网站建设属于什么费,网站建设网络推广,上海营销型网站代理,商城站人工售票时间表这里写目录标题1.给出一个线性回归模型并求出因子贡献度2.biomod22.1 pseudo-absences:伪不存在点#xff08;PA#xff09;2.1.1 random2.2.2 disk2.2.3 user.defined method3.使用网格划分区域3.1 计算质心4. 完整案例1.给出一个线性回归模型并求出因子贡献度
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这里写目录标题1.给出一个线性回归模型并求出因子贡献度2.biomod22.1 pseudo-absences:伪不存在点PA2.1.1 random2.2.2 disk2.2.3 user.defined method3.使用网格划分区域3.1 计算质心4. 完整案例1.给出一个线性回归模型并求出因子贡献度
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# 线性回归模型
lm_df - data.frame(x iris$Sepal.Length,y iris$Sepal.Width)
lm_model - lm(data lm_df,y ~ x)
broom::tidy(lm_model)ggplot(data lm_df,aes(x,y))geom_point()geom_smooth(method lm, se FALSE)# 变量重要性
install.packages(vip)
install.packages(mlbench)
library(vip)
set.seed(100)
trn - as.data.frame(mlbench::mlbench.friedman1(500))
linmod - lm(y ~ .^2, data trn)
backward - step(linmod, direction backward, trace 0)
# 计算贡献度
vi(backward)# 可视化
p1 - vip(backward, num_features length(coef(backward)), geom point, horizontal FALSE)
p2 - vip(backward, num_features length(coef(backward)), geom point, horizontal FALSE, mapping aes_string(color Sign))
grid.arrange(p1, p2, nrow 1)结果展示
图像绘制
重要性结果展示
2.biomod2 首先需要安装biomod2包install.packages(“biomod2”) 最终生成的文件为individual_projections该文件夹中包括.img、.xml两种数据格式其中包括很多算法如GLM,RF,SRE,ANN,CTA,FDA,CTA等多种模型 这类似于一个集成算法集合多个模型求取模型的平均值以得出一个更好的模型。
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# 加载成都市的适量边界图后面会用到
library(mapchina)
cd_sf - mapchina::china %%dplyr::filter(Name_Perfecture 成都市) %%group_by(Name_Province) %%summarise(geometry sf::st_union(geometry)) %%ungroup()
colnames(cd_sf) # see all variable names
plot(cd_sf)#install.packages(biomod2)
library(biomod2) ?biomod2::BIOMOD_FormatingData()
# 2.1 pseudo-absences:伪不存在点PA
生成PA点的四种方法random、disk、sre、user.table
2.1.1 random
随机选择PA点
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# 1.the random method : PA are randomly selected over the studied area (excluding presence points)
library(sf)
p1_random - sf::st_sample(cd_sf,300)
ggplot()geom_sf(data cd_sf)geom_sf(data p1_random)结果展示 2.2.2 disk
# 2.the disk method : PA are randomly selected within circles around presence
# points defined by a minimum and a maximum distance values (defined in meters).
## Format Data with pseudo-absences : disk method
# myBiomodData.d - BIOMOD_FormatingData(resp.var myResp.PA,
# expl.var myExpl,
# resp.xy myRespXY,
# resp.name myRespName,
# PA.nb.rep 4,
# PA.nb.absences 500,
# PA.strategy disk,
# PA.dist.min 5,
# PA.dist.max 35) # 生成环形缓冲区pts_presence - sf::st_sample(cd_sf,300)
pts_presence#使用生成的第一个点画圆
st_buffer(pts_presence[[1]], dist 1) %% plot()
plot(pts_presence[[1]],add TRUE)结果展示
2.2.3 user.defined method
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#用户自定义
## Format Data with pseudo-absences : user.defined method
# myPAtable - data.frame(PA1 ifelse(myResp 1, TRUE, FALSE),
# PA2 ifelse(myResp 1, TRUE, FALSE))
# for (i in 1:ncol(myPAtable)) myPAtable[sample(which(myPAtable[, i] FALSE), 500), i] TRUE
# myBiomodData.u - BIOMOD_FormatingData(resp.var myResp.PA,
# expl.var myExpl,
# resp.xy myRespXY,
# resp.name myRespName,
# PA.strategy user.defined,
# PA.user.table myPAtable)pts_absence - pts_presence %% st_as_sf() %% mutate(id 1:n()) %%group_by(id) %%nest(data -id) %% mutate(circle purrr::map(.x data,.f function(x) {st_buffer(x x,dist 1)})) %% mutate(point purrr::map(.x circle,.f function(x) {st_sample(x,1)})) %% dplyr::select(point) %% unnest() %% ungroup() %% dplyr::select(-id)
pts_absence格式转换
# 将生成的点转换为数据框格式
#install.packages(sfheaders)
library(sfheaders)
pts_absence %% st_as_sf() %% sfheaders::sf_to_df() %% dplyr::select(x,y) %% mutate(label absence) %% head()3.使用网格划分区域
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# 网格划分形成栅格图像
cd_grid - cd_sf %% st_make_grid(cellsize 0.2) %%st_intersection(cd_sf) %%st_cast(MULTIPOLYGON) %%st_sf() %%mutate(cellid row_number())
plot(cd_grid)为每个网格添加标签
#devtools::install_github(yutannihilation/ggsflabel)
ggplot(data cd_grid)geom_sf()ggsflabel::geom_sf_label(aes(label cellid))theme_light()3.1 计算质心
# 计算质心
library(terra)
library(tidyterra)
library(ggplot2)bj_dem - raster(D:/Datasets/w001001.adf)
plot(bj_dem)(sp_sf - bj_dem %% calc(x .,fun function(x) ifelse(x 100,x,NA)) %% # 按属性筛选rasterToPolygons() %% st_as_sf() %% summarise(geometry st_union(geometry)) %% st_make_valid())
plot(sp_sf)centroid - st_centroid(sp_sf)ggplot()geom_spatraster(data rast(bj_dem)) scale_fill_whitebox_c(palette muted,na.value white)geom_sf(data sp_sf,alpha 0,color blue)geom_sf(data centroid,size 3,color red)4. 完整案例
# Load species occurrences (6 species available)
data(DataSpecies)
head(DataSpecies)# Select the name of the studied species
myRespName - GuloGulo# Get corresponding presence/absence data
myResp - as.numeric(DataSpecies[, myRespName])# Get corresponding XY coordinates
myRespXY - DataSpecies[, c(X_WGS84, Y_WGS84)]# Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 bio_12)
data(bioclim_current)
myExpl - terra::rast(bioclim_current)## --------------------------------------------------------------------------------
# Format Data with true absences
myBiomodData - BIOMOD_FormatingData(resp.var myResp,expl.var myExpl,resp.xy myRespXY,resp.name myRespName)
myBiomodData
summary(myBiomodData)
plot(myBiomodData)物种分布数据
