Nothing
R/geodetector.R
In gdverse: Analysis of Spatial Stratified Heterogeneity
#' @title factor detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' The factor detector q-statistic measures the spatial stratified heterogeneity of a
#' variable Y, or the determinant power of a covariate X of Y.
#'
#' @param y Variable Y, continuous numeric vector.
#' @param x Covariate X, \code{factor}, \code{character} or \code{discrete numeric}.
#' @param confintv (optional) Whether to compute the confidence interval for the q statistic,
#' default is `FALSE`.
#' @param alpha (optional) Confidence level of the interval, default is `0.95`.
#'
#' @return A list.
#' \describe{
#' \item{\code{Q-statistic}}{the q statistic for factor detector}
#' \item{\code{P-value}}{the p value for factor detector}
#' \item{\code{CIL}}{the confidence interval lower bound}
#' \item{\code{CIU}}{the confidence interval upper bound}
#' }
#' @export
#'
#' @examples
#' factor_detector(y = 1:7,x = c('x',rep('y',3),rep('z',3)))
#'
factor_detector = \(y,x,confintv = FALSE,alpha = 0.95){
x = gdverse::all2int(x)
gdf = tibble::tibble(x = x, y = y) %>%
dplyr::group_by(x) %>%
dplyr::filter(dplyr::n() > 1) %>%
dplyr::ungroup() %>%
dplyr::mutate(x = factor(x))
x = gdf$x
y = gdf$y
rss = \(y) (length(y) - 1) * stats::var(y)
qv = 1 - sum(tapply(y, x, rss)) / rss(y)
N = length(y)
L = length(levels(x))
Fv = ((N - L) * qv) / ((L - 1) * (1 - qv))
hmean = tapply(y, x, mean)
Nh = tapply(y, x, length)
v1 = sum(hmean ^ 2)
v2 = (sum(sqrt(Nh) * hmean)) ^ 2 / N
lambda = (v1 - v2) / (stats::var(y) * (N - 1) / N)
pv = suppressWarnings(stats::pf(Fv, df1 = (L - 1), df2 = (N - L),
ncp = lambda, lower.tail = FALSE))
if (confintv) {
alpha1 = dplyr::if_else(alpha>=0.5,alpha,1-alpha)
alpha2 = 1 - alpha1
ncp1 = .calc_ncfncp(Fv,L-1,N-L,alpha1)
ncp2 = .calc_ncfncp(Fv,L-1,N-L,alpha2)
fd = list("Q-statistic" = qv, "P-value" = pv,
"CIL" = (ncp1 / (ncp1 + N)),
"CIU" = (ncp2 / (ncp2 + N)))
} else {
fd = list("Q-statistic" = qv, "P-value" = pv)
}
return(fd)
}
#' @title interaction detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' Identify the interaction between different risk factors, that is, assess whether factors X1 and X2 together
#' increase or decrease the explanatory power of the dependent variable Y, or whether the effects of these factors
#' on Y are independent of each other.
#'
#' @param y Dependent variable, continuous numeric vector.
#' @param x1 Covariate \eqn{X_1}, \code{factor}, \code{character} or \code{discrete numeric}.
#' @param x2 Covariate \eqn{X_2}, \code{factor}, \code{character} or \code{discrete numeric}.
#'
#' @return A list.
#' \describe{
#' \item{\code{Variable1 Q-statistics}}{Q-statistics for variable1}
#' \item{\code{Variable2 Q-statistics}}{Q-statistics for variable2}
#' \item{\code{Variable1 and Variable2 interact Q-statistics}}{Q-statistics for variable1 and variable2 interact}
#' \item{\code{Interaction}}{the interact result type}
#' }
#' @export
#'
#' @examples
#' interaction_detector(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3)))
#'
interaction_detector = \(y,x1,x2){
x12 = paste0(x1,x2,'_')
qv1 = gdverse::factor_detector(y,x1)[[1]]
qv2 = gdverse::factor_detector(y,x2)[[1]]
qv12 = gdverse::factor_detector(y,x12)[[1]]
if (qv12 < min(qv1, qv2)) {
interaction = c("Weaken, nonlinear")
} else if (qv12 >= min(qv1, qv2) & qv12 <= max(qv1, qv2)) {
interaction = c("Weaken, uni-")
} else if (qv12 > max(qv1, qv2) & (qv12 < qv1 + qv2)) {
interaction = c("Enhance, bi-")
} else if (qv12 == qv1 + qv2) {
interaction = c("Independent")
} else {
interaction = c("Enhance, nonlinear")
}
interd = list(qv1,qv2,qv12,interaction)
names(interd) = c("Variable1 Q-statistics","Variable2 Q-statistics",
"Variable1 and Variable2 interact Q-statistics",
"Interaction")
return(interd)
}
#' @title risk detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' Determine whether there is a significant difference between the attribute means of two sub regions.
#'
#' @param y Variable Y, continuous numeric vector.
#' @param x Covariate X, \code{factor}, \code{character} or \code{discrete numeric}.
#' @param alpha (optional) Confidence level of the interval, default is `0.95`.
#'
#' @return A tibble.
#' @export
#'
#' @examples
#' risk_detector(y = 1:7,
#' x = c('x',rep('y',3),rep('z',3)))
#'
risk_detector = \(y,x,alpha = 0.95){
x = factor(x)
gdf = tibble::tibble(x = x, y = y)
paradf = utils::combn(levels(x),2,simplify = FALSE)
x1 = purrr::map_chr(seq_along(paradf), \(i) paradf[[i]][1])
x2 = purrr::map_chr(seq_along(paradf), \(i) paradf[[i]][2])
paradf = tibble::tibble(zone1st = paste0('zone',x1),
zone2nd = paste0('zone',x2))
twounit_risk_detector = \(n1,n2,cutoff = 0.95){
y1 = dplyr::filter(gdf, x == n1) %>% dplyr::pull(y)
y2 = dplyr::filter(gdf, x == n2) %>% dplyr::pull(y)
df0 = min(c(length(y1),length(y2))) - 1
tt = tryCatch({
stats::t.test(y1,y2,conf.level = cutoff)
}, error = function(e){
list("statistic" = 0,
"parameter" = df0,
"p.value" = 1)
})
risk = ifelse(tt$p.value < (1 - cutoff), "Yes", "No")
risk = factor(risk,levels = c("Yes", "No"), labels = c("Yes", "No"))
riskd = list(tt$statistic,tt$parameter,tt$p.value,risk)
names(riskd) = c("T-statistic","Degree-freedom","P-value","Risk")
return(riskd)
}
rd = purrr::map2_dfr(x1,x2,twounit_risk_detector,cutoff = alpha) %>%
dplyr::bind_cols(paradf,.)
return(rd)
}
#' @title ecological detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' Compare the effects of two factors \eqn{X_1} and \eqn{X_2} on the spatial distribution of the attribute \eqn{Y}.
#'
#' @param y Dependent variable, continuous numeric vector.
#' @param x1 Covariate \eqn{X_1}, \code{factor}, \code{character} or \code{discrete numeric}.
#' @param x2 Covariate \eqn{X_2}, \code{factor}, \code{character} or \code{discrete numeric}.
#' @param alpha (optional) Confidence level of the interval,default is `0.95`.
#'
#' @return A list.
#' \describe{
#' \item{\code{F-statistic}}{the result of F statistic for ecological detector}
#' \item{\code{P-value}}{the result of P value for ecological detector}
#' \item{\code{Ecological}}{is there a significant difference between the two factors \eqn{X_1} and \eqn{X_2} on the spatial distribution of the attribute \eqn{Y}}
#' }
#' @export
#'
#' @examples
#' ecological_detector(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3)))
#'
ecological_detector = \(y,x1,x2,alpha = 0.95){
q1 = factor_detector(y,x1)[[1]]
q2 = factor_detector(y,x2)[[1]]
fv = (1 - q1) / (1 - q2)
n = length(y)
p0 = stats::pf(fv, df1 = n - 1, df2 = n - 1, lower.tail = FALSE)
eco = ifelse(p0 < (1 - alpha), "Yes", "No")
eco = factor(eco,levels = c("Yes", "No"),labels = c("Yes", "No"))
ecod = list(fv,p0,eco)
names(ecod) = c("F-statistic","P-value","Ecological")
return(ecod)
}
#' @title geographical detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @note
#' Note that only one type of geodetector is supported at a time in `geodetector()`.
#'
#' @param formula A formula of geographical detector model.
#' @param data A data.frame or tibble of observation data.
#' @param type (optional) The type of geographical detector, which must be one of `factor`(default),
#' `interaction`, `risk`, `ecological`.
#' @param alpha (optional) Specifies the size of the alpha (confidence level). Default is `0.95`.
#'
#' @return A list.
#' \describe{
#' \item{\code{factor}}{the result of factor detector}
#' \item{\code{interaction}}{the result of interaction detector}
#' \item{\code{risk}}{the result of risk detector}
#' \item{\code{ecological}}{the result of ecological detector}
#' }
#' @export
#'
#' @examples
#' geodetector(y ~ x1 + x2,
#' tibble::tibble(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3))))
#'
#' geodetector(y ~ x1 + x2,
#' tibble::tibble(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3))),
#' type = 'interaction')
#'
#' geodetector(y ~ x1 + x2,
#' tibble::tibble(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3))),
#' type = 'risk',alpha = 0.95)
#'
#' geodetector(y ~ x1 + x2,
#' tibble::tibble(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3))),
#' type = 'ecological',alpha = 0.95)
#'
geodetector = \(formula,data,type = "factor",alpha = 0.95){
if (!(type %in% c("factor","interaction","risk", "ecological"))){
stop("`type` must be one of `factor`,`interaction`,`risk` and `ecological`!")
}
if (inherits(data,'sf')) {data = sf::st_drop_geometry(data)}
data = tibble::as_tibble(data)
formulaname = sdsfun::formula_varname(formula,data)
response = data[, formulaname[[1]], drop = TRUE]
explanatory = data[, formulaname[[2]]]
switch(type,
"factor" = {
res = purrr::map_dfr(names(explanatory),
\(i) gdverse::factor_detector(response,
data[,i,drop = TRUE])) %>%
dplyr::mutate(variable = names(explanatory)) %>%
dplyr::select(variable,dplyr::everything()) %>%
dplyr::arrange(dplyr::desc(`Q-statistic`))
res = list("factor" = res)
class(res) = "factor_detector"
},
"interaction" = {
res = utils::combn(names(explanatory), 2, simplify = FALSE) %>%
purrr::map_dfr(\(i) gdverse::interaction_detector(response,
data[,i[1],drop = TRUE],
data[,i[2],drop = TRUE]) %>%
tibble::as_tibble() %>%
dplyr::mutate(variable1 = i[1],
variable2 = i[2]) %>%
dplyr::select(variable1,variable2,Interaction,
dplyr::everything()))
res = list("interaction" = res)
class(res) = "interaction_detector"
},
"risk" = {
res = purrr::map_dfr(names(explanatory),
\(i) gdverse::risk_detector(response,
data[,i,drop = TRUE],
alpha) %>%
dplyr::mutate(variable = i) %>%
dplyr::select(variable,zone1st,zone2nd,Risk,
dplyr::everything()))
res = list("risk" = res)
class(res) = "risk_detector"
},
"ecological" = {
res = utils::combn(names(explanatory), 2, simplify = FALSE) %>%
purrr::map_dfr(\(i) gdverse::ecological_detector(response,
data[,i[1],drop = TRUE],
data[,i[2],drop = TRUE],
alpha) %>%
tibble::as_tibble() %>%
dplyr::mutate(variable1 = i[1],
variable2 = i[2]) %>%
dplyr::select(variable1,variable2,Ecological,
dplyr::everything()))
res = list("ecological" = res)
class(res) = "ecological_detector"
}
)
return(res)
}
#' @title print factor detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to format output for factor detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `knitr::kable()`.
#'
#' @return Formatted string output
#' @export
#'
print.factor_detector = \(x, ...) {
cat(" Factor Detector ")
# pander::pander(x$factor)
print(knitr::kable(x$factor,format = "markdown",digits = 12,align = 'c',...))
}
#' @title print interaction detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to format output for interaction detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `knitr::kable()`.
#'
#' @return Formatted string output
#' @export
#'
print.interaction_detector = \(x, ...) {
cat(" Interaction Detector ")
IntersectionSymbol = rawToChar(as.raw(c(0x20, 0xE2, 0x88, 0xA9, 0x20)))
x = x$interaction %>%
dplyr::mutate(`Interactive variable` = paste0(variable1,
IntersectionSymbol,
variable2)) %>%
dplyr::select(`Interactive variable`,Interaction)
# pander::pander(x)
print(knitr::kable(x,format = "markdown",align = 'c',...))
}
#' @title print risk detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to format output for risk detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `knitr::kable()`.
#'
#' @return Formatted string output
#' @export
#'
print.risk_detector = \(x, ...) {
cat(" Risk Detector \n")
x = dplyr::select(x$risk,variable,zone1st,zone2nd,Risk)
xvar = x %>%
dplyr::count(variable) %>%
dplyr::pull(variable)
rd2mat = \(x,zonevar){
matt = x %>%
dplyr::filter(variable == zonevar) %>%
dplyr::select(-variable) %>%
tidyr::pivot_wider(names_from = zone1st,
values_from = Risk)
mattname = names(matt)
mattname[1] = 'zone'
names(matt) = mattname
return(matt)
}
for (i in xvar){
cat(sprintf("\n Variable %s:",i))
print(knitr::kable(rd2mat(x,i),format = "markdown",align = 'c',...))
}
}
#' @title print ecological detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to format output for ecological detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `knitr::kable()`.
#'
#' @return Formatted string output
#' @export
#'
print.ecological_detector = \(x, ...) {
cat(" Ecological Detector ")
x = dplyr::select(x$ecological,
dplyr::all_of(c('variable1','variable2','Ecological')))
ed2mat = \(x){
matt = x %>%
tidyr::pivot_wider(names_from = "variable2",
values_from = "Ecological")
matname = matt$variable1
matt = matt %>%
dplyr::select(-variable1) %>%
as.matrix()
rownames(matt) = matname
return(matt)
}
print(knitr::kable(ed2mat(x),format = "markdown",align = 'c',...))
}
#' @title plot factor detector result
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to plot output for factor detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param slicenum (optional) The number of labels facing inward. Default is `2`.
#' @param alpha (optional) Confidence level. Default is `0.95`.
#' @param keep (optional) Whether to keep Q-value results for insignificant variables,
#' default is `TRUE`.
#' @param qlabelsize (optional) Set the font size of the q-value text labels in the plot.
#' @param ... (optional) Other arguments passed to `ggplot2::theme()`.
#'
#' @return A ggplot2 layer.
#' @export
#'
plot.factor_detector = \(x, slicenum = 2, alpha = 0.95,
keep = TRUE, qlabelsize = 3.88, ...) {
g = x$factor %>%
dplyr::select(variable, qv = `Q-statistic`,pv = `P-value`) %>%
dplyr::filter(!is.na(qv)) %>%
dplyr::mutate(variable = forcats::fct_reorder(variable, qv, .desc = TRUE)) %>%
dplyr::mutate(variable_col = c("first",rep("others",times = nrow(.)-1))) %>%
dplyr::mutate(qv_text = paste0(sprintf("%4.2f", qv * 100), "%"))
if (!keep) {
g = g %>%
dplyr::mutate(significance = dplyr::if_else(pv <= 1 - alpha,
'Significant',
'Not Significant',
NA)) %>%
dplyr::filter(significance == 'Significant')
}
fig_factor = ggplot2::ggplot(g,
ggplot2::aes(x = qv, y = variable, fill = variable_col)) +
ggplot2::geom_col() +
ggplot2::scale_x_continuous(expand = ggplot2::expansion(mult = c(0, 0.1))) +
ggplot2::scale_y_discrete(limits = rev) +
ggplot2::scale_fill_manual(breaks = c("first", "others"),
values = c("#DE3533","#808080")) +
ggplot2::geom_text(data = dplyr::slice(g, seq(1,slicenum)),
ggplot2::aes(label = qv_text), hjust = 1.25,
family = "serif", fontface = "bold", size = qlabelsize) +
ggplot2::geom_text(data = dplyr::slice(g, -seq(1,slicenum)),
ggplot2::aes(label = qv_text), hjust = -0.1,
family = "serif", fontface = "bold", size = qlabelsize) +
ggplot2::labs(x = "Q value", y = "") +
ggplot2::theme_bw() +
ggplot2::theme(panel.grid.major.y = ggplot2::element_blank(),
axis.text.y = ggplot2::element_text(family = "serif"),
axis.text.x = ggplot2::element_text(family = "serif"),
legend.position = "off", ...)
return(fig_factor)
}
#' @title plot interaction detector result
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to plot output for interaction detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param alpha (optional) Picture transparency. Default is `1`.
#' @param ... (optional) Other arguments passed to `ggplot2::theme()`.
#'
#' @return A ggplot2 layer
#' @export
#'
plot.interaction_detector = \(x,alpha = 1,...){
g = x$interaction %>%
dplyr::select(interactv = `Variable1 and Variable2 interact Q-statistics`,
dplyr::everything())
gv1 = dplyr::count(g,variable1)
gv2 = dplyr::count(g,variable2)
gv = gv1 %>%
dplyr::left_join(gv2,by = 'n') %>%
dplyr::arrange(dplyr::desc(n))
g = g %>%
dplyr::mutate(variable1 = factor(variable1,levels = gv$variable1),
variable2 = factor(variable2,levels = rev(gv$variable2)))
fig_interaction = ggplot2::ggplot(g,
ggplot2::aes(x = variable1, y = variable2,
size = interactv, color = Interaction)) +
ggplot2::geom_point(alpha = alpha) +
ggplot2::scale_size(range = c(1,10)) +
ggplot2::guides(size = ggplot2::guide_legend(
override.aes = list(shape = 21,
fill = "transparent",
color = "black"))) +
ggplot2::scale_color_manual(values = c("Enhance, nonlinear" = "#EA4848",
"Independent" = "#E08338",
"Enhance, bi-" = "#F2C55E",
"Weaken, uni-" = "#6EE9EF",
"Weaken, nonlinear" = "#558DE8")) +
ggplot2::labs(x = "", y = "", size = "", color = "") +
ggplot2::coord_fixed() +
ggplot2::theme_bw() +
ggplot2::theme(axis.text.y = ggplot2::element_text(family = "serif"),
axis.text.x = ggplot2::element_text(family = "serif"),
...)
return(fig_interaction)
}
#' @title plot risk detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to plot output for risk detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `ggplot2::theme()`.
#'
#' @return A ggplot2 layer
#' @export
#'
plot.risk_detector = \(x, ...) {
plot_rduni = \(g,gname,...){
fig_rd = ggplot2::ggplot(data = g,
ggplot2::aes(x = zone1st, y = zone2nd, fill = risk)) +
ggplot2::geom_tile(color = "white", size = 0.75) +
ggplot2::geom_text(ggplot2::aes(label = risk), color = "black") +
ggplot2::scale_fill_manual(values = c("N" = "#7fdbff", "Y" = "#ffa500")) +
ggplot2::coord_fixed() +
ggplot2::theme_minimal() +
ggplot2::theme(axis.title.x = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank(),
axis.text.x = ggplot2::element_text(angle = 60,hjust = 1,family = "serif"),
axis.text.y = ggplot2::element_text(family = "serif"),
legend.position = "none",
panel.grid = ggplot2::element_blank(), ...) +
ggplot2::annotate("text", x = Inf, y = -Inf, label = gname,
vjust = -1.75, hjust = 1.25,
color = "#ff0000", family = "serif")
return(fig_rd)
}
g = dplyr::select(x$risk,variable,zone1st,zone2nd,Risk) %>%
dplyr::mutate(risk = forcats::fct_recode(Risk,"Y" = "Yes", "N" = "No"))
zonevars = unique(g$variable)
fig_rds = purrr::map(zonevars,\(x) plot_rduni(dplyr::filter(g,variable == x)
,x,...))
# fig_p = cowplot::plot_grid(plotlist = fig_rds,
# ncol = ceiling(sqrt(length(zonevars))),
# label_fontfamily = 'serif',
# label_fontface = 'plain')
fig_p = patchwork::wrap_plots(fig_rds,
ncol = ceiling(sqrt(length(zonevars))))
return(fig_p)
}
#' @title plot ecological detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to plot output for ecological detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `ggplot2::theme()`.
#'
#' @return A ggplot2 layer
#' @export
#'
plot.ecological_detector = \(x, ...) {
g = dplyr::select(x$ecological,
dplyr::all_of(c('variable1','variable2','Ecological')))
gv1 = dplyr::count(g,variable1)
gv2 = dplyr::count(g,variable2)
gv = gv1 %>%
dplyr::left_join(gv2,by = 'n') %>%
dplyr::arrange(dplyr::desc(n))
g = g %>%
dplyr::mutate(variable1 = factor(variable1,levels = gv$variable1),
variable2 = factor(variable2,levels = rev(gv$variable2)),
eco = forcats::fct_recode(Ecological,"Y" = "Yes", "N" = "No"))
fig_ed = ggplot2::ggplot(data = g,
ggplot2::aes(x = variable1, y = variable2, fill = eco)) +
ggplot2::geom_tile(color = "white", size = 0.75) +
ggplot2::geom_text(ggplot2::aes(label = eco), color = "black") +
ggplot2::scale_fill_manual(values = c("N" = "#7fdbff", "Y" = "#ffa500")) +
ggplot2::coord_fixed() +
ggplot2::theme_minimal() +
ggplot2::theme(axis.title.x = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank(),
axis.text.y = ggplot2::element_text(family = "serif"),
axis.text.x = ggplot2::element_text(family = "serif"),
legend.position = "none",
panel.grid = ggplot2::element_blank(), ...)
return(fig_ed)
}
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#' @title factor detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' The factor detector q-statistic measures the spatial stratified heterogeneity of a
#' variable Y, or the determinant power of a covariate X of Y.
#'
#' @param y Variable Y, continuous numeric vector.
#' @param x Covariate X, \code{factor}, \code{character} or \code{discrete numeric}.
#' @param confintv (optional) Whether to compute the confidence interval for the q statistic,
#' default is `FALSE`.
#' @param alpha (optional) Confidence level of the interval, default is `0.95`.
#'
#' @return A list.
#' \describe{
#' \item{\code{Q-statistic}}{the q statistic for factor detector}
#' \item{\code{P-value}}{the p value for factor detector}
#' \item{\code{CIL}}{the confidence interval lower bound}
#' \item{\code{CIU}}{the confidence interval upper bound}
#' }
#' @export
#'
#' @examples
#' factor_detector(y = 1:7,x = c('x',rep('y',3),rep('z',3)))
#'
factor_detector = \(y,x,confintv = FALSE,alpha = 0.95){
x = gdverse::all2int(x)
gdf = tibble::tibble(x = x, y = y) %>%
dplyr::group_by(x) %>%
dplyr::filter(dplyr::n() > 1) %>%
dplyr::ungroup() %>%
dplyr::mutate(x = factor(x))
x = gdf$x
y = gdf$y
rss = \(y) (length(y) - 1) * stats::var(y)
qv = 1 - sum(tapply(y, x, rss)) / rss(y)
N = length(y)
L = length(levels(x))
Fv = ((N - L) * qv) / ((L - 1) * (1 - qv))
hmean = tapply(y, x, mean)
Nh = tapply(y, x, length)
v1 = sum(hmean ^ 2)
v2 = (sum(sqrt(Nh) * hmean)) ^ 2 / N
lambda = (v1 - v2) / (stats::var(y) * (N - 1) / N)
pv = suppressWarnings(stats::pf(Fv, df1 = (L - 1), df2 = (N - L),
ncp = lambda, lower.tail = FALSE))
if (confintv) {
alpha1 = dplyr::if_else(alpha>=0.5,alpha,1-alpha)
alpha2 = 1 - alpha1
ncp1 = .calc_ncfncp(Fv,L-1,N-L,alpha1)
ncp2 = .calc_ncfncp(Fv,L-1,N-L,alpha2)
fd = list("Q-statistic" = qv, "P-value" = pv,
"CIL" = (ncp1 / (ncp1 + N)),
"CIU" = (ncp2 / (ncp2 + N)))
} else {
fd = list("Q-statistic" = qv, "P-value" = pv)
}
return(fd)
}
#' @title interaction detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' Identify the interaction between different risk factors, that is, assess whether factors X1 and X2 together
#' increase or decrease the explanatory power of the dependent variable Y, or whether the effects of these factors
#' on Y are independent of each other.
#'
#' @param y Dependent variable, continuous numeric vector.
#' @param x1 Covariate \eqn{X_1}, \code{factor}, \code{character} or \code{discrete numeric}.
#' @param x2 Covariate \eqn{X_2}, \code{factor}, \code{character} or \code{discrete numeric}.
#'
#' @return A list.
#' \describe{
#' \item{\code{Variable1 Q-statistics}}{Q-statistics for variable1}
#' \item{\code{Variable2 Q-statistics}}{Q-statistics for variable2}
#' \item{\code{Variable1 and Variable2 interact Q-statistics}}{Q-statistics for variable1 and variable2 interact}
#' \item{\code{Interaction}}{the interact result type}
#' }
#' @export
#'
#' @examples
#' interaction_detector(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3)))
#'
interaction_detector = \(y,x1,x2){
x12 = paste0(x1,x2,'_')
qv1 = gdverse::factor_detector(y,x1)[[1]]
qv2 = gdverse::factor_detector(y,x2)[[1]]
qv12 = gdverse::factor_detector(y,x12)[[1]]
if (qv12 < min(qv1, qv2)) {
interaction = c("Weaken, nonlinear")
} else if (qv12 >= min(qv1, qv2) & qv12 <= max(qv1, qv2)) {
interaction = c("Weaken, uni-")
} else if (qv12 > max(qv1, qv2) & (qv12 < qv1 + qv2)) {
interaction = c("Enhance, bi-")
} else if (qv12 == qv1 + qv2) {
interaction = c("Independent")
} else {
interaction = c("Enhance, nonlinear")
}
interd = list(qv1,qv2,qv12,interaction)
names(interd) = c("Variable1 Q-statistics","Variable2 Q-statistics",
"Variable1 and Variable2 interact Q-statistics",
"Interaction")
return(interd)
}
#' @title risk detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' Determine whether there is a significant difference between the attribute means of two sub regions.
#'
#' @param y Variable Y, continuous numeric vector.
#' @param x Covariate X, \code{factor}, \code{character} or \code{discrete numeric}.
#' @param alpha (optional) Confidence level of the interval, default is `0.95`.
#'
#' @return A tibble.
#' @export
#'
#' @examples
#' risk_detector(y = 1:7,
#' x = c('x',rep('y',3),rep('z',3)))
#'
risk_detector = \(y,x,alpha = 0.95){
x = factor(x)
gdf = tibble::tibble(x = x, y = y)
paradf = utils::combn(levels(x),2,simplify = FALSE)
x1 = purrr::map_chr(seq_along(paradf), \(i) paradf[[i]][1])
x2 = purrr::map_chr(seq_along(paradf), \(i) paradf[[i]][2])
paradf = tibble::tibble(zone1st = paste0('zone',x1),
zone2nd = paste0('zone',x2))
twounit_risk_detector = \(n1,n2,cutoff = 0.95){
y1 = dplyr::filter(gdf, x == n1) %>% dplyr::pull(y)
y2 = dplyr::filter(gdf, x == n2) %>% dplyr::pull(y)
df0 = min(c(length(y1),length(y2))) - 1
tt = tryCatch({
stats::t.test(y1,y2,conf.level = cutoff)
}, error = function(e){
list("statistic" = 0,
"parameter" = df0,
"p.value" = 1)
})
risk = ifelse(tt$p.value < (1 - cutoff), "Yes", "No")
risk = factor(risk,levels = c("Yes", "No"), labels = c("Yes", "No"))
riskd = list(tt$statistic,tt$parameter,tt$p.value,risk)
names(riskd) = c("T-statistic","Degree-freedom","P-value","Risk")
return(riskd)
}
rd = purrr::map2_dfr(x1,x2,twounit_risk_detector,cutoff = alpha) %>%
dplyr::bind_cols(paradf,.)
return(rd)
}
#' @title ecological detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' Compare the effects of two factors \eqn{X_1} and \eqn{X_2} on the spatial distribution of the attribute \eqn{Y}.
#'
#' @param y Dependent variable, continuous numeric vector.
#' @param x1 Covariate \eqn{X_1}, \code{factor}, \code{character} or \code{discrete numeric}.
#' @param x2 Covariate \eqn{X_2}, \code{factor}, \code{character} or \code{discrete numeric}.
#' @param alpha (optional) Confidence level of the interval,default is `0.95`.
#'
#' @return A list.
#' \describe{
#' \item{\code{F-statistic}}{the result of F statistic for ecological detector}
#' \item{\code{P-value}}{the result of P value for ecological detector}
#' \item{\code{Ecological}}{is there a significant difference between the two factors \eqn{X_1} and \eqn{X_2} on the spatial distribution of the attribute \eqn{Y}}
#' }
#' @export
#'
#' @examples
#' ecological_detector(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3)))
#'
ecological_detector = \(y,x1,x2,alpha = 0.95){
q1 = factor_detector(y,x1)[[1]]
q2 = factor_detector(y,x2)[[1]]
fv = (1 - q1) / (1 - q2)
n = length(y)
p0 = stats::pf(fv, df1 = n - 1, df2 = n - 1, lower.tail = FALSE)
eco = ifelse(p0 < (1 - alpha), "Yes", "No")
eco = factor(eco,levels = c("Yes", "No"),labels = c("Yes", "No"))
ecod = list(fv,p0,eco)
names(ecod) = c("F-statistic","P-value","Ecological")
return(ecod)
}
#' @title geographical detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @note
#' Note that only one type of geodetector is supported at a time in `geodetector()`.
#'
#' @param formula A formula of geographical detector model.
#' @param data A data.frame or tibble of observation data.
#' @param type (optional) The type of geographical detector, which must be one of `factor`(default),
#' `interaction`, `risk`, `ecological`.
#' @param alpha (optional) Specifies the size of the alpha (confidence level). Default is `0.95`.
#'
#' @return A list.
#' \describe{
#' \item{\code{factor}}{the result of factor detector}
#' \item{\code{interaction}}{the result of interaction detector}
#' \item{\code{risk}}{the result of risk detector}
#' \item{\code{ecological}}{the result of ecological detector}
#' }
#' @export
#'
#' @examples
#' geodetector(y ~ x1 + x2,
#' tibble::tibble(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3))))
#'
#' geodetector(y ~ x1 + x2,
#' tibble::tibble(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3))),
#' type = 'interaction')
#'
#' geodetector(y ~ x1 + x2,
#' tibble::tibble(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3))),
#' type = 'risk',alpha = 0.95)
#'
#' geodetector(y ~ x1 + x2,
#' tibble::tibble(y = 1:7,
#' x1 = c('x',rep('y',3),rep('z',3)),
#' x2 = c(rep('a',2),rep('b',2),rep('c',3))),
#' type = 'ecological',alpha = 0.95)
#'
geodetector = \(formula,data,type = "factor",alpha = 0.95){
if (!(type %in% c("factor","interaction","risk", "ecological"))){
stop("`type` must be one of `factor`,`interaction`,`risk` and `ecological`!")
}
if (inherits(data,'sf')) {data = sf::st_drop_geometry(data)}
data = tibble::as_tibble(data)
formulaname = sdsfun::formula_varname(formula,data)
response = data[, formulaname[[1]], drop = TRUE]
explanatory = data[, formulaname[[2]]]
switch(type,
"factor" = {
res = purrr::map_dfr(names(explanatory),
\(i) gdverse::factor_detector(response,
data[,i,drop = TRUE])) %>%
dplyr::mutate(variable = names(explanatory)) %>%
dplyr::select(variable,dplyr::everything()) %>%
dplyr::arrange(dplyr::desc(`Q-statistic`))
res = list("factor" = res)
class(res) = "factor_detector"
},
"interaction" = {
res = utils::combn(names(explanatory), 2, simplify = FALSE) %>%
purrr::map_dfr(\(i) gdverse::interaction_detector(response,
data[,i[1],drop = TRUE],
data[,i[2],drop = TRUE]) %>%
tibble::as_tibble() %>%
dplyr::mutate(variable1 = i[1],
variable2 = i[2]) %>%
dplyr::select(variable1,variable2,Interaction,
dplyr::everything()))
res = list("interaction" = res)
class(res) = "interaction_detector"
},
"risk" = {
res = purrr::map_dfr(names(explanatory),
\(i) gdverse::risk_detector(response,
data[,i,drop = TRUE],
alpha) %>%
dplyr::mutate(variable = i) %>%
dplyr::select(variable,zone1st,zone2nd,Risk,
dplyr::everything()))
res = list("risk" = res)
class(res) = "risk_detector"
},
"ecological" = {
res = utils::combn(names(explanatory), 2, simplify = FALSE) %>%
purrr::map_dfr(\(i) gdverse::ecological_detector(response,
data[,i[1],drop = TRUE],
data[,i[2],drop = TRUE],
alpha) %>%
tibble::as_tibble() %>%
dplyr::mutate(variable1 = i[1],
variable2 = i[2]) %>%
dplyr::select(variable1,variable2,Ecological,
dplyr::everything()))
res = list("ecological" = res)
class(res) = "ecological_detector"
}
)
return(res)
}
#' @title print factor detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to format output for factor detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `knitr::kable()`.
#'
#' @return Formatted string output
#' @export
#'
print.factor_detector = \(x, ...) {
cat(" Factor Detector ")
# pander::pander(x$factor)
print(knitr::kable(x$factor,format = "markdown",digits = 12,align = 'c',...))
}
#' @title print interaction detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to format output for interaction detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `knitr::kable()`.
#'
#' @return Formatted string output
#' @export
#'
print.interaction_detector = \(x, ...) {
cat(" Interaction Detector ")
IntersectionSymbol = rawToChar(as.raw(c(0x20, 0xE2, 0x88, 0xA9, 0x20)))
x = x$interaction %>%
dplyr::mutate(`Interactive variable` = paste0(variable1,
IntersectionSymbol,
variable2)) %>%
dplyr::select(`Interactive variable`,Interaction)
# pander::pander(x)
print(knitr::kable(x,format = "markdown",align = 'c',...))
}
#' @title print risk detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to format output for risk detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `knitr::kable()`.
#'
#' @return Formatted string output
#' @export
#'
print.risk_detector = \(x, ...) {
cat(" Risk Detector \n")
x = dplyr::select(x$risk,variable,zone1st,zone2nd,Risk)
xvar = x %>%
dplyr::count(variable) %>%
dplyr::pull(variable)
rd2mat = \(x,zonevar){
matt = x %>%
dplyr::filter(variable == zonevar) %>%
dplyr::select(-variable) %>%
tidyr::pivot_wider(names_from = zone1st,
values_from = Risk)
mattname = names(matt)
mattname[1] = 'zone'
names(matt) = mattname
return(matt)
}
for (i in xvar){
cat(sprintf("\n Variable %s:",i))
print(knitr::kable(rd2mat(x,i),format = "markdown",align = 'c',...))
}
}
#' @title print ecological detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to format output for ecological detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `knitr::kable()`.
#'
#' @return Formatted string output
#' @export
#'
print.ecological_detector = \(x, ...) {
cat(" Ecological Detector ")
x = dplyr::select(x$ecological,
dplyr::all_of(c('variable1','variable2','Ecological')))
ed2mat = \(x){
matt = x %>%
tidyr::pivot_wider(names_from = "variable2",
values_from = "Ecological")
matname = matt$variable1
matt = matt %>%
dplyr::select(-variable1) %>%
as.matrix()
rownames(matt) = matname
return(matt)
}
print(knitr::kable(ed2mat(x),format = "markdown",align = 'c',...))
}
#' @title plot factor detector result
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to plot output for factor detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param slicenum (optional) The number of labels facing inward. Default is `2`.
#' @param alpha (optional) Confidence level. Default is `0.95`.
#' @param keep (optional) Whether to keep Q-value results for insignificant variables,
#' default is `TRUE`.
#' @param qlabelsize (optional) Set the font size of the q-value text labels in the plot.
#' @param ... (optional) Other arguments passed to `ggplot2::theme()`.
#'
#' @return A ggplot2 layer.
#' @export
#'
plot.factor_detector = \(x, slicenum = 2, alpha = 0.95,
keep = TRUE, qlabelsize = 3.88, ...) {
g = x$factor %>%
dplyr::select(variable, qv = `Q-statistic`,pv = `P-value`) %>%
dplyr::filter(!is.na(qv)) %>%
dplyr::mutate(variable = forcats::fct_reorder(variable, qv, .desc = TRUE)) %>%
dplyr::mutate(variable_col = c("first",rep("others",times = nrow(.)-1))) %>%
dplyr::mutate(qv_text = paste0(sprintf("%4.2f", qv * 100), "%"))
if (!keep) {
g = g %>%
dplyr::mutate(significance = dplyr::if_else(pv <= 1 - alpha,
'Significant',
'Not Significant',
NA)) %>%
dplyr::filter(significance == 'Significant')
}
fig_factor = ggplot2::ggplot(g,
ggplot2::aes(x = qv, y = variable, fill = variable_col)) +
ggplot2::geom_col() +
ggplot2::scale_x_continuous(expand = ggplot2::expansion(mult = c(0, 0.1))) +
ggplot2::scale_y_discrete(limits = rev) +
ggplot2::scale_fill_manual(breaks = c("first", "others"),
values = c("#DE3533","#808080")) +
ggplot2::geom_text(data = dplyr::slice(g, seq(1,slicenum)),
ggplot2::aes(label = qv_text), hjust = 1.25,
family = "serif", fontface = "bold", size = qlabelsize) +
ggplot2::geom_text(data = dplyr::slice(g, -seq(1,slicenum)),
ggplot2::aes(label = qv_text), hjust = -0.1,
family = "serif", fontface = "bold", size = qlabelsize) +
ggplot2::labs(x = "Q value", y = "") +
ggplot2::theme_bw() +
ggplot2::theme(panel.grid.major.y = ggplot2::element_blank(),
axis.text.y = ggplot2::element_text(family = "serif"),
axis.text.x = ggplot2::element_text(family = "serif"),
legend.position = "off", ...)
return(fig_factor)
}
#' @title plot interaction detector result
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to plot output for interaction detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param alpha (optional) Picture transparency. Default is `1`.
#' @param ... (optional) Other arguments passed to `ggplot2::theme()`.
#'
#' @return A ggplot2 layer
#' @export
#'
plot.interaction_detector = \(x,alpha = 1,...){
g = x$interaction %>%
dplyr::select(interactv = `Variable1 and Variable2 interact Q-statistics`,
dplyr::everything())
gv1 = dplyr::count(g,variable1)
gv2 = dplyr::count(g,variable2)
gv = gv1 %>%
dplyr::left_join(gv2,by = 'n') %>%
dplyr::arrange(dplyr::desc(n))
g = g %>%
dplyr::mutate(variable1 = factor(variable1,levels = gv$variable1),
variable2 = factor(variable2,levels = rev(gv$variable2)))
fig_interaction = ggplot2::ggplot(g,
ggplot2::aes(x = variable1, y = variable2,
size = interactv, color = Interaction)) +
ggplot2::geom_point(alpha = alpha) +
ggplot2::scale_size(range = c(1,10)) +
ggplot2::guides(size = ggplot2::guide_legend(
override.aes = list(shape = 21,
fill = "transparent",
color = "black"))) +
ggplot2::scale_color_manual(values = c("Enhance, nonlinear" = "#EA4848",
"Independent" = "#E08338",
"Enhance, bi-" = "#F2C55E",
"Weaken, uni-" = "#6EE9EF",
"Weaken, nonlinear" = "#558DE8")) +
ggplot2::labs(x = "", y = "", size = "", color = "") +
ggplot2::coord_fixed() +
ggplot2::theme_bw() +
ggplot2::theme(axis.text.y = ggplot2::element_text(family = "serif"),
axis.text.x = ggplot2::element_text(family = "serif"),
...)
return(fig_interaction)
}
#' @title plot risk detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to plot output for risk detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `ggplot2::theme()`.
#'
#' @return A ggplot2 layer
#' @export
#'
plot.risk_detector = \(x, ...) {
plot_rduni = \(g,gname,...){
fig_rd = ggplot2::ggplot(data = g,
ggplot2::aes(x = zone1st, y = zone2nd, fill = risk)) +
ggplot2::geom_tile(color = "white", size = 0.75) +
ggplot2::geom_text(ggplot2::aes(label = risk), color = "black") +
ggplot2::scale_fill_manual(values = c("N" = "#7fdbff", "Y" = "#ffa500")) +
ggplot2::coord_fixed() +
ggplot2::theme_minimal() +
ggplot2::theme(axis.title.x = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank(),
axis.text.x = ggplot2::element_text(angle = 60,hjust = 1,family = "serif"),
axis.text.y = ggplot2::element_text(family = "serif"),
legend.position = "none",
panel.grid = ggplot2::element_blank(), ...) +
ggplot2::annotate("text", x = Inf, y = -Inf, label = gname,
vjust = -1.75, hjust = 1.25,
color = "#ff0000", family = "serif")
return(fig_rd)
}
g = dplyr::select(x$risk,variable,zone1st,zone2nd,Risk) %>%
dplyr::mutate(risk = forcats::fct_recode(Risk,"Y" = "Yes", "N" = "No"))
zonevars = unique(g$variable)
fig_rds = purrr::map(zonevars,\(x) plot_rduni(dplyr::filter(g,variable == x)
,x,...))
# fig_p = cowplot::plot_grid(plotlist = fig_rds,
# ncol = ceiling(sqrt(length(zonevars))),
# label_fontfamily = 'serif',
# label_fontface = 'plain')
fig_p = patchwork::wrap_plots(fig_rds,
ncol = ceiling(sqrt(length(zonevars))))
return(fig_p)
}
#' @title plot ecological detector
#' @author Wenbo Lv \email{lyu.geosocial@gmail.com}
#' @description
#' S3 method to plot output for ecological detector in `geodetector()`.
#'
#' @param x Return by `geodetector()`.
#' @param ... (optional) Other arguments passed to `ggplot2::theme()`.
#'
#' @return A ggplot2 layer
#' @export
#'
plot.ecological_detector = \(x, ...) {
g = dplyr::select(x$ecological,
dplyr::all_of(c('variable1','variable2','Ecological')))
gv1 = dplyr::count(g,variable1)
gv2 = dplyr::count(g,variable2)
gv = gv1 %>%
dplyr::left_join(gv2,by = 'n') %>%
dplyr::arrange(dplyr::desc(n))
g = g %>%
dplyr::mutate(variable1 = factor(variable1,levels = gv$variable1),
variable2 = factor(variable2,levels = rev(gv$variable2)),
eco = forcats::fct_recode(Ecological,"Y" = "Yes", "N" = "No"))
fig_ed = ggplot2::ggplot(data = g,
ggplot2::aes(x = variable1, y = variable2, fill = eco)) +
ggplot2::geom_tile(color = "white", size = 0.75) +
ggplot2::geom_text(ggplot2::aes(label = eco), color = "black") +
ggplot2::scale_fill_manual(values = c("N" = "#7fdbff", "Y" = "#ffa500")) +
ggplot2::coord_fixed() +
ggplot2::theme_minimal() +
ggplot2::theme(axis.title.x = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank(),
axis.text.y = ggplot2::element_text(family = "serif"),
axis.text.x = ggplot2::element_text(family = "serif"),
legend.position = "none",
panel.grid = ggplot2::element_blank(), ...)
return(fig_ed)
}
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