sim5 {DImodels} R Documentation

## The Simulated "sim5" Dataset

### Description

The sim5 dataset was simulated. There are nine species that vary in proportions (p1 - p9). It is assumed that species 1 to 5 come from functional group 1, species 6 and 7 from functional group 2 and species 8 and 9 from functional group 3. The response was simulated assuming that there were species identity effects and functional group specific interaction effects, with theta = 0.7.

### Usage

data(sim5)

### Format

A data frame with 206 observations on the following 12 variables:

community

A numeric vector identifying each unique community, i.e., two rows with the same community value also share the same set of p1 to p9 values.

richness

A numeric vector identifying the number of species in the initial composition.

p1

A numeric vector indicating the initial proportion of species 1.

p2

A numeric vector indicating the initial proportion of species 2.

p3

A numeric vector indicating the initial proportion of species 3.

p4

A numeric vector indicating the initial proportion of species 4.

p5

A numeric vector indicating the initial proportion of species 5.

p6

A numeric vector indicating the initial proportion of species 6.

p7

A numeric vector indicating the initial proportion of species 7.

p8

A numeric vector indicating the initial proportion of species 8.

p9

A numeric vector indicating the initial proportion of species 9.

response

A numeric vector giving the simulated response variable.

### Details

What are Diversity-Interactions (DI) models?

Diversity-Interactions (DI) models (Kirwan et al 2009) are a set of tools for analysing and interpreting data from experiments that explore the effects of species diversity on community-level responses. We strongly recommend that users read the short introduction to Diversity-Interactions models (available at: DImodels). Further information on Diversity-Interactions models is also available in Kirwan et al 2009 and Connolly et al 2013.

Parameter values for the simulation

DI models take the general form of:

y = Identities + Interactions + Structures + \epsilon

where y is a community-level response, the Identities are the effects of species identities and enter the model as individual species proportions at the beginning of the time period, the Interactions are the interactions among the species proportions, while Structures include other experimental structures such as blocks, treatments or density.

The dataset sim5 was simulated with:

• identity effects for the nine species with values = 10, 9, 8, 7, 11, 6, 5, 8, 9

• functional group specific interaction effects; assume functional groups are labelled FG1, FG2 and FG3, then the interaction parameter values are: between FG1 and FG2 = 8, between FG1 and FG3 = 3, between FG2 and FG3 = 6, within FG1 = 6, within FG2 = 4 and within FG3 = 5

• theta = 0.7 (where \theta is a non-linear parameter included as a power on each pipj product within interaction variables, see Connolly et al 2013 for details)

• \epsilon assumed normally distributed with mean 0 and standard deviation 1.2.

### References

Connolly J, T Bell, T Bolger, C Brophy, T Carnus, JA Finn, L Kirwan, F Isbell, J Levine, A Lüscher, V Picasso, C Roscher, MT Sebastia, M Suter and A Weigelt (2013) An improved model to predict the effects of changing biodiversity levels on ecosystem function. Journal of Ecology, 101, 344-355.

Kirwan L, J Connolly, JA Finn, C Brophy, A Lüscher, D Nyfeler and MT Sebastia (2009) Diversity-interaction modelling - estimating contributions of species identities and interactions to ecosystem function. Ecology, 90, 2032-2038.

### Examples


####################################
## Code to simulate the sim5 dataset

## Simulate dataset sim5 with 9 species and three functional groups.
## The species 1-5 are FG1, species 6-7 are FG2 and species 8-9 are FG3.
## Assume ID effects and the FG interactions model, with theta = 0.7.

## Set up proportions
data("design_a")
sim5 <- design_a

## Create the functional group interaction variables, with theta = 0.7.
FG_matrix <- DI_data(prop = 3:11, FG = c("FG1","FG1","FG1","FG1","FG1","FG2","FG2","FG3","FG3"),
data = sim5, theta = 0.7, what = "FG")
sim5 <- data.frame(sim5, FG_matrix)
names(sim5)[12:17] <- paste0(names(sim5)[12:17], "_theta")

## To simulate the response, first create a matrix of predictors that includes p1-p9, the
##  treatment and the interaction variables.
X <- model.matrix(~ p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9
+ bfg_FG1_FG2_theta + bfg_FG1_FG3_theta + bfg_FG2_FG3_theta
+ wfg_FG1_theta + wfg_FG2_theta + wfg_FG3_theta -1, data = sim5)

## Create a vector of 'known' parameter values for simulating the response.
## The first nine are the p1-p9 parameters, and the second set of six are the interaction
##  parameters.
sim5_coeff <- c(10,9,8,7,11, 6,5, 8,9,     8,3,6, 6,4,5)

##Create response and add normally distributed error
sim5$response <- as.numeric(X %*% sim5_coeff) set.seed(35748) r <- rnorm(n = 206, mean = 0, sd = 1.2) sim5$response <- round(sim5$response + r, digits = 3) sim5[,12:17] <- NULL ########################### ## Analyse the sim5 dataset ## Load the sim5 data data(sim5) ## View the first few entries head(sim5) ## Explore the variables in sim5 str(sim5) ## Check characteristics of sim5 hist(sim5$response)
summary(sim5$response) plot(sim5$richness, sim5$response) plot(sim5$p1, sim5$response) plot(sim5$p2, sim5$response) plot(sim5$p3, sim5$response) plot(sim5$p4, sim5$response) plot(sim5$p5, sim5$response) plot(sim5$p6, sim5$response) plot(sim5$p7, sim5$response) plot(sim5$p8, sim5$response) plot(sim5$p9, sim5$response) ## What model fits best? Selection using F-test in autoDI auto1 <- autoDI(y = "response", prop = 3:11, FG = c("FG1","FG1","FG1","FG1","FG1","FG2","FG2","FG3","FG3"), data = sim5, selection = "Ftest") summary(auto1) ## Fit the functional group model, with theta, using DI and the FG tag m1 <- DI(y = "response", prop = 3:11, FG = c("FG1","FG1","FG1","FG1","FG1","FG2","FG2","FG3","FG3"), DImodel = "FG", estimate_theta = TRUE, data = sim5) summary(m1) CI_95 <- theta_CI(m1, conf = .95) CI_95 plot(m1) ## Check goodness-of-fit using a half-normal plot with a simulated envelope library(hnp) hnp(m1) ## Graph the profile likelihood library(ggplot2) ggplot(m1$profile_loglik, aes(x = grid, y = prof)) +
theme_bw() +
geom_line() +
xlim(0,1.5) +
xlab(expression(theta)) +
ylab("Log-likelihood") +
geom_vline(xintercept = CI_95, lty = 3) +
labs(title = "   Log-likelihood versus theta",
caption = "dotted vertical lines are upper and lower bounds of 95% CI for theta")

## Fit the functional group model, with theta set equal to the estimate from m1, and custom_formula.
## Note, it is not possible to estimate theta with custom_formula (only select a 'known' value).
## First, create the functional group interactions (theta value as estimated from m1),
##  store them in a new dataset and rename them with a theta indicator.
FG_matrix <- DI_data(prop = 3:11, FG=c("FG1","FG1","FG1","FG1","FG1","FG2","FG2","FG3","FG3"),
theta = 0.7296887, data = sim5, what = "FG")
sim5new <- data.frame(sim5, FG_matrix)
names(sim5new)[13:18] <- paste0(names(sim5new)[13:18], "_theta")
m2 <- DI(custom_formula = response ~ 0 + p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9 +
bfg_FG1_FG2_theta + bfg_FG1_FG3_theta + bfg_FG2_FG3_theta
+ wfg_FG1_theta + wfg_FG2_theta + wfg_FG3_theta, data = sim5new)
## This will adjust the standard errors in m2 for the 'estimation' of theta
m2$df.residual <- m2$df.residual - 1
## This will adjust the AIC in m2 for the 'estimation' of theta
m2$aic <- m2$aic + 2
summary(m2)



[Package DImodels version 1.1 Index]