R语言 secr包 predictDsurface()函数中文帮助文档(中英文对照)

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predictDsurface(secr)
predictDsurface()所属R语言包：secr

                                         Predict Density Surface
                                         预测密度表面

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Predict density at each point on a raster mask from a fitted secr model.
预测密度在每个点上的光栅掩模从一个配备秘书服务模型。


用法----------Usage----------


predictDsurface(object, mask = NULL, se.D = FALSE, cl.D = FALSE, alpha =
0.05)



参数----------Arguments----------

参数：object
fitted secr object
装秘书服务对象


参数：mask
secr mask object
秘书服务遮罩对象


参数：se.D
logical for whether to compute prediction SE
逻辑计算预测SE


参数：cl.D
logical for whether to compute confidence limits
逻辑，是否计算的置信区间


参数：alpha
alpha level for 100(1 – alpha)% confidence intervals  
α水平为100（1  - α）％置信区间


Details

详细信息----------Details----------

Predictions use the linear model for density on the link scale in the fitted secr model "object", or the fitted user-defined function, if that was specified in secr.fit.
的预测使用链路上规模的线性模型的密度秘书服务模型的拟合对象“，或用户定义的函数的拟合，如果这是指定的secr.fit的。

If "mask" is NULL then predictions are for the mask component of "object".
如果“面具”是NULL，则预测的面具是“对象”的组成部分。

SE and confidence limits are computed only if specifically requested. They are not available for user-defined density functions.
SE和置信限计算只有明确要求。他们是不是可以为用户自定义的密度函数。

Density is adjusted automatically for the number of clusters in "mashed" models (see mash).
密度的数字聚类在“泥”模式的自动调整（见mash）。


值----------Value----------

Object of class "Dsurface" inheriting from "mask". Predicted densities are added to the covariate dataframe (attribute "covariates") as column(s) with prefix "D." If the model uses multiple groups, multiple columns will be distinguished by the group name (e.g., "D.F" and "D.M"). If groups are not defined the column is named "D.0".
对象的类的Dsurface“继承自”面具“。预测密度增加的协变量数据框（属性的协变量）列（S）与前缀D.“如果模型使用多个组，将分为多个列组的名称（例如，”D F“和”D.M“）。如果没有定义组的列被命名为“D.0”。

For multi-session models the value is a multi-session mask.
对于多会话模型的值是一个多会话的面具。

The pointwise prediction SE is saved as a covariate column prefixed "SE." (or multiple columns if multiple groups). Confidence limits are likewise saved with prefixes "lcl."  and "ucl.".
逐点预测作为协变量列前缀SE SE保存。“（或多个列，如果有多个组）。置信界限也同样保存前缀“LCL”和“伦敦大学学院”。


参见----------See Also----------

plot.Dsurface, secr.fit, predict.secr
plot.Dsurface，secr.fit，predict.secr


实例----------Examples----------



## use canned possum model[＃使用罐装负鼠模型]
shorePossums <- predictDsurface(possum.model.Dsh2)
plot(shorePossums, plottype = "shaded", polycol = "blue", border = 100)
plot(traps(possumCH), detpar = list(col = "black"), add = TRUE)

## extract and summarise[＃提取和总结]
summary(covariates(shorePossums))

## Not run: [＃不运行：]

## extrapolate to a new mask; add covariate needed by model; plot[＃外推到一个新的面具，添加协变量所需要的模型;图]
regionmask <- make.mask(traps(possumCH), buffer = 1000, spacing = 10,
    poly = possumremovalarea)
dts <- distancetotrap(regionmask, possumarea)
covariates(regionmask) <- data.frame(d.to.shore = dts)
regionPossums <- predictDsurface(possum.model.Dsh2, regionmask,
    se.D = TRUE, cl.D = TRUE)
par(mfrow = c(1,2))
plot(regionPossums, plottype = "shaded", mesh = NA, breaks = 20)
plot(regionPossums, plottype = "contour", add = TRUE)
plot(regionPossums, covariate = "SE", plottype = "shaded",
    mesh = NA, breaks = 20)
plot(regionPossums, covariate = "SE", plottype = "contour",
    add = TRUE)

## confidence surfaces[＃信心面]
plot(regionPossums, covariate = "lcl", breaks = seq(0,3,0.2),
    plottype = "shaded")
plot(regionPossums, covariate = "lcl", plottype = "contour",
    add = TRUE, levels=seq(0,2.7,0.2))
title("lower 95% surface")
plot(regionPossums, covariate = "ucl", breaks=seq(0,3,0.2),
    plottype = "shaded")
plot(regionPossums, covariate = "ucl", plottype = "contour",
    add = TRUE, levels=seq(0,2.7,0.2))
title("upper 95% surface")

## annotate with CI[＃注释与CI]
par(mfrow = c(1,1))
plot(regionPossums, plottype = "shaded", mesh = NA, breaks = 20)
plot(traps(possumCH), add=T, detpar = list(col = "black"))
spotHeight(regionPossums, dec=1,pre=c("lcl","ucl"), cex=0.8)

## perspective plot[＃角度图]
pm <- plot(regionPossums, plottype = "persp", box = FALSE, zlim =
    c(0,3), phi=30, d = 5, col = "green", shade = 0.75, border = NA)
lines(trans3d (possumremovalarea$x, possumremovalarea$y,
     rep(1,nrow(possumremovalarea)), pmat = pm))

## compare estimates of region N[＃比较的区域N的估计。]
## grid cell area is 0.01 ha[＃网格单元的面积为0.01公顷]
sum(covariates(regionPossums)[,"D.0"]) * 0.01
region.N(possum.model.Dsh2, regionmask)


## End(Not run)[＃（不执行）]


转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
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