PlotLMContinuous: Plot modelled effects of continuous variables with...

In timnewbold/StatisticalModels: What the package does (short line)

Plot modelled effects of continuous variables with uncertainty

Description

Plots estimated effects on a specified response variable of one or two specified continuous variables, from a linear or generalized linear model. Where two continuous effects are specified, a perspective plot is used. If a factor is specified as 'byFactor', modelled effects of the continuous variable at each factor level are shown.

Usage

PlotLMContinuous(model,terms,se.fit=FALSE,seMultiplier=1.96,
                             xlim=NULL,ylim=NULL,zlim=NULL,
                             xlab=NULL,ylab=NULL,zlab=NULL,
                             params=list(),
                             byFactor=NULL,
                             yTransform=function(x){return(x)},
                             xTransform=function(x){return(x)},
                             log="",exclude.too.far=0.2)

Arguments

model	The model to derive parameter estimates from
terms	The continuous variables (up to a maximum of 2) to plot an a continuous axis
se.fit	Whether to show uncertainty
xlim	Specified x-axis limits
ylim	Specified y-axis limits
zlim	Specified z-axis limits (for perspective plots of 2 continuous effects)
xlab	The x-axis label
ylab	The y-axis label
zlab	The z-axis label (for perspective plots of 2 continuous effects)
params	A list of R graphical parameters, which will overwrite the defaults
byFactor	Plot separate lines for each level in this factor
yTransform	A function to use to back transform y-axis values
xTransform	A function to use to back transform x-axis values
log	Whether to log-transform both the x- and y-axis values "xy", one set of values "x" or "y", or neither "" (the default)
exclude.too.far	When plotting persepective plots based on 2 continuous variables, takes the 2-dimensional surface described by the two variables and excludes values that are too far from the data used to fit the model. See exclude.too.far function in the mgcv package

Author(s)

Tim Newbold <[e-mail]>

Examples

  # Load example data (site-level effects of land use on biodiversity from the PREDICTS database)
  data(PREDICTSSiteData)
  
  # Run a model of species richness as a function of land use, human population density
  # and distance to nearest road (with an interaction between human population density
  # and road distance)
  m1 <- GLMER(modelData = PREDICTSSites,responseVar = "Species_richness",fitFamily = "poisson",fixedStruct = "LandUse+poly(logHPD.rs,2)+poly(logDistRd.rs,2)+poly(logHPD.rs,2):poly(logDistRd.rs,2)",randomStruct = "(1|SS)+(1|SSB)+(1|SSBS)",REML = TRUE)
  
  # Plot the effect of human population density
  PlotGLMERContinuous(model = m1$model,data = m1$data,effects = "logHPD.rs",otherContEffects = "logDistRd.rs",otherFactors = list(LandUse="Primary Vegetation"),xlab = "Human Population Density",ylab = "Species richness",logLink = "e",plotRug = TRUE)
  
  # Plot a perspective plot of the effects on species richness of human population density
  # and distance to nearest road
  PlotGLMERContinuous(model = m1$model,data = m1$data,effects = c("logHPD.rs","logDistRd.rs"),otherFactors = list(LandUse="Primary Vegetation"),xlab = "Human population density",ylab = "Distance to road",zlab = "Species richness",logLink = "e",plotUncertainty = FALSE)
  
  # Run a model of species as a function of land use, human population density, and their interaction
  m1 <- GLMER(modelData = PREDICTSSites,responseVar = "Species_richness",fitFamily = "poisson",fixedStruct = "LandUse+poly(logHPD.rs,2)+LandUse:poly(logHPD.rs,2):",randomStruct = "(1|SS)+(1|SSB)+(1|SSBS)",REML = TRUE)
  
  # Plot the effect on species richness of the interaction between human population density and land use
  PlotGLMERContinuous(model = m1$model,data = m1$data,effects = "logHPD.rs",otherFactors = list(LandUse="Primary Vegetation"),xlab = "Human population density",ylab = "Species richness",byFactor = "LandUse",logLink = "e",line.cols=c("#E6AB02","#D95F02","#7570B3","#66A61E","#1B9E77","#E7298A"))
  legend(0.75,15,c("Primary","Secondary","Plantation","Cropland","Pasture","Urban"),
         col=c("#66A61E","#1B9E77","#7570B3","#E6AB02","#D95F02","#E7298A"),bty="n",lty=1)
  

timnewbold/StatisticalModels documentation built on Aug. 25, 2023, 4:58 p.m.