hier.part (version 1.0-6)

rand.hp: Randomization Test for Hierarchical Partitioning

Description

Randomizes elements in each column in xcan and recalculates hier.part num.reps times

Usage

rand.hp(y, xcan,
        family = c("gaussian", "binomial", "Gamma", "inverse.gaussian",
                   "poisson", "quasi", "quasibinomial", "quasipoisson",
                   "beta", "ordinal"),
        link = c("logit", "probit", "cloglog", "cauchit", "loglog"),
        gof = c("Rsqu", "RMSPE", "logLik"),
        num.reps = 100,
        ...)

Arguments

y

a vector containing the dependent variables

xcan

a dataframe containing the n independent variables

family

a character string naming a family function used by stats::glm (See stats::family for details of family functions). Valid values are "gaussian", "binomial", "Gamma", "inverse.gaussian", "poisson", "quasi", "quasibinomial","quasipoisson". Alternatively a value of "beta" will use the betareg::betareg beta regression model, or "ordinal" will use the MASS::polr ordered logistic or probit regression. For these last two options, a value for the link argument is required.

link

character specification of the link function, only used if family = "beta" or "ordinal". For "beta", this argument equals the "link" argument in betareg::betareg. For "ordinal", it equals the "method" argument in MASS::polr, where "logit" = "logistic".

gof

Goodness-of-fit measure. Currently "RMSPE", Root-mean-square 'prediction' error, "logLik", Log-Likelihood or "Rsqu", R-squared. R-squared is only applicable if family = "Gaussian".

num.reps

Number of repeated randomizations

...

additional arguments to passed to glm, betareg::betareg, or MASS::polr

Value

a list containing

Irands

matrix of num.reps + 1 rows of I values for each predictor variable. The first row contains the observed I values and the remaining num.reps rows contains the I values returned for each randomization.

Iprobs

data.frame of observed I values for each variable, Z-scores for the generated distribution of randomized Is and an indication of statistical significance. Z-scores are calculated as (observed - mean(randomizations))/sd(randomizations), and statistical significance (*) is based on upper 0.95 confidence limit (\(Z >= 1.65\)).

Details

This function is a randomization routine for the hier.part function which returns a matrix of I values (the independent contribution towards explained variance in a multivariate dataset) for all values from num.reps randomizations. For each randomization, the values in each variable (i.e each column of xcan) are randomized independently, and hier.part is run on the randomized xcan. As well as the randomized I matrix, the function returns a summary table listing the observed I values, the 95th and 99th percentile values of I for the randomized dataset.

References

Hatt, B. E., Fletcher, T. D., Walsh, C. J. and Taylor, S. L. 2004 The influence of urban density and drainage infrastructure on the concentrations and loads of pollutants in small streams. Environmental Management 34, 112--124.

Mac Nally, R. 2000 Regression and model building in conservation biology, biogeography and ecology: the distinction between and reconciliation of 'predictive' and 'explanatory' models. Biodiversity and Conservation 9, 655--671.

Mac Nally, R. 2002 Multiple regression and inference in conservation biology and ecology: further comments on identifying important predictor variables. Biodiversity and Conservation 11, 1397--1401.

Walsh, C. J., Papas, P. J., Crowther, D., Sim, P. T., and Yoo, J. 2004 Stormwater drainage pipes as a threat to a stream-dwelling amphipod of conservation significance, Austrogammarus australis, in southeastern Australia. Biodiversity and Conservation 13, 781--793.

See Also

hier.part, partition

Examples

# NOT RUN {
    #linear regression of log(electrical conductivity) in streams
    #against four independent variables describing catchment
    #characteristics (from Hatt et al. 2004).

    
# }
# NOT RUN {
data(urbanwq)
    env <- urbanwq[,2:5]
    rand.hp(urbanwq$lec, env, fam = "gaussian",
    gof = "Rsqu", num.reps = 999)$Iprobs
    
# }
# NOT RUN {
    #logistic regression of an amphipod species occurrence in
    #streams against four independent variables describing
    #catchment characteristics (from Walsh et al. 2004).

    
# }
# NOT RUN {
data(amphipod)
    env1 <- amphipod[,2:5]
    rand.hp(amphipod$australis, env1, fam = "binomial",
    gof = "logLik", num.reps = 999)$Iprobs
    
# }