# A function to compute joint-count test for binomial and multinomial (asymptotic and permutation distributions). A function to compute joincount tests for spatial qualitative data. This function is a wrapper of `joincount.multi` and `joincount.test` in `spdep` package. ```r jc.test(formula = NULL, data = NULL, fx = NULL, listw = NULL, na.action, zero.policy = NULL, distr = "asymptotic", alternative = "greater", control =list()) ``` ## Arguments - `formula`: a symbolic description of the factor(s). - `data`: an (optional) data frame or a sf object with points/multipolygons geometry containing the variable(s) to be tested. - `fx`: a factor or a matrix of factors in columns - `listw`: A `listw` object created for example by `nb2listw` from `spdep` package; if `nb2listw` not given, the spatial weights are built using the object given in `listw` argument (usually an `sf` object). Default = `NULL`. - `na.action`: A function (default `options("na.action")`), can also be `na.omit` or `na.exclude`. It may be necessary to set `zero.policy` to `TRUE` because this subsetting may create no-neighbour observations. - `zero.policy`: Similar to the corresponding parameter of `lagsarlm` function in `spatialreg` package. If `TRUE` assign zero to the lagged value of zones without neighbours. Default = `NULL`. - `distr`: character. Distribution type "asymptotic" (default) or "mc". - `alternative`: character string specifying the alternative hypothesis, must be one of "greater" (default), or "less". - `control`: list of additional arguments. ## Returns An `spjctest` object. This type of object is a list of `htest` objects. The length of the list is the number of factor variables included in the `formula` or the number of columns in `fx`. Each element of the list can be a `jclist` object, for binomial factors, or a `jcmulti` object for multinomial factors. See `joincount.test` or `joincount.multi` for additional details. ## Control arguments ||| |:--|:--| |`nsim`|number of permutations used in the Monte Carlo distribution. Default `nsim` = 999.| |`seedinit`|seed to select the initial element during the simulations. Default `seedinit` = 1111.| |`adjust.n`|default *TRUE*, if *FALSE* the number of observations is not adjusted for no-neighbour observations, if *TRUE*, the number of observations is adjusted consistently. See `joincount.multi` for aditional information.| |`spChk`|should the data vector names be checked against the spatial objects for identity integrity, *TRUE*, or *FALSE*, default *NULL* to use *get.spChkOption()*. See `joincount.multi` for aditional information.| |`sampling`|default *nonfree*, may be *free*. See `joincount.test` for additional information.| |`queen`|default *TRUE*. Defines the neighborhood criteria for *sf* objects.| |`style`|defines the style for `listw` . Default = *B* (binary).| |`knn`|chooses the number of neighboors when this criteria is used. Default `knn` = 5.| ## Examples ```r ## Case 1 ## Multinomial + Binomial using a sf multipolygon data("provinces_spain") # sf::sf_use_s2(FALSE) provinces_spain$Mal2Fml <- factor(provinces_spain$Mal2Fml > 100) levels(provinces_spain$Mal2Fml) = c("men","woman") provinces_spain$Older <- cut(provinces_spain$Older, breaks = c(-Inf,19,22.5,Inf)) levels(provinces_spain$Older) = c("low","middle","high") f1 <- ~ Older + Mal2Fml jc1 <- jc.test(formula = f1, data = provinces_spain, distr = "mc", alternative = "greater", zero.policy = TRUE) summary(jc1) provinces_spain$Coast <- factor(provinces_spain$Coast) levels(provinces_spain$Coast) = c("no","yes") f2 <- ~ Mal2Fml + Coast jc2 <- jc.test(formula = f2, data = provinces_spain, distr = "mc", zero.policy = TRUE) summary(jc2) # Case 2: ## Multinomial using a sf multipoint data("FastFood.sf") # sf::sf_use_s2(FALSE) f1 <- ~ Type jc3 <- jc.test(formula = f1, data = FastFood.sf, distr = "asymptotic", control = list(knn = 6)) summary(jc3) # Examples function joincount.test data(oldcol, package = "spdep") HICRIME <- cut(COL.OLD$CRIME, breaks = c(0,35,80), labels = c("low","high")) names(HICRIME) <- rownames(COL.OLD) jc4 <- jc.test(fx = HICRIME, listw = spdep::nb2listw(COL.nb, style="B")) summary(jc4) jc5 <- jc.test(fx = HICRIME, listw = spdep::nb2listw(COL.nb, style="B"), distr = "mc") summary(jc5) HICRIME <- cut(COL.OLD$CRIME, breaks = c(0, 35, 80), labels = c("low","high")) names(HICRIME) <- rownames(COL.OLD) jc6 <- jc.test(fx = HICRIME, listw = spdep::nb2listw(COL.nb, style = "B")) summary(jc6) ``` ## References * Cliff, A. D., Ord, J. K. 1981 **Spatial processes**, Pion, pp. 19-20. * Upton, G., Fingleton, B. 1985 **Spatial data analysis by example: point pattern and qualitative data**, Wiley, pp. 158–170. ## See Also `print.summary.spqtest`, `joincount.test`, `joincount.multi` ## Author(s) ||| |:--|:--| |Fernando López|fernando.lopez@upct.es| |Román Mínguez|roman.minguez@uclm.es| |Antonio Páez|paezha@gmail.com| |Manuel Ruiz|manuel.ruiz@upct.es|

jc.test function