst <- read.csv("./Data/By metric/LST/landsat_lsts_long.csv", encoding="UTF-8", check.names = FALSE)
str(lst)
lst$buffer <- as.numeric(lst$buffer)
lst$date <- as.Date(lst$date, format = "%d/%m/%Y")
lst$Year <- as.factor(lst$Year)
lst$Month <- as.numeric(lst$Month)
lst$TimePeriod <- as.factor(lst$TimePeriod)
lst$variable <- as.factor(lst$variable)
#lst$weather_variable <- as.factor(lst$weather_variable)
str(lst)
c1_l1 <- as_tibble(filter(lst, variable == "lst_c1_l1_lst"))
res.aov <- anova_test(
data = c1_l1, dv = value, wid = Month,
within = c(buffer, TimePeriod),
effect.size = "ges",
detailed = TRUE,
)
get_anova_table(res.aov, correction = "auto")
Can anyone explain the error or suggest a solution?
Edit
Larger sample of data (500 of 2400 rows):
> dput(lst[1:500, ])
structure(list(buffer = c(200, 200, 200, 200, 200, 300, 300,
300, 300, 300, 400, 400, 400, 400, 400, 500, 500, 500, 500, 500,
600, 600, 600, 600, 600, 700, 700, 700, 700, 700, 800, 800, 800,
800, 800, 900, 900, 900, 900, 900, 1000, 1000, 1000, 1000, 1000,
1100, 1100, 1100, 1100, 1100, 1200, 1200, 1200, 1200, 1200, 1300,
1300, 1300, 1300, 1300, 1400, 1400, 1400, 1400, 1400, 1500, 1500,
1500, 1500, 1500, 1600, 1600, 1600, 1600, 1600, 1700, 1700, 1700,
1700, 1700, 1800, 1800, 1800, 1800, 1800, 1900, 1900, 1900, 1900,
1900, 2000, 2000, 2000, 2000, 2000, 100, 100, 100, 100, 100,
200, 200, 200, 200, 200, 300, 300, 300, 300, 300, 400, 400, 400,
400, 400, 500, 500, 500, 500, 500, 600, 600, 600, 600, 600, 700,
700, 700, 700, 700, 800, 800, 800, 800, 800, 900, 900, 900, 900,
900, 1000, 1000, 1000, 1000, 1000, 1100, 1100, 1100, 1100, 1100,
1200, 1200, 1200, 1200, 1200, 1300, 1300, 1300, 1300, 1300, 1400,
1400, 1400, 1400, 1400, 1500, 1500, 1500, 1500, 1500, 1600, 1600,
1600, 1600, 1600, 1700, 1700, 1700, 1700, 1700, 1800, 1800, 1800,
1800, 1800, 1900, 1900, 1900, 1900, 1900, 2000, 2000, 2000, 2000,
2000, 100, 100, 100, 100, 100, 200, 200, 200, 200, 200, 300,
300, 300, 300, 300, 400, 400, 400, 400, 400, 500, 500, 500, 500,
500, 600, 600, 600, 600, 600, 700, 700, 700, 700, 700, 800, 800,
800, 800, 800, 900, 900, 900, 900, 900, 1000, 1000, 1000, 1000,
1000, 1100, 1100, 1100, 1100, 1100, 1200, 1200, 1200, 1200, 1200,
1300, 1300, 1300, 1300, 1300, 1400, 1400, 1400, 1400, 1400, 1500,
1500, 1500, 1500, 1500, 1600, 1600, 1600, 1600, 1600, 1700, 1700,
1700, 1700, 1700, 1800, 1800, 1800, 1800, 1800, 1900, 1900, 1900,
1900, 1900, 2000, 2000, 2000, 2000, 2000, 100, 100, 100, 100,
100, 200, 200, 200, 200, 200, 300, 300, 300, 300, 300, 400, 400,
400, 400, 400, 500, 500, 500, 500, 500, 600, 600, 600, 600, 600,
700, 700, 700, 700, 700, 800, 800, 800, 800, 800, 900, 900, 900,
900, 900, 1000, 1000, 1000, 1000, 1000, 1100, 1100, 1100, 1100,
1100, 1200, 1200, 1200, 1200, 1200, 1300, 1300, 1300, 1300, 1300,
1400, 1400, 1400, 1400, 1400, 1500, 1500, 1500, 1500, 1500, 1600,
1600, 1600, 1600, 1600, 1700, 1700, 1700, 1700, 1700, 1800, 1800,
1800, 1800, 1800, 1900, 1900, 1900, 1900, 1900, 2000, 2000, 2000,
2000, 2000, 100, 100, 100, 100, 100, 200, 200, 200, 200, 200,
300, 300, 300, 300, 300, 400, 400, 400, 400, 400, 500, 500, 500,
500, 500, 600, 600, 600, 600, 600, 700, 700, 700, 700, 700, 800,
800, 800, 800, 800, 900, 900, 900, 900, 900, 1000, 1000, 1000,
1000, 1000, 1100, 1100, 1100, 1100, 1100, 1200, 1200, 1200, 1200,
1200, 1300, 1300, 1300, 1300, 1300, 1400, 1400, 1400, 1400, 1400,
1500, 1500, 1500, 1500, 1500, 1600, 1600, 1600, 1600, 1600, 1700,
1700, 1700, 1700, 1700, 1800, 1800, 1800, 1800, 1800, 1900, 1900,
1900, 1900, 1900, 2000, 2000, 2000, 2000, 2000, 100, 100, 100,
100, 100), date = structure(c(18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270, 18270,
18270, 18270, 18270, 18270, 18270, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182, 17182,
17182, 17182, 17182, 17182, 17182, 17182, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302, 18302,
18302, 18302, 18302, 18302, 18302, 18302, 18302, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334,
18334, 18334, 18334, 18334, 18334, 18334, 18334, 18334, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230,
17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230, 17230
), class = "Date"), Year = structure(c(3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), levels = c("2016",
"2017", "2020"), class = "factor"), Month = c(1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), TimePeriod = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), levels = c("1", "2"), class = "factor"), variable = structure(c(1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L), levels = c("lst_c1_l1_lst", "lst_c1_l1_single_lst",
"lst_c2_l1_lst", "lst_c2_l1_single_lst", "lst_c2_l2_lst"), class = "factor"),
value = c(39.42118494, 39.42118494, 39.69002203, 39.69002203,
39.69002203, 39.46416636, 39.46416636, 39.73204192, 39.73204192,
39.73204192, 39.48439384, 39.48439384, 39.75312025, 39.75312025,
39.75312025, 39.49212885, 39.49212885, 39.77773065, 39.77773065,
39.77773065, 39.23692955, 39.23692955, 39.50876672, 39.50876672,
39.50876672, 39.2688776, 39.2688776, 39.5308463, 39.5308463,
39.5308463, 39.26009488, 39.26009488, 39.51862556, 39.51862556,
39.51862556, 39.23362907, 39.23362907, 39.49314495, 39.49314495,
39.49314495, 39.22544967, 39.22544967, 39.48686828, 39.48686828,
39.48686828, 39.22921385, 39.22921385, 39.49069941, 39.49069941,
39.49069941, 39.22121268, 39.22121268, 39.47842417, 39.47842417,
39.47842417, 39.25855237, 39.25855237, 39.5177284, 39.5177284,
39.5177284, 39.31654836, 39.31654836, 39.57357954, 39.57357954,
39.57357954, 39.29034224, 39.29034224, 39.54928469, 39.54928469,
39.54928469, 39.21842964, 39.21842964, 39.48037064, 39.48037064,
39.48037064, 39.18790111, 39.18790111, 39.43845258, 39.43845258,
39.43845258, 39.15256059, 39.15256059, 39.41036294, 39.41036294,
39.41036294, 39.11216125, 39.11216125, 39.36755125, 39.36755125,
39.36755125, 39.08030747, 39.08030747, 39.33167812, 39.33167812,
39.33167812, 39.12993955, 39.12993955, 39.39689619, 39.39689619,
39.39689619, 43.8048763, 43.8048763, 44.17214494, 44.17214494,
44.17214494, 43.77706328, 43.77706328, 44.14471764, 44.14471764,
44.14471764, 43.76715395, 43.76715395, 44.12919958, 44.12919958,
44.12919958, 43.6734276, 43.6734276, 44.03295273, 44.03295273,
44.03295273, 43.43969325, 43.43969325, 43.79043297, 43.79043297,
43.79043297, 43.46617317, 43.46617317, 43.82164862, 43.82164862,
43.82164862, 43.45817407, 43.45817407, 43.81200158, 43.81200158,
43.81200158, 43.49256715, 43.49256715, 43.86329877, 43.86329877,
43.86329877, 43.49382639, 43.49382639, 43.86636556, 43.86636556,
43.86636556, 43.48534435, 43.48534435, 43.85745411, 43.85745411,
43.85745411, 43.38213544, 43.38213544, 43.75286456, 43.75286456,
43.75286456, 43.3023932, 43.3023932, 43.6661483, 43.6661483,
43.6661483, 43.24328463, 43.24328463, 43.60471125, 43.60471125,
43.60471125, 43.09468321, 43.09468321, 43.44744563, 43.44744563,
43.44744563, 42.94489759, 42.94489759, 43.29081306, 43.29081306,
43.29081306, 42.85892867, 42.85892867, 43.19730725, 43.19730725,
43.19730725, 42.76776694, 42.76776694, 43.10273585, 43.10273585,
43.10273585, 42.68127243, 42.68127243, 43.01059651, 43.01059651,
43.01059651, 42.66140238, 42.66140238, 42.99118471, 42.99118471,
42.99118471, 43.70369864, 43.70369864, 44.07106004, 44.07106004,
44.07106004, 36.70611252, 36.70611252, 36.9107136, 36.9107136,
36.9107136, 36.72775555, 36.72775555, 36.92395327, 36.92395327,
36.92395327, 36.74749898, 36.74749898, 36.93449529, 36.93449529,
36.93449529, 36.6533153, 36.6533153, 36.83617274, 36.83617274,
36.83617274, 36.44506508, 36.44506508, 36.63608161, 36.63608161,
36.63608161, 36.43721713, 36.43721713, 36.62929251, 36.62929251,
36.62929251, 36.43733177, 36.43733177, 36.62324992, 36.62324992,
36.62324992, 36.43583453, 36.43583453, 36.61967969, 36.61967969,
36.61967969, 36.40827146, 36.40827146, 36.589868, 36.589868,
36.589868, 36.3907843, 36.3907843, 36.57116139, 36.57116139,
36.57116139, 36.35854057, 36.35854057, 36.54628954, 36.54628954,
36.54628954, 36.40712661, 36.40712661, 36.59210861, 36.59210861,
36.59210861, 36.43340117, 36.43340117, 36.60861537, 36.60861537,
36.60861537, 36.31455504, 36.31455504, 36.48974973, 36.48974973,
36.48974973, 36.22447072, 36.22447072, 36.39436863, 36.39436863,
36.39436863, 36.21140555, 36.21140555, 36.38112795, 36.38112795,
36.38112795, 36.19563266, 36.19563266, 36.35853431, 36.35853431,
36.35853431, 36.16204925, 36.16204925, 36.32527651, 36.32527651,
36.32527651, 36.16206731, 36.16206731, 36.3293186, 36.3293186,
36.3293186, 36.43714217, 36.43714217, 36.622595, 36.622595,
36.622595, 28.09393998, 28.09393998, 28.15452004, 28.15452004,
28.15452004, 28.05127491, 28.05127491, 28.10864511, 28.10864511,
28.10864511, 28.02972073, 28.02972073, 28.04448871, 28.04448871,
28.04448871, 28.03610704, 28.03610704, 28.04071712, 28.04071712,
28.04071712, 28.02902252, 28.02902252, 27.99060337, 27.99060337,
27.99060337, 28.05021483, 28.05021483, 28.01166398, 28.01166398,
28.01166398, 28.03387381, 28.03387381, 27.9922879, 27.9922879,
27.9922879, 27.96075552, 27.96075552, 27.90532568, 27.90532568,
27.90532568, 27.92236376, 27.92236376, 27.87621457, 27.87621457,
27.87621457, 27.88527292, 27.88527292, 27.87611205, 27.87611205,
27.87611205, 27.84768492, 27.84768492, 27.85894957, 27.85894957,
27.85894957, 27.83940352, 27.83940352, 27.84847982, 27.84847982,
27.84847982, 27.81627322, 27.81627322, 27.82635454, 27.82635454,
27.82635454, 27.7815786, 27.7815786, 27.77298566, 27.77298566,
27.77298566, 27.76044908, 27.76044908, 27.73830099, 27.73830099,
27.73830099, 27.74557724, 27.74557724, 27.73498831, 27.73498831,
27.73498831, 27.67807817, 27.67807817, 27.73279985, 27.73279985,
27.73279985, 27.68961021, 27.68961021, 27.7333632, 27.7333632,
27.7333632, 27.72383966, 27.72383966, 27.77542992, 27.77542992,
27.77542992, 27.94160345, 27.94160345, 27.97709364, 27.97709364,
27.97709364, 32.44370349, 32.44370349, 32.50556817, 32.50556817,
32.50556817, 32.36811356, 32.36811356, 32.43038012, 32.43038012,
32.43038012, 32.38152433, 32.38152433, 32.43476867, 32.43476867,
32.43476867, 32.36183697, 32.36183697, 32.40781406, 32.40781406,
32.40781406, 32.29013031, 32.29013031, 32.33402053, 32.33402053,
32.33402053, 32.33718591, 32.33718591, 32.3814359, 32.3814359,
32.3814359, 32.33472529, 32.33472529, 32.38747789, 32.38747789,
32.38747789, 32.3284777, 32.3284777, 32.38604191, 32.38604191,
32.38604191, 32.31624064, 32.31624064, 32.37249513, 32.37249513,
32.37249513, 32.30115037, 32.30115037, 32.35506708, 32.35506708,
32.35506708, 32.22876705, 32.22876705, 32.28006241, 32.28006241,
32.28006241, 32.16531796, 32.16531796, 32.21779251, 32.21779251,
32.21779251, 32.13354855, 32.13354855, 32.18199623, 32.18199623,
32.18199623, 32.04679917, 32.04679917, 32.08793194, 32.08793194,
32.08793194, 31.95865036, 31.95865036, 31.99349115, 31.99349115,
31.99349115, 31.89786652, 31.89786652, 31.92950005, 31.92950005,
31.92950005, 31.8230681, 31.8230681, 31.85623561, 31.85623561,
31.85623561, 31.746572, 31.746572, 31.78002994, 31.78002994,
31.78002994, 31.72857776, 31.72857776, 31.7725018, 31.7725018,
31.7725018, 32.4731662, 32.4731662, 32.53562016, 32.53562016,
32.53562016)), row.names = c(NA, 500L), class = "data.frame")
BLUF: anova_test requires factors in wid= and within=, and uses the values to form names, so the first in within= must not start with a number.
How to find this problem:
Look in the source for cbind and see that that package forms an expression by string concatenation (found in factorial_design). This is generally a bad idea when you don't control the data, and your issue here is a good example why. (I suggest you submit a bug report from this question.)
debugonce(factorial_design), then evaluate your call to anova_test. Since the string-forming expression in factorial_design is using wide.dv.name, we'll step through until that is defined.
Look at the value:
wide.dv.name
# [1] "100_1, 100_2, 1000_1, 1000_2, 1100_1, 1100_2, 1200_1, 1200_2, 1300_1, 1300_2, 1400_1, 1400_2, 1500_1, 1500_2, 1600_1, 1600_2, 1700_1, 1700_2, 1800_1, 1800_2, 1900_1, 1900_2, 200_1, 200_2, 2000_1, 2000_2, 300_1, 300_2, 400_1, 400_2, 500_1, 500_2, 600_1, 600_2, 700_1, 700_2, 800_1, 800_2, 900_1, 900_2"
lm_formula
# [1] "cbind(100_1, 100_2, 1000_1, 1000_2, 1100_1, 1100_2, 1200_1, 1200_2, 1300_1, 1300_2, 1400_1, 1400_2, 1500_1, 1500_2, 1600_1, 1600_2, 1700_1, 1700_2, 1800_1, 1800_2, 1900_1, 1900_2, 200_1, 200_2, 2000_1, 2000_2, 300_1, 300_2, 400_1, 400_2, 500_1, 500_2, 600_1, 600_2, 700_1, 700_2, 800_1, 800_2, 900_1, 900_2) ~ 1"
Okay, so that is clearly not good, R doesn't like variables starting with a number. These are formed from wide.
Looking at the problem values 100_1 and such, these are formed from buffer and Month, your arguments to within=. Reading the docs for anova_test, we see
within: (optional) within-subjects factor variables
while our buffer (and TimePeriod) are not factors. Since this problem is unlikely to happen if buffer (the leading portion of the strings formed) started with other than a number character, we'll focus on that. We can try to form a factor (not changing the contents), but I doubt it'll work since there does not appear to be any validation code in there that differentiates numbers from factors:
c1_l1$bufferf <- factor(c1_l1$buffer)
res.aov <- anova_test(
data = c1_l1, dv = value, wid = Month,
within = c(bufferf, TimePeriod),
effect.size = "ges",
detailed = TRUE,
)
# Error in str2lang(x) : <text>:1:10: unexpected input
# 1: cbind(100_
Okay, confirmed. Let's try prepending buffer with a letter. I'll fast-forward to make the following changes:
buffer: prepend a letter and convert to factorTimePeriod and Month: convert to factorsc1_l1$buffer_chr <- factor(paste0("b", c1_l1$buffer))
c1_l1$TimePeriod_fct <- factor(c1_l1$TimePeriod)
c1_l1$Month_fct <- factor(c1_l1$Month)
res.aov <- anova_test(
data = c1_l1, dv = value, wid = Month_fct,
within = c(buffer_chr, TimePeriod_fct),
effect.size = "ges",
detailed = TRUE,
)
# Error in eigen(SSPE, only.values = TRUE)$values >= sqrt(.Machine$double.eps) :
# invalid comparison with complex values
But frankly that is a completely different error, and may be resolved with more complete data or through other aspects of your analysis/exploration.