am trying to run my analysis on Rstudio but am getting the below error
Error in rma.mv(yi = filtered_data$interaction_var - 1, V = V_matrix, :
Length of 'yi' (184) and length/dimensions of 'V' (61) is not the same.
can someone help how to solve the error please
Hi. Welcome aboard.
We really need a lot more information about what you are doing.
We need to see your code and some sample data. See
FAQ Asking Questions
A handy way to supply some sample data is the dput() function. In the case of a large dataset something like dput(head(mydata, 100)) should supply the data we need. Just do dput(mydata) where mydata is your data. Copy the output and paste it here between
```
```
structure(list(PaperID = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), levels = 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",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46",
"47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57",
"58", "59", "60", "61", "62", "63", "64", "65", "66", "67"), class = "factor"),
study = c("Niziolek", "Niziolek", "Niziolek", "Niziolek",
"Niziolek", "Niziolek", "Zhang", "Zhang", "Zhang", "Zhang",
"Zhang", "Zhang", "Xie", "Xie", "Xie", "Xie", "Xie", "Xie",
"Xie", "Xie"), pub_year = c(2013L, 2013L, 2013L, 2013L, 2013L,
2013L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L), previous_meta = c("no",
"no", "no", "no", "no", "no", "no", "no", "no", "no", "no",
"no", "no", "no", "no", "no", "no", "no", "no", "no"), order = c("Coleoptera",
"Coleoptera", "Coleoptera", "Coleoptera", "Coleoptera", "Coleoptera",
"Lepidoptera", "Lepidoptera", "Lepidoptera", "Lepidoptera",
"Lepidoptera", "Lepidoptera", "Lepidoptera", "Lepidoptera",
"Lepidoptera", "Lepidoptera", "Lepidoptera", "Lepidoptera",
"Lepidoptera", "Lepidoptera"), family = c("Scarabaeidae",
"Scarabaeidae", "Scarabaeidae", "Scarabaeidae", "Scarabaeidae",
"Scarabaeidae", "Noctuidae", "Noctuidae", "Noctuidae", "Noctuidae",
"Noctuidae", "Noctuidae", "Crambidae", "Crambidae", "Crambidae",
"Crambidae", "Crambidae", "Crambidae", "Crambidae", "Crambidae"
), Species = c("Popillia japonica", "Popillia japonica",
"Popillia japonica", "Popillia japonica", "Popillia japonica",
"Popillia japonica", "Spodoptera litura", "Spodoptera litura",
"Spodoptera litura", "Spodoptera litura", "Spodoptera litura",
"Spodoptera litura", "Ostrinia furnacalis", "Ostrinia furnacalis",
"Ostrinia furnacalis", "Ostrinia furnacalis", "Ostrinia furnacalis",
"Ostrinia furnacalis", "Ostrinia furnacalis", "Ostrinia furnacalis"
), Native.Invasive = c("Invasive", "Invasive", "Invasive",
"Invasive", "Invasive", "Invasive", "Native", "Native", "Native",
"Native", "Native", "Native", "Common", "Common", "Common",
"Common", "Common", "Common", "Common", "Common"), trait = c("Consumption",
"Consumption", "Consumption", "Consumption", "Consumption",
"Consumption", "Consumption", "Consumption", "Consumption",
"growth rate", "growth rate", "growth rate", "Consumption",
"Consumption", "Consumption", "Consumption", "Consumption",
"Consumption", "Consumption", "Consumption"), herbivore_type = c("Chewer",
"Chewer", "Chewer", "Chewer", "Chewer", "Chewer", "Chewer",
"Chewer", "Chewer", "Chewer", "Chewer", "Chewer", "Chewer",
"Chewer", "Chewer", "Chewer", "Chewer", "Chewer", "Chewer",
"Chewer"), Host.plant = c("Acer palmatum", "Acer palmatum",
"Acer palmatum", "Acer palmatum", "Acer palmatum", "Acer palmatum",
"Ricinus communis", "Ricinus communis", "Ricinus communis",
"Ricinus communis", "Ricinus communis", "Ricinus communis",
"zea mays", "zea mays", "zea mays", "zea mays", "zea mays",
"zea mays", "zea mays", "zea mays"), host.plant.invasive.native = c("Invasive",
"Invasive", "Invasive", "Invasive", "Invasive", "Invasive",
"Invasive", "Invasive", "Invasive", "Invasive", "Invasive",
"Invasive", "Non native", "Non native", "Non native", "Non native",
"Non native", "Non native", "Non native", "Non native"),
plant.species = c("Glycine max", "Glycine max", "Glycine max",
"Glycine max", "Glycine max", "Glycine max", "Glycine max",
"Glycine max", "Glycine max", "Glycine max", "Glycine max",
"Glycine max", "Zea mays", "Zea mays", "Zea mays", "Zea mays",
"Zea mays", "Zea mays", "Zea mays", "Zea mays"), Family2 = c("Fabaceae",
"Fabaceae", "Fabaceae", "Fabaceae", "Fabaceae", "Fabaceae",
"Fabaceae", "Fabaceae", "Fabaceae", "Fabaceae", "Fabaceae",
"Fabaceae", "Poaceae", "Poaceae", "Poaceae", "Poaceae", "Poaceae",
"Poaceae", "Poaceae", "Poaceae"), diet = c("Leaf", "Leaf",
"Leaf", "Leaf", "Leaf", "Leaf", "Leaf", "Leaf", "Leaf", "Leaf",
"Leaf", "Leaf", "Artificial", "Artificial", "Artificial",
"Artificial", "Artificial", "Artificial", "Artificial", "Artificial"
), system_feature = c("Closed", "Closed", "Closed", "Closed",
"Closed", "Closed", "Closed", "Closed", "Closed", "Closed",
"Closed", "Closed", "Closed", "Closed", "Closed", "Closed",
"Closed", "Closed", "Closed", "Closed"), Native = c("Agricultural",
"Agricultural", "Agricultural", "Agricultural", "Agricultural",
"Agricultural", "Agricultural", "Agricultural", "Agricultural",
"Agricultural", "Agricultural", "Agricultural", "Agricultural",
"Agricultural", "Agricultural", "Agricultural", "Agricultural",
"Agricultural", "Agricultural", "Agricultural"), setting = c("field",
"field", "field", "field", "field", "field", "field", "field",
"field", "field", "field", "field", "growth chamber", "growth chamber",
"growth chamber", "growth chamber", "growth chamber", "growth chamber",
"growth chamber", "growth chamber"), exposure = c("Both",
"Both", "Both", "Both", "Both", "Both", "plant", "plant",
"plant", "plant", "plant", "plant", "Herbivore", "Herbivore",
"Herbivore", "Herbivore", "Herbivore", "Herbivore", "Herbivore",
"Herbivore"), latitude = c(40.0166667, 40.0166667, 40.0166667,
40.0166667, 40.0166667, 40.0166667, 37.633333, 37.633333,
37.633333, 37.633333, 37.633333, 37.633333, 40.016667, 40.016667,
40.016667, 40.016667, 40.016667, 40.016667, 40.016667, 40.016667
), longitude = c(-88.2333333, -88.2333333, -88.2333333, -88.2333333,
-88.2333333, -88.2333333, 116.85, 116.85, 116.85, 116.85,
116.85, 116.85, 116.266667, 116.266667, 116.266667, 116.266667,
116.266667, 116.266667, 116.266667, 116.266667), elevation = c(300,
300, 300, 300, 300, 300, 15, 15, 15, 15, 15, 15, 45, 45,
45, 45, 45, 45, 45, 45), Number_of_factors = c(2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), treatment = structure(c(20L, 3L, 9L, 20L, 3L, 9L,
3L, 20L, 9L, 3L, 20L, 9L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), levels = c("Behavioral preferences",
"climate", "CO2", "CO2_drought", "CO2_fertilizer", "CO2_flooding",
"CO2_light", "CO2_O3", "CO2_Temp", "core distribution", "Drought",
"Fertilizer", "flooding", "Increased_precipitation", "light",
"O3", "Ocean acidification", "Oviposition", "Season", "Temperature",
"Temperature_Drought", "Temperature_increased_precipitation",
"vernal_freeze"), class = "factor"), mean_control = c(5.21,
5.21, 5.21, 5.38, 5.38, 5.38, 534, 534, 534, 330.862423,
330.862423, 330.862423, 31.32061069, 32.72328244, 29.77480916,
32.1221374, 30.17557252, 34.18320611, 31.32061069, 32.72328244
), SE_control = c(1.66, 1.66, 1.66, 1.54, 1.54, 1.54, 18.55,
18.55, 18.55, 9.199178645, 9.199178645, 9.199178645, 1.335877863,
0.171755725, 0.591603053, 0.90648855, 0.534351145, 0.248091603,
1.335877863, 0.171755725), SD_control = c(20.99752366, 20.99752366,
20.99752366, 19.47963039, 19.47963039, 19.47963039, 321.2954248,
321.2954248, 321.2954248, 159.334448, 159.334448, 159.334448,
2.313808331, 0.297489642, 1.024686546, 1.570084225, 0.925523332,
0.429707261, 2.313808331, 0.297489642), n_control = c(160L,
160L, 160L, 160L, 160L, 160L, 300L, 300L, 300L, 300L, 300L,
300L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), mean_treatment = c(8.89,
2.54, 5.18, 7.72, 6.86, 6.12, 647.42, 560.82, 659.79, 294.9281314,
359.6106504, 284.5790554, 31.10114504, 32.91412214, 34.52671756,
33.15267176, 34.36450382, 33.22900763, 35.90076336, 34.63167939
), SE_treatment = c(2.11, 0.43, 1.86, 2.24, 1.97, 1.7, 18.55,
22.68, 20.62, 9.774921438, 10.06080751, 7.473538521, 0.467557252,
0.868320611, 2.213740458, 0.801526718, 2.356870229, 0.658396947,
0.486641221, 0.868320611), SD_treatment = c(26.68962345,
5.439117575, 23.52734579, 28.33400784, 24.91874796, 21.50348809,
321.2954248, 392.8291232, 357.1488765, 169.3066057, 174.2582978,
129.4454843, 0.809832916, 1.503975416, 3.834310948, 1.388284999,
4.082218983, 1.140376964, 0.84288732, 1.503975416), n_treatment = c(160L,
160L, 160L, 160L, 160L, 160L, 300L, 300L, 300L, 300L, 300L,
300L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), g_overallacross = c(0.5802,
-0.4847, NA, NA, NA, NA, 0.303741542, 0.056046099, NA, -0.348643795,
0.057808306, NA, 0.8767, NA, NA, NA, NA, NA, NA, NA), go_varacross = c(0.00316969,
0.00316969, NA, NA, NA, NA, 0.003342944, 0.003333661, NA,
0.003345995, 0.003333681, NA, 0.09492561, NA, NA, NA, NA,
NA, NA, NA), g_interaction = c(NA, NA, -0.024928825, NA,
NA, -0.064631602, NA, NA, -0.020665122, NA, NA, -0.122839844,
NA, NA, NA, NA, NA, NA, NA, NA), g_interact_var = c(NA, NA,
0.006250121, NA, NA, 0.006250816, NA, NA, 0.003333378, NA,
NA, 0.003334905, NA, NA, NA, NA, NA, NA, NA, NA)), row.names = c(NA,
20L), class = "data.frame")herbivore_data<-read.csv("C:/Users/Dr Invasive/Desktop/herbivory dat/herbivory data 2.csv")
herbivore_data$PaperID <-as.factor(herbivore_data$PaperID)
dput(head(herbivore_data, 20))
species_list=unique(herbivore_data $Species)
species_list =data.frame(species_list)
#To pull the phylogeny
#https://cran.r-project.org/web/packages/rotl/vignettes/data_mashups.html
species_list$species<-as.character(species_list$species)
sapply(species_list,class)
taxon_search <- tnrs_match_names(names =species_list$species, context_name = "All life")
knitr::kable(taxon_search)
species_list $ott_name <- unique_name(taxon_search)
species_list $ott_id <- taxon_search$ott_id
hits <- lapply(species_list $ott_id, studies_find_trees, property = "ot:ottId", detailed = FALSE)
sapply(hits, function(x) sum(x[["n_matched_trees"]]))
ott_in_tree <- ott_id(taxon_search)[is_in_tree(ott_id(taxon_search))]
tr <- tol_induced_subtree(ott_ids = ott_in_tree)
plot(tr)
tr$tip.label <- strip_ott_ids(tr$tip.label, remove_underscores = TRUE)
tr$tip.label %in% species_list $ott_name
## computes branch lengths (default method is "Grafen", but I don't even see alternatives in the help vignette so I did not specify anything)
tree2<-compute.brlen(tr)
#http://www.phytools.org/Cordoba2017/ex/2/Intro-to-phylogenies.html
tree2
plot(tree2,type="fan")
plot(tree2,no.margin=TRUE)
plot(tree2,no.margin=TRUE)
plot(tree2)
write.nexus(tree2, file = "your_tree.nexus")
tree2$edge.length
specieslist_tree2<-tree2 $tip
##Check if the species list from the dataset and the sigma file match each other
Species=unique(herbivore_data $Species)
Species =data.frame(Species)
Species <- Species[order(Species$Species),]
specieslist_tree2 =data.frame(specieslist_tree2)
specieslist_tree2 <- specieslist_tree2[order(specieslist_tree2$specieslist_tree2),]
all.equal(Species, specieslist_tree2)
identical(Species, specieslist_tree2)
# Elements in Species that are not in specieslist_tree2
diff1 <- setdiff(Species, specieslist_tree2)
# Elements in specieslist_tree2 that are not in Species
diff2 <- setdiff(specieslist_tree2, Species)
# Check if the vectors have the same elements (ignoring order)
are_equal <- setequal(Species, specieslist_tree2)
# Check the class of the vectors
class(Species)
class(specieslist_tree2)
# Check the length of the vectors
length(Species)
length(specieslist_tree2)
# Elements in Species that are not in specieslist_tree2
diff1 <- setdiff(Species, specieslist_tree2)
# Elements in specieslist_tree2 that are not in Species
diff2 <- setdiff(specieslist_tree2, Species)
# Check if the vectors have the same elements (ignoring order)
are_equal <- setequal(Species, specieslist_tree2)
##Check if the species list from the dataset and the sigma file match each other
Species=unique(herbivore_data $Species)
Species =data.frame(Species)
Species <- Species[order(Species$Species),]
specieslist_tree2 =data.frame(specieslist_tree2)
specieslist_tree2 <- specieslist_tree2[order(specieslist_tree2$specieslist_tree2),]
all.equal(Species, specieslist_tree2)
identical(Species, specieslist_tree2)
# Elements in Species that are different from specieslist_tree2
different_elements <- Species[Species != specieslist_tree2]
# Elements in specieslist_tree2 that are different from Species
different_elements2 <- specieslist_tree2[specieslist_tree2 != Species]
# Print the differing elements
print(different_elements)
print(different_elements2)
# Remove leading/trailing spaces and convert to lowercase
Species_cleaned <- tolower(trimws(Species))
specieslist_tree2_cleaned <- tolower(trimws(specieslist_tree2))
# Check if the cleaned vectors are identical
are_equal_cleaned <- identical(Species_cleaned, specieslist_tree2_cleaned)
# Check if the cleaned vectors are identical
are_equal_cleaned <- identical(Species_cleaned, specieslist_tree2_cleaned)
# Print the result
print(are_equal_cleaned)
# Identify the differing elements
differing_elements <- Species_cleaned[Species_cleaned != specieslist_tree2_cleaned]
# Print the differing elements
print(differing_elements)
# Check if the cleaned vectors are identical, ignoring case
are_equal_ignore_case <- identical(tolower(Species), tolower(specieslist_tree2))
# Print the result
print(are_equal_ignore_case)
# Find elements in Species_cleaned that are not in specieslist_tree2_cleaned
diff1 <- setdiff(Species_cleaned, specieslist_tree2_cleaned)
# Find elements in specieslist_tree2_cleaned that are not in Species_cleaned
diff2 <- setdiff(specieslist_tree2_cleaned, Species_cleaned)
# Print the differing elements
print(diff1)
print(diff2)
# Remove additional text in parentheses and leading/trailing spaces
Species_cleaned <- gsub("\\s*\\(.*?\\)\\s*", "", Species_cleaned)
# Check if the cleaned vectors are identical
are_equal_cleaned <- identical(Species_cleaned, specieslist_tree2_cleaned)
# Print the result
print(are_equal_cleaned)
# Identify the differing elements
differing_elements <- Species_cleaned[Species_cleaned != specieslist_tree2_cleaned]
# Print the differing elements
print(differing_elements)
your_data <- read.csv("C:/Users/Dr Invasive/Desktop/herbivory dat/herbivory data 2.csv")
Species <- read.csv("C:/Users/Dr Invasive/Desktop/herbivory dat/herbivory data 2.csv")
specieslist_tree2 <- read.csv("C:/Users/Dr Invasive/Desktop/herbivory dat/herbivory data 2.csv")
class(Species)
class(specieslist_tree2)
# Check the number of rows in both data frames
nrow(Species)
nrow(specieslist_tree2)
# Check the column names of both data frames
colnames(Species)
colnames(specieslist_tree2)
file_path <- file.path("C:/Users/Dr Invasive/Desktop/herbivory dat/herbivory data 2.csv")
file_path <- "C:/Users/Dr Invasive/Desktop/herbivory dat/herbivory data 2.csv"
if (!file.exists(file_path)) {
cat("The file does not exist at the specified path.\n")
}
# Check the current working directory
getwd()
# Set the working directory to the desired location
setwd("C:/Users/Dr Invasive/Desktop/herbivory dat/")
# generate the variance-covariance matrix and write out the file
sigma<-vcv.phylo(tree2, cor=TRUE)
write.table(sigma, file="C:/Users/Dr Invasive/Desktop/herbivory dat/herbivory data 2.csv")
# Generate the variance-covariance matrix
sigma <- vcv.phylo(tree2, cor=TRUE)
# Print the variance-covariance matrix
print(sigma)
colnames(herbivore_data)
plot(herbivore_data$g_overallacross, herbivore_data$g_overallacross)
# Create the scatterplot
plot(herbivore_data$g_overallacross, herbivore_data$go_varacross,
xlab = "g_overallacross", ylab = "go_varacross", main = "Scatterplot of g_overallacross vs. go_varacross")
# Add labels for data points
text(herbivore_data$g_overallacross, herbivore_data$go_varacross, labels = herbivore_data$Species, pos = 3, cex = 0.7)
plot(herbivore_data$g_overallacross~herbivore_data$go_varacross)
#Excluding treatments that are not relevant to contemporary climate change
herbivore_data$treatment <- factor(herbivore_data$treatment)
str(herbivore_data)
herb_data<-herbivore_data[!(herbivore_data$treatment %in% c("CO2_flooding", "CO2_light", "CO2_O3","flooding", "Increased_precipitation", "light", "O3", "Temperature_increased_precipitation")), ]
herb_data $treatment <- factor(herb_data $treatment)
levels(herb_data$treatment)
str(herb_data)
##Hedge's g, overall and aggregated
data_nona<-subset(herb_data, g_overallacross!="NA")
data_nona $treatment <- factor(data_nona $treatment)
data_nona $trait <- factor(data_nona $trait)
data_nona $exposure <- factor(data_nona $exposure)
## standardize variables
data_nona $elev<-scale(data_nona $elevation,center=TRUE, scale=TRUE)
data_nona $lat<-scale(data_nona $latitude,center=TRUE, scale=TRUE)
data_nona $long<-scale(data_nona $longitude,center=TRUE, scale=TRUE)
data_nona $year<-scale(data_nona $pub_year,center=TRUE, scale=TRUE)
# Convert the 'treatment' and 'trait' variables to numeric
data_nona$your_variable_treatment <- as.numeric(data_nona$treatment)
data_nona$your_variable_trait <- as.numeric(data_nona$trait)
# Check the structure and summary of data_nona before conversion
str(data_nona)
summary(data_nona)
# Convert variables to numeric
data_nona$your_variable_treatment <- as.numeric(data_nona$treatment)
data_nona$your_variable_trait <- as.numeric(data_nona$trait)
# Check the structure and summary of data_nona after conversion
str(data_nona)
summary(data_nona)
# Check data types
cat("Data types:\n")
cat("your_variable_treatment: ", class(data_nona$your_variable_treatment), "\n")
cat("your_variable_trait: ", class(data_nona$your_variable_trait), "\n")
# Create a new variable representing the interaction
data_nona$interaction_var <- data_nona$your_variable_treatment * data_nona$your_variable_trait
# Calculate the V matrix
V_matrix <- vcv(tree2)
# Find the species in your dataset that are also in the phylogenetic tree
matching_species <- intersect(data_nona$Species, tree2$tip.label)
# Create a new dataset with only the matching species
filtered_data <- data_nona[data_nona$Species %in% matching_species, ]
# Verify that the filtered dataset contains only the matching species
unique_species_in_filtered_data <- unique(filtered_data$Species)
print(unique_species_in_filtered_data)
# Check for missing values
if (any(is.na(filtered_data$interaction_var))) {
cat("There are missing values in the interaction variable.\n")
} else {
# Specify the formula using the new interaction variable and provide the V matrix
final_mod <- rma.mv(yi = filtered_data$interaction_var - 1,
V = V_matrix,
random = list(~1 | PaperID),
data = filtered_data,
struct = "UN",
method = "REML")
cat("Analysis completed successfully.\n")
}
class(g_overallacross)
# Print the structure of the final_mod model object
str(final_mod)rma.mv expects all your data (i.e. both the yi part and the V part ) to be within what is passed to its data parameter.
in your case you pass filtered_data as the data.
Therefore you need to have filtered_data contain your yi as you want it, and your V as you want it, and in the yi and V paramaters to the rma.mv function you are simply naming which parts of filtered_data are to be used for the yi purpose and which variable to use for the V.
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