My supervisor told me to run the brms package to model my data using the brm() function. When I tried to do that, I had to install Rtools43. After that, when I tried to run the code the following error occured:
Error in compileCode(f, code, language = language, verbose = verbose) :
C:\rtools43\x86_64-w64-mingw32.static.posix\bin/ld.exe: file14b04a5d5e69.o:file14b04a5d5e69.cpp:(.text$_ZN3tbb8internal26task_scheduler_observer_v3D0Ev[_ZN3tbb8internal26task_scheduler_observer_v3D0Ev]+0x1c): undefined reference to `tbb::internal::task_scheduler_observer_v3::observe(bool)'C:\rtools43\x86_64-w64-mingw32.static.posix\bin/ld.exe: file14b04a5d5e69.o:file14b04a5d5e69.cpp:(.text$_ZN3tbb10interface623task_scheduler_observerD1Ev[_ZN3tbb10interface623task_scheduler_observerD1Ev]+0x1c): undefined reference to `tbb::internal::task_scheduler_observer_v3::observe(bool)'C:\rtools43\x86_64-w64-mingw32.static.posix\bin/ld.exe: file14b04a5d5e69.o:file14b04a5d5e69.cpp:(.text$_ZN3tbb10interface623task_scheduler_observerD1Ev[_ZN3tbb10interface623task_scheduler_observerD1Ev]+0x37): undefined reference to `tbb::internal::task_scheduler_observer_v3::observe(bool)'C:\rtools43\x86_64-w64-mingw32.static.posix\bin/ld.exe: file14b04a5d5e69.o:file14b04a5d5e69.cpp:(.text$_ZN3tbb10interface
In addition: Warning message:
Rows containing NAs were excluded from the model.
Error in sink(type = "output") : invalid connection
This is my code:
pacman::p_load(tidyverse, readxl, janitor, emmeans, multcomp, magrittr,
parameters, effectsize, multcompView, see, performance,
conflicted, ggpubr, rstatix, nortest)
library(rstatix)
conflict_prefer("select", "dplyr")
conflict_prefer("filter", "dplyr")
conflict_prefer("summarise", "dplyr")
conflict_prefer("extract", "magrittr")
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") ## Color blind friendly palette
##--------------------------------------------------------------------------------------------------------------------------
## Funktion um Excel Datei mit mehreren Sheets zu öffnen und eines davon auszuwählen
library(readxl)
read_excel_allsheets <- function(filename) {
sheets <- readxl::excel_sheets(filename)
x <- lapply(sheets, function(x) readxl::read_excel(filename, sheet = x))
return(x)
}
big_tbl <- read_excel_allsheets ("Mesocosms_R.xlsx")
big_tbl
phyto_plankton_tbl<- big_tbl[[14]]
##--------------------------------------------------------------------------------------------------------------------------
## Data transformation
phyto_plankton_tbl %>%
mutate( ## Mutieren hier block und trt von chr zu factor
block = as.factor(block),
trt = as.factor(trt))
phyto_plankton_tbl <- phyto_plankton_tbl %>%
gather(key = "time", value = "PelaChl", t0, t1, t2, t3, t4, t5) %>% ## Ändert Tabelle aus width format into long format
convert_as_factor(trt, time)
print(phyto_plankton_tbl, n = 40)
##--------------------------------------------------------------------------------------------------------------------------
## Visualization
pelaChl_bxp <- ggplot(data = phyto_plankton_tbl, aes(x= time, y = PelaChl, fill = trt)) +
geom_boxplot() +
ylim(0, NA) +
scale_fill_manual(values=cbPalette) + ## Adds color blind firendly palette
labs(x = "Week", y = "Pelagic Chlorophyll [µg/L]",
fill = "Treatment") +
## geom_jitter() +
theme_bw()
pelaChl_bxp
pelaChl_point <- ggplot(data = phyto_plankton_tbl, aes(x= time, y = PelaChl, color = trt, group = trt)) +
## geom_line()
## geom_jitter() +
scale_color_manual(values=cbPalette) + ## Adds color blind firendly palette
labs(x = "Week", y = "Total pelagic chlorophyll [µg/L]",
color = "Treatment") +
geom_smooth(method='loess', se = FALSE) + ## Method loess nutzt eine polynomiale regression
theme_bw()
pelaChl_point
##--------------------------------------------------------------------------------------------------------------------------
## Test ANOVA Model assumptions
phyto_plankton_tbl %>%
group_by(time, trt) %>%
identify_outliers(PelaChl) ## is.extreme checks outliers assumption. Must be FALSE for everyone
## ==> Zeigt extreme outliers
ggqqplot(phyto_plankton_tbl, "PelaChl", facet.by = "time")
## ==> sieht einigermaßen normally distributed aus
## Classic ANOVA
PelaChl_anova <- aov(PelaChl ~ trt, data = phyto_plankton_tbl)
summary(PelaChl_anova)
library(brms)
##conflicts_prefer(dplyr::add_rownames)
##conflicts_prefer(stats::chisq.test)
##conflicts_prefer(rstatix::cohens_d)
##conflicts_prefer(rstatix::eta_squared)
##conflicts_prefer(stats::fisher.test)
##conflicts_prefer(TH.data::geyser)
##conflicts_prefer(brms::kidney)
##conflicts_prefer(dplyr::lag)
##conflicts_prefer(rstatix::make_clean_names)
##conflicts_prefer(magrittr::set_names)
##conflicts_prefer(effectsize::standardize)
chloro_mod <- brm(log(PelaChl) ~ trt * time + (1|block), data = phyto_plankton_tbl, family = gaussian())
summary(chloro_mod)
I
I searched in different forums and tried to connect to the tbb library, but only found a code for C to do so. I tried reinstalling the Rtools but when i run:
install.packages("Rcpp")
Rcpp::evalCpp("2+2")
it works and puts out 4. So Rtools is working right?
Data:
structure(list(tank = c(1, 2, 3, 5, 7, 8, 9, 10, 11, 13, 14,
16, 17, 18, 20, 22, 23, 24, 1, 2, 3, 5, 7, 8, 9, 10, 11, 13,
14, 16, 17, 18, 20, 22, 23, 24, 1, 2, 3, 5, 7, 8, 9, 10, 11,
13, 14, 16, 17, 18, 20, 22, 23, 24, 1, 2, 3, 5, 7, 8, 9, 10,
11, 13, 14, 16, 17, 18, 20, 22, 23, 24, 1, 2, 3, 5, 7, 8, 9,
10, 11, 13, 14, 16, 17, 18, 20, 22, 23, 24, 1, 2, 3, 5, 7, 8,
9, 10, 11, 13, 14, 16, 17, 18, 20, 22, 23, 24), block = c(1,
1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 1, 1, 1, 2,
2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 1, 1, 1, 2, 2, 2, 3,
3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4,
4, 4, 5, 5, 5, 6, 6, 6, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5,
5, 5, 6, 6, 6, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6,
6, 6), trt = structure(c(4L, 1L, 3L, 4L, 1L, 3L, 3L, 1L, 4L,
3L, 1L, 4L, 1L, 4L, 3L, 4L, 3L, 1L, 4L, 1L, 3L, 4L, 1L, 3L, 3L,
1L, 4L, 3L, 1L, 4L, 1L, 4L, 3L, 4L, 3L, 1L, 4L, 1L, 3L, 4L, 1L,
3L, 3L, 1L, 4L, 3L, 1L, 4L, 1L, 4L, 3L, 4L, 3L, 1L, 4L, 1L, 3L,
4L, 1L, 3L, 3L, 1L, 4L, 3L, 1L, 4L, 1L, 4L, 3L, 4L, 3L, 1L, 4L,
1L, 3L, 4L, 1L, 3L, 3L, 1L, 4L, 3L, 1L, 4L, 1L, 4L, 3L, 4L, 3L,
1L, 4L, 1L, 3L, 4L, 1L, 3L, 3L, 1L, 4L, 3L, 1L, 4L, 1L, 4L, 3L,
4L, 3L, 1L), levels = c("Control", "LV", "P-/F+", "P+/F+"), class = "factor"),
time = structure(c(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, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), levels = c("t0", "t1", "t2",
"t3", "t4", "t5"), class = "factor"), PelaChl = c(NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 4.4, 0.8, 3.6, 2.2, 3.5, 2.1, 1.6, 1.3, 6.7, 3.2, 1.3,
1.3, 1.9, 2.6, 0.6, 5, 1.7, 1.1, 5.2, 1.5, 3.5, 6, 2.3, 1.7,
2.2, 1.2, 3.1, 1.9, 1.7, 1.1, 6.8, 1.6, 0.8, 4.9, 6.5, 1.2,
5.6, 3.4, 4.1, 15.6, 3.8, 2.9, 3.5, 2.3, 5.3, 6.4, 1.7, 1.9,
5.2, 3.3, 2.8, 1.7, 13.6, 0.9, 8.5, 5.9, 5.3, 13.1, 7.2,
4.5, 7.9, 2.8, 11.4, 9.8, 1.9, 11.9, 34.6, 25.3, 6.1, 3.7,
11, 3, 33, 8.4, 21.4, 21.1, 20.6, 12.4, 19.2, 2.3, 23, 22.4,
6.7, 34.8, 18.1, 9.5, 20.1, 117.9, 24.6, 22.5)), row.names = c(NA,
-108L), class = c("tbl_df", "tbl", "data.frame"))
> phyto_plankton_tbl<- big_tbl[[14]]
> dput(phyto_plankton_tbl)
structure(list(tank = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24), block = c(1,
1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6,
6, 6), trt = c("P+/F+", "Control", "P-/F+", "LV", "P+/F+", "LV",
"Control", "P-/F+", "P-/F+", "Control", "P+/F+", "LV", "P-/F+",
"Control", "LV", "P+/F+", "Control", "P+/F+", "LV", "P-/F+",
"LV", "P+/F+", "P-/F+", "Control"), t0 = c(NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA), t1 = c(4.4, 0.8, 3.6, 1.6, 2.2, 3.1, 3.5, 2.1, 1.6,
1.3, 6.7, 2.7, 3.2, 1.3, 0.9, 1.3, 1.9, 2.6, 1.2, 0.6, 3.6, 5,
1.7, 1.1), t2 = c(5.2, 1.5, 3.5, 1.9, 6, 6.1, 2.3, 1.7, 2.2,
1.2, 3.1, 3.7, 1.9, 1.7, 0.9, 1.1, 6.8, 1.6, 2.2, 0.8, 7.3, 4.9,
6.5, 1.2), t3 = c(5.6, 3.4, 4.1, 6, 15.6, 2.6, 3.8, 2.9, 3.5,
2.3, 5.3, 2.1, 6.4, 1.7, 2.2, 1.9, 5.2, 3.3, 2.3, 2.8, 3.5, 1.7,
13.6, 0.9), t4 = c(8.5, 5.9, 5.3, 7.2, 13.1, 3.7, 7.2, 4.5, 7.9,
2.8, 11.4, 2.1, 9.8, 1.9, 5, 11.9, 34.6, 25.3, 4, 6.1, 6.8, 3.7,
11, 3), t5 = c(33, 8.4, 21.4, 6.5, 21.1, 18.5, 20.6, 12.4, 19.2,
2.3, 23, 10.5, 22.4, 6.7, 8.8, 34.8, 18.1, 9.5, 8, 20.1, 4.2,
117.9, 24.6, 22.5)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-24L))