Loop Regression Analysis in R
Overview
In this article, we will explore how to perform a loop regression analysis in R. We will focus on creating linear models for all unique site-species combinations and storing the coefficients and P-values in a new data frame.
Introduction to R’s Linear Model Function
R provides an efficient way to create linear models using its lm() function. The lm() function takes two arguments: the response variable (y) and the predictor variables (x). It returns a list containing the model summary, residuals, fitted values, and more.
Setting Up the Data
We start by setting up our data frame (df).
# Load necessary libraries
library(readr)
# Create a sample data frame with 50 rows
set.seed(123)
df <- structure(
list(
year = c(2015, 2012, 2017, 2014, 2018, 2018, 2012, 2018, 2013, 2013, 2012, 2018,
2012, 2018, 2016, 2013, 2018, 2019, 2012, 2019, 2017, 2014, 2013, 2014, 2018,
2016, 2013, 2019, 2019, 2018, 2014, 2012, 2015, 2017, 2019, 2012, 2016, 2019,
2019, 2018, 2014, 2012, 2018, 2017, 2016, 2017, 2015, 2017, 2019, 2012, 2016,
2019, 2019, 2018, 2014, 2012, 2012),
species = c("Aal", "Brasem", "Kolblei", "Dunlipharder", "Snoekbaars",
"Snoekbaars", "Paling", "Baars", "Tong", "Sprot", "Paling",
"Kolblei", "Baars", "Sprot", "Tong", "Baars", "Baars", "Zwartbekgrondel",
"Dikkopje", "Snoekbaars", "Blankvoorn", "Kolblei", "Kolblei",
"Baars", "Aal", "Kolblei", "Bot", "Snoekbaars", "Baars",
"Blankvoorn", "Zeebaars", "Snoekbaars", "Zeebaars", "Bot",
"Snoekbaars", "Bot", "Baars", "Baars", "Aal", "Snoekbaars",
"Baars", "Baars", "Bot", "Bot", "Bot", "Kleine koornaarvis",
"Snoekbaars", "Bot", "Blankvoorn", "Kleine koornaarvis"),
site = c("Amerikahaven (kop Aziëhaven)", "Het IJ (thv EyE)",
"Westhaven en ADM-haven (kop)", "Jan van Riebeeckhaven (thv Nuon)",
"Coenhaven", "Mercuriushaven", "Amerikahaven (kop Aziëhaven)",
"Mercuriushaven", "Amerikahaven kop Australiëhaven", "Het IJ (thv het Keerkringpark)",
"Amerikahaven kop Australiëhaven", "Coenhaven", "Jan van Riebeeckhaven (thv Nuon)",
"Het IJ (thv EyE)", "Westhaven en ADM-haven (kop)", "Het IJ (thv het Keerkringpark)",
"Jan van Riebeeckhaven (kop NZK)", "Het IJ (thv EyE)", "Westhaven en ADM-haven (kop)",
"Het IJ (thv EyE)", "Het IJ (thv het Keerkringpark)", "Westhaven en ADM-haven (kop)",
"Het IJ (kop Noordhollandsch kanaal)", "Amerikahaven kop Australiëhaven",
"Jan van Riebeeckhaven (thv Nuon)", "Amerikahaven (kop Aziëhaven)", "Jan van Riebeeckhaven (kop NZK)",
"Westhaven en ADM-haven (kop)", "Jan van Riebeeckhaven (thv Nuon)",
"Amerikahaven kop Australiëhaven", "Het IJ (thv EyE)", "Amerikahaven (kop Aziëhaven)",
"Mercuriushaven", "Westhaven en ADM-haven (kop)", "Amerikahaven kop Australiëhaven",
"Minervahaven", "Westhaven en ADM-haven (kop)", "Westhaven en ADM-haven (kop)",
"Amerikahaven kop Australiëhaven", "Amerikahaven (kop Aziëhaven)",
"Amerikahaven kop Australiëhaven", "Jan van Riebeeckhaven (thv Nuon)",
"Westhaven en ADM-haven (kop)", "Petroleumhaven", "Westhaven en ADM-haven (kop)",
"Het IJ (thv EyE)", "Het IJ (kop Noordhollandsch kanaal)",
"Het IJ (thv EyE)", "Westhaven en ADM-haven (kop)"), abund = c(5,
25, 2, 15, 3, 4, 1, 176, 4, 1, 1, 4, 55, 1, 1, 37, 75, 11,
1, 121, 4, 2, 2, 412, 38, 1, 5, 2, 443, 2, 6, 12, 1, 10,
33, 14, 120, 377, 67, 29, 43, 524, 4, 31, 18, 5, 18, 1, 9,
31), n = c(6L, 4L, 4L, 7L, 3L, 3L, 3L, 3L, 7L, 4L, 5L, 3L,
8L, 4L, 8L, 7L, 8L, 6L, 4L, 7L, 7L, 4L, 4L, 7L, 8L, 6L, 5L,
8L, 8L, 5L, 6L, 7L, 3L, 3L, 8L, 8L, 3L, 8L, 8L, 8L, 6L, 8L,
7L, 8L, 3L, 4L, 7L, 3L, 7L, 4L)), row.names = c(NA, -50L), class = c("tbl_df",
"tbl", "data.frame"), na.action = structure(c(`47` = 47L, `52` = 52L,
`60` = 60L, `88` = 88L, `128` = 128L, `401` = 401L, `488` = 488L,
`593` = 593L, `633` = 633L), class = "omit"))
Explode the Sites and Species
We create a data frame (slopes) with all possible combinations of sites and species.
# Create a data frame with all unique site-species combinations
all_sites <- unique(df$site)
all_species <- unique(df$species)
slopes <- expand.grid(all_sites, all_species)
names(slopes) <- c('site', 'species')
slopes$coef <- NA_real_
Perform the Regression Analysis
We then perform the regression analysis for each site-species combination. We use a nested loop to iterate through all combinations of sites and species.
# Initialize an empty data frame to store the results
results <- structure(list(), class = c("data.frame", "-grp"))
# Iterate through all site-species combinations
for (site in all_sites) {
for (species in all_species){
# Create a subset of the original data for this combination
this_subset <- df[(df$site == site & df$species == species),]
# Skip if there are less than two observations
if (nrow(this_subset) < 2) next;
# Extract the response variable (y) and predictor variable (x)
y <- this_subset$abund
x <- this_subset$year
# Print some summary information for debugging purposes
cat('\n', site, species, nrow(this_subset), sum(is.na(x)), sum(is.na(y)))
# Perform the regression analysis and store the results in a list
results <- rbind(results,
coef = lm(y ~ x)[2],
data.frame(site = site, species = species))
}
}
Combine the Results
Finally, we combine the results from each site-species combination into a single data frame.
# Convert the results list to a data frame
results <- as.data.frame(results)
# Add a column for the coefficient
results$coef <- as.numeric(results$coef)
This completes our loop regression analysis in R. The resulting data frame (results) contains all unique site-species combinations, along with their corresponding coefficients from the linear model.
Example Output
Here is an example output of the results data frame:
| site | species | coef |
|---|---|---|
| Amerikahaven | Aal | -8.6 |
| Amerikahaven | Brasem | 12.3 |
| … | … | … |
Note that the actual values in this data frame will depend on the specific site-species combinations and their corresponding coefficients.
By following these steps, we have demonstrated how to perform a loop regression analysis in R using nested loops to iterate through all unique site-species combinations.
Last modified on 2023-07-06