unmarkedFit-class(unmarked)
unmarkedFit-class()所属R语言包：unmarked
Class "unmarkedFit"
类“unmarkedFit”
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Contains fitted model information which can be manipulated or extracted using the methods described below.
包含拟合模型的信息，可以使用下面描述的方法处理或提取。
插槽----------Slots----------
fitType: Object of class "character"
fitType：对象类"character"的
call: Object of class "call"
call：对象类"call"的
formula: Object of class "formula"
formula：对象类"formula"的
data: Object of class "unmarkedFrame"
data：对象类"unmarkedFrame"的
sitesRemoved: Object of class "numeric"
sitesRemoved：对象类"numeric"的
estimates: Object of class "unmarkedEstimateList"
estimates：对象类"unmarkedEstimateList"的
AIC: Object of class "numeric"
AIC：对象类"numeric"的
opt: Object of class "list" containing results from
opt：对象类"list"包含结果
negLogLike: Object of class "numeric"
negLogLike：对象类"numeric"的
nllFun: Object of class "function"
nllFun：对象类"function"的
knownOcc: unmarkedFitOccu only: sites known to be occupied
knownOcc：unmarkedFitOccu唯一的站点被占用
K: unmarkedFitPCount only: upper bound used in integration
K：unmarkedFitPCount的上限整合
mixture: unmarkedFitPCount only: Mixing distribution
mixture：unmarkedFitPCount的只有：混合分布
keyfun: unmarkedFitDS only: detection function used by
keyfun：unmarkedFitDS：检测功能使用
unitsOut: unmarkedFitDS only: density units
unitsOut：unmarkedFitDS只：密度单位
方法----------Methods----------
[ signature(x = "unmarkedFit", i = "ANY", j = "ANY",
[<CODE>签名（X =“unmarkedFit”，“ANY”，J =“ANY”
backTransform signature(obj = "unmarkedFit"): back-transform
backTransformsignature(obj = "unmarkedFit")：变换
coef signature(object = "unmarkedFit"): returns parameter estimates. type can be one of names(obj), eg 'state' or 'det'.
系数signature(object = "unmarkedFit")：返回参数估计值。类型可以是一个名称（OBJ），例如“国家”或“DET”。
confint signature(object = "unmarkedFit"): Returns confidence
confint signature(object = "unmarkedFit")：返回信心
fitted signature(object = "unmarkedFit"): returns expected
装signature(object = "unmarkedFit")：预期收益
getData signature(object = "unmarkedFit"): extracts data
GETDATA signature(object = "unmarkedFit")：提取数据
getP signature(object = "unmarkedFit"): calculates and extracts
getPsignature(object = "unmarkedFit")：计算和提取
hessian signature(object = "unmarkedFit"): Returns hessian
返回麻麻signature(object = "unmarkedFit")：
linearComb signature(obj = "unmarkedFit", coefficients = "matrixOrVector"): Returns estimate and SE on original
linearComb signature(obj = "unmarkedFit", coefficients = "matrixOrVector")：返回在原有的估计和SE
mle signature(object = "unmarkedFit"): Same as coef(fit)?
MLEsignature(object = "unmarkedFit")：作为系数（FIT）相同？
names signature(x = "unmarkedFit"): Names of parameter levels
名signature(x = "unmarkedFit")：名称参数水平
nllFun signature(object = "unmarkedFit"): returns negative
nllFun signature(object = "unmarkedFit")：返回负
parboot signature(object = "unmarkedFit"): Parametric
parbootsignature(object = "unmarkedFit")：参数
plot signature(x = "unmarkedFit", y = "missing"): Plots
图signature(x = "unmarkedFit", y = "missing")：图
predict signature(object = "unmarkedFit"): Returns predictions and standard errors for original data or for covariates in a new
预测signature(object = "unmarkedFit")：返回预测和对原始数据的标准误差的协变量在一个新的
profile signature(fitted = "unmarkedFit"): used by confint
个人资料signature(fitted = "unmarkedFit")：使用confint
residuals signature(object = "unmarkedFit"): returns residuals
残差signature(object = "unmarkedFit")：返回残差
sampleSize signature(object = "unmarkedFit"): returns number
采样大小signature(object = "unmarkedFit")：返回数
SE signature(obj = "unmarkedFit"): returns standard errors
SE signature(obj = "unmarkedFit")：返回标准错误
show signature(object = "unmarkedFit"): concise results
显示signature(object = "unmarkedFit")：简洁结果
summary signature(object = "unmarkedFit"): results with more
总结signature(object = "unmarkedFit")结果更
update signature(object = "unmarkedFit"): refit model with
更新signature(object = "unmarkedFit")：改装模型
vcov signature(object = "unmarkedFit"): returns
vcov signature(object = "unmarkedFit")：返回
smoothed signature(object="unmarkedFitColExt"): Returns the smoothed trajectory from a colonization-extinction model fit. Takes additional logical argument mean which specifies
平滑signature(object="unmarkedFitColExt")：返回的平滑轨迹从定植灭绝的模型拟合。需要额外的逻辑论证意味着它指定
projected signature(object="unmarkedFitColExt"): Returns the projected trajectory from a colonization-extinction model fit. Takes additional logical argument mean which specifies
预计signature(object="unmarkedFitColExt")：返回从定植灭绝的模型拟合预测轨迹。需要额外的逻辑论证意味着它指定
logLik signature(object="unmarkedFit"):
logLiksignature(object="unmarkedFit")：
LRT signature(m1="unmarkedFit", m2="unmarkedFit"): Returns the chi-squared statistic, degrees-of-freedom, and p-value from
LRT signature(m1="unmarkedFit", m2="unmarkedFit")：返回的卡方统计，度的自由，p值
注意----------Note----------
This is a superclass with child classes for each fit type
这是一个父类与子类，为每一个合适的类型
实例----------Examples----------
showClass("unmarkedFit")
# Format removal data for multinomPois [格式移动数据multinomPois]
data(ovendata)
ovenFrame <- unmarkedFrameMPois(y = ovendata.list$data,
siteCovs = as.data.frame(scale(ovendata.list$covariates[,-1])),
type = "removal")
# Fit a couple of models[适合一对夫妇的车型]
(fm1 <- multinomPois(~ 1 ~ ufp + trba, ovenFrame))
summary(fm1)
# Apply a bunch of methods to the fitted model[一堆的方法来拟合模型]
# Look at the different parameter types[在不同的参数类型]
names(fm1)
fm1['state']
fm1['det']
# Coefficients from abundance part of the model[模型的一部分，从丰富的系数]
coef(fm1, type='state')
# Variance-covariance matrix[方差 - 协方差矩阵]
vcov(fm1, type='state')
# Confidence intervals using profiled likelihood[置信区间异形可能性]
confint(fm1, type='state', method='profile')
# Expected values[预期值]
fitted(fm1)
# Original data[原始数据]
getData(fm1)
# Detection probabilities[检测概率]
getP(fm1)
# log-likelihood[对数似然]
logLik(fm1)
# Back-transform detection probability to original scale[备份变换检测概率原有规模]
# backTransform only works on models with no covariates or [backTransform只适用于没有协变量或模型]
# in conjunction with linearComb (next example)[在与linearComb（下一个例子）]
backTransform(fm1, type ='det')
# Predicted abundance at specified covariate values[在指定的协变量值的预测丰富]
(lc <- linearComb(fm1, c(Int = 1, ufp = 0, trba = 0), type='state'))
backTransform(lc)
# Assess goodness-of-fit[评估善良的拟合]
parboot(fm1)
plot(fm1)
# Predict abundance at specified covariate values.[在指定的协变量值的预测丰富。]
newdat <- data.frame(ufp = 0, trba = seq(-1, 1, length=10))
predict(fm1, type='state', newdata=newdat)
# Number of sites in the sample[样本中的站点数]
sampleSize(fm1)
# Fit a new model without covariates[安装一个新的协变量的模型，而不]
(fmNull <- update(fm1, formula = ~1 ~1))
# Likelihood ratio test[似然比检验]
LRT(fm1, fmNull)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 13:27:23
