Fsiland is used to fit spatial influence of landscape.
Fsiland(formula,land,data,family ="gaussian",sif="exponential", init = 100,
border=F,wd=50)a symbolic description (see lm() or glm()) of the response variable concerning local variables. Random effects are also allowed according to the syntax in package lme4 (see lmer() function in package lme4).
an object of class sf that gives the landscape variables for the model
a dataframe containing the response variable and the local variables.
the distribution of response variable. family can be "gaussian", "poisson" or "binomial" and the associated link function are identity, log and logit respectively.
the family of the spatial influence function. sif can be "exponential" or "gaussian".
a vector indicating the starting values for the estimation of the mean distance of the spatial influence functions. If one value is given, parameter for each spatial influnce function are initialised with this value. By default, initialisation is equal to 100 for each landscape variable.
a logical indicating if pixels into the field (polygon) where obervations are located have to be take into account to evaluate the influences. If border=T (by default), all pixels are take into accpount. If Border=F, only pixels outside the fiels (polygon) are condidered to evaluate influnce for landscape variables.
a number indiacting the size of pixels for raster discretisazion.
Fsiland returns an object of type list.
vector of estimated coefficients
vector of estimated coefficients
an object of class formula that indicates the local model used
a dataframe of estimated contributions of each spatial variable (in column) to each observation (in row). The number of columns is equal to the length of list land
log-likelihood for the model
log-likelihood for the local model (only local variables)
an object of type lm/glm/lmer that corresponds to the estimated model conditionnaly to the estimated influence functions.
fitted values
the family of the spatial influence function
an object of class optim or optimize giving informations about the optimization procedure see optim() or optimize() for further details.
akaike information criterion
akaike information criterion for local model (no landscape variable)
number of parameters
p.value of the test of global effect of spatial variables. Obtained from the likelihood ratio test between the complete model and the local model.
family distribution for the model
standard error for gaussian family, NA in other case
type of local model: GLM for generalised model, LMM for linear mixed model or GLMM for generalised linear mixed model
standard deviation of random effects for LMM or GLMM
estimated residuals
a logical indicating the value used for estimation
a number indicating the value used for raster discretisazion
Chandler R. and Hepinstall-Cymerman J. (2016) Estimating the spatial scales of landscape effects on abundance. Landscape ecology, 31: 1383-1394.
# NOT RUN {
data(dataSiland)
data(landSiland)
resF=Fsiland(y~locvar,land=landSiland,data=dataSiland,sif="exponential")
resF
resF$AIC
# }
# NOT RUN {
# }
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