稿件被 Geoderma(影响因子1.898)拒了，建议修改后发表在一个应用性的 期刊上。希望有专家能指点一二，感激不尽。 注：PSD指particle-size distribution土壤粒径分布 本文所指PSD模型是指针对PSD发展的各种回归函数 几个关键的问题 (下面的红字)： 1， 审稿人 2指出我没有引用的一篇文章（我没有看过），尽管是1997年 发表的，但密切相关。因此他认为我的文章没有新意，不宜发表。估计没有仔细 看文章了。而这篇文章提出的两个自然指数和幂指数模型仍是基于内 插的（有3-1mm）的数据，与我所引的两项中国人Zhu and Fan, 1999; Cai et al, 2003的研究类似，而本研究是使用分形概念 进行外插,问题不尽相同（是否能使用是下面第二点的问题）。 Reviewer #2 finds the manuscript unacceptable first because the scientific content is close to a non-quoted paper previously published by Rousseva,1997. If the authors have read the paper, then they should not have repeat the same idea again just using a different database and different empirical models. This paper offers nothing new and such empirical comparisons using various models do not offer much insight into the development of the particle-size ditribution theory 2，编辑和审稿人1提出模型间经验假设间的冲突。我也不大明白不同假设 的模型是否能同时用于描述一条曲线（现象），即统计学中的不同区间的 假设不同（幂指数和非幂指数）。同时使用不同假设的模型真的不行吗？ （用分形概念做外插，用PSD模型做内插） 这一点是本研究的 命门 了。（救命啊～～，或者就死掉好了） I am concerned about the use of a fractal approach to extrapolate data, since a fractal model is associated with PSD described by power-laws, at least on sub- domains, and most of the PSD models that you use contradict this power-law assumption. the author mixes two distinct models to achieve the conversion （审稿人 1） 3，审稿人1对这方面的研究不熟悉，但审稿意见很细致。他/她指出 在转换两个不同系统之间的转换应考虑不同质地分类系统的分级特点。 当然他/她也明白我所用的方法与他举出的例子存在方法上的不同，但 仍认为需要有所讨论。 The above premise is sound but the conversion between two different systems was previously shown to require the consideration of combined texture triangle diagram and the related separate limits in each system. Although there are some differences in fitting the data and in summarizing the PSD to its mean and standard deviation in the above references, the problem needs some discussion 4，另一点也可以很明显的看出，审稿人1对这方面研究是不熟悉的。 因为先估计土壤质地分布，并用它作为输入得到其他土壤水力属性的 研究很多。这样做主要是因为测其他属性花费高。但这种常识性的知识 （包括他/她提到的一些其他疑问），我在文章是没有必要详细阐明的。 I have a basic question relative to fitting data to a model then using the model in, say, hydraulic properties of a soil. Why not use the observed data directly? Can you better develop the need for this process? Are there examples to use. 附全文： COMMENTS FROM EDITORS AND REVIEWERS Dear *** *** ***, I very much regret to inform you that your manuscript is not recommended for publication in our journal. This decision is based on the following expert reviewers, which I hope you will find useful. Reviewer #2 finds the manuscript unacceptable first because the scientific content is close to a non-quoted paper previously published by Rousseva,1997. I suggest you to read this paper, to quote it of course, and to improve your own manuscript taking into account this paper and Reviewer #1's comments, in order to publish your specific study in a more practical journal . I would add some personal comments: 1) I consider that good fitting of a soil PSD model does not mean good estimation of the soil hydraulic properties (cf. your conclusive sentence). This depends if there exists some sound relationship between the parameters of the PSD model and the parameters of the hydraulic properties. This might be the case with the Van Genuchten mode, since an explicit link is theoretically proposed between the n, m parameters and a parameterization of the hydraulic properties, but this is not the case of all the models you tested. 2) I am concerned about the use of a fractal approach to extrapolate data, since a fractal model is associated with PSD described by power-laws, at least on sub- domains, and most of the PSD models that you use contradict this power-law assumption. 3) In a Geoderma paper, we would avoid some basic definition as Eq.1 for R2, since this is well known by the scientist audience. Depending on the type of audience you expect if you publish your research report in a more applied journal as I recommend you, you can decide to keep this definition. With my best regards Dr Edith Perrier Joint Editor-in-Chief Geoderma Reviewer #1: Review of GEODER4664,An investigation of soil particle-size distribution models for the conversion of soil texture classification from ISSS and Kachinsky to USDA System by *** *** GENERAL COMMENTS The author fits three sets of China soil data to as many as ten models and compares the performance of each model with the data. The majority of the observed data are expressed in the ISSS system of classification with the upper cutoff limits for the separates in the sand, silt, and clay placed, respectively, at 2, .02, and .002 mm. The other two sets have the upper limits for the sand separate placed at 1 mm with mixed limits for other separates. These two sets are in Kachinsky system of classifications. The expressed objective of the manuscript is to convert data from these three systems to equivalent textures in the USDA system of classification with the upper cut off limits for the separates in the sand, silt, and clay placed, respectively, at 2, .05, and .002 mm. Although the author does not describe the soil texture triangle for Kachinsky system of classifications, with which this reviewer has no familiarity, the paper may be reviewed without that information (see below). Although not specifically stated, the premise of the conversion to the USDA system of classification is that a model that fits the PSD of one data system with predetermined separate limits can be used to describe the PSD of another system with different separate limits. On this basis, the author follows routine procedures to fit the data to several previously used models and obtains his results. The above premise is sound but the conversion between two different systems was previously shown to require the consideration of combined texture triangle diagram and the related separate limits in each system. For example, Shirazi, et al. (2001), also referenced by the author, showed that texture conversion from the ISSS system to the USDA system is always possible, but the reverse conversion is limited on the basis of the equality of the geometric mean and the geometric standard deviation of the whole sample (Plate 2C). Those authors explored the problem in more detail in Shirazi and Boersma (Iranian Journal of Science and technology, Transaction B, Vol. 25:600-708, 2001). Although there are some differences in fitting the data and in summarizing the PSD to its mean and standard deviation in the above references, the problem needs some discussion, particularly with respect to Kachinsky system of classification. Strictly, there may exit no conversion between Kachinsky system of classification and the USDA system because of the missing upper limit in the sand separate in Kachinsky system of classification. By using the fractal calculation to extrapolate the sand separate and one of the ten models for the silt and clay separates, the author mixes two distinct models to achieve the conversion. It is not clear to this reviewer that the new PSD is a real sample representing a specific China soil sample. Please explain. I have a basic question relative to fitting data to a model then using the model in, say, hydraulic properties of a soil. Why not use the observed data directly? Can you better develop the need for this process? Are there examples to use. SPECIFIC COMMENTS This reviewer had difficulty understanding the meaning of %Unchanged in Table 3. The author previously stated that the heading implies stability (line 272). Please explain. The descriptions Number of cases for PSD on Tables 3 to 5 are not clear. Please make a statement that better explains the connection between the numbers in the tables and the goodness of fit, or generality of the model relative to data sets. Also show the connection between figures and tables. Why is there different number of models listed in the Tables and in the figures? Some are ten and others are eight. What do you want to show by Tables 4(b, c) and 5 (a b). I am not certain if the question on lines 105-107 can be answered affirmatively. Please change attributions on line 49 to properties. Please explain the sentence at the start of line 123. Please change For on line 126 to because. Please remove line 171 and begin the sentence with line 172. Reviewer #2: The author obviously did not read or forget to cite the paper by Rouseva who has studied carefully the conversion between the Katchinski, ISSS and USDA system. Rousseva, S.S., 1997. Data transformations between soil texture schemes. European Journal of Soil Science 48, 749-758. If the authors have read the paper, then they should not have repeat the same idea again just using a different database and different empirical models. This paper offers nothing new and such empirical comparisons using various models do not offer much insight into the development of the particle-size ditribution theory
R Code for CRW simulation #copy and paste the following code in R #to simulate Correlated Random Walk in an open space #Original code by Xiaohua Dai #required libraries require(circular) require(CircStats) ##CRW initial parameters #length ~ gamma distribution (sh, sc) #For a gamma distribution: gamma(shape, scale) # mean = shape*scale # variance = shape*scale*scale #Then, scale = variance/mean, shape = mean/scale #shape parameter: sh = 0.285 #scale parameter: sc = 362 #turning angle ~ wrapped cauchy distribution (m, rh, s) #mean turning angle in radians: m = 0.145 #mean resultant length rho: rh = 0.356 #square displacements R = matrix(0,1000,25) #x,y coordinates x = matrix(0,1000,25) y = matrix(0,1000,25) #turning angles the = matrix(0,1000,25) #lower 2.5% CI of R r25 = matrix(0,25) #mean of R rm = matrix(0,25) #upper 2.5% CI of R r975 = matrix(0,25) #Start simulation; sim = times of simulation for(sim in 1:1000){ for(step in 2:25){ l - rgamma(1,shape=sh,scale=sc) ta - rwrappedcauchy(1,mu=m,rho=rh) the = the +ta x = x +l*cos(the ) y = y +l*sin(the ) R = x ^2+y ^2 } } for(step in 1:25){ r25 = sort(R ) rm = mean(R ) r975 = sort(R ) } #output write.table(data.frame(r25,rm,r975),CRWoutput.txt) write.csv(data.frame(r25,rm,r975),CRWoutput.csv) Wednesday July 5, 2006 - 11:15am (EEST) Permanent Link | 0 Comments
R code for grid-based movement simulation Grid size: 1km 1km square Initial Agent: Individual animal Local movements: Habitat selection index H i (according to the percentage levels of utilization distribution, UD i , incell i ): ## H could be also determined according to the habitat quality, prey density, etc. Time step: 0.5hr At time step t : agent atcell m (center coordinate = ( x t , y t )) When t +1 the agent move to (or stay at) one of the nine cells ( n = m -4, , m +4) as follows ( x t -1, y t -1) ( x t , y t -1) ( x t +1, y t -1) ( x t -1, y t ) ( x t , y t ) ( x t +1, y t ) ( x t -1, y t +1) ( x t , y t +1) ( x t +1, y t +1) Possibility ( p ) of moving to/staying atcell n is P n = H n / SUM ( H i ), i from m- 4 to m +4. #####Here's the R script to simulate animal movement###### #Original code by Xiaohua Dai # Required R packages require(adehabitat) require(car) require(spdep) ## Initial parameters # Location time series (x,y) # time = number of time steps time - 15000 x - array(0,time) y - array(0,time) # Number of animal occurences at location x,y: location # Grid map of Kruger # (NOTE: zero-value grids buffer around its border: # 1. to make the grid contains NRow * NCol cells # 2. to ensure each cell in Kruger has 8 neighbouring cells) location - image.asc(Kruger) # The values of habitat selection index H decrease with the increasing of utilization level # H = 0 when the cells are not in home range therefore elephants wont move to the cells H - location UD - image.asc(KrugerUD) H - round(100/UD) BB - array(H) neigh - cell2nb(NRow,NCol,torus=FALSE,type=queen) # Generate 8 neigHours for each cell image(as.asc(H)) # Display the grid space of habitats # Location coordinates (lx, ly) # Use lxy to combine lx and ly together as a data frame lx - rep(1:NRow, NCol) # e.g. 123412341234 ly - rep(1:NCol, each=NRow) # e.g. 111122223333 lxy - data.frame(lx,ly) # Initial location of animal loc - round(runif(1,min=1,max=length(lx))) ##Movement simulation for(t in 1:time){ # Record location time series x - lxy$lx y - lxy$ly # Draw location point points(lxy$lx ,lxy$ly , col = round(runif(1, max=10)), pch = 19) # 9-cell neigHourhood matrix of habitat selection # Repeat the number of k according to its selection level BB ] # Previous cell also included since animal have a certain probability to stay in it. cxy - rep(loc,BB ) for(i in 1:8) { k - neigh ] #8 neigHouring cells cxy - c(cxy, rep(k,BB )) } # Sample one value in the selection array cxy # The larger BB ] is, the higer probability for the animal to move to cell k # Move to the new location and add 1 to the number of animal occurence at loc loc - some(cxy,1) location - location +1 }# Simulate the next move Wednesday July 5, 2006 - 11:22am (EEST) Permanent Link | 0 Comments
R code to simulate animal movement in a torus # Original code by Xiaohua Dai # Required R packages require(adehabitat) require(car) require(spdep) ## Initial parameters # Location time series (x,y) # time = number of time steps time - 15000 x - array(0,time) y - array(0,time) # Number of animal occurences at location x,y: location # location - round(runif(length(HB),min=1,max=3)) BB - array(HB) neigh - cell2nb(CellN,CellN,torus=TRUE,type=queen) # Generate 8 neighbours for each cell image(as.asc(HB)) # Display the grid space of habitats # Location coordinates (lx, ly) # Use lxy to combine lx and ly together as a data frame lx - rep(1:CellN, CellN) ly - rep(1:CellN, each=CellN) lxy - data.frame(lx,ly) # Initial location of animal loc - round(runif(1,min=1,max=length(lx))) ##Movement simulation for(t in 1:time){ # Record location time series x - lxy$lx y - lxy$ly # Draw location point points(lxy$lx ,lxy$ly , col = round(runif(1, max=10)), pch = 19) # 9-cell neighbourhood matrix of habitat selection cxy - loc for(i in 1:8) { k - neigh ] #8 neighbouring cells in a torus # Repeat the number of k according to its preference degree BB ] # Previous cell also included since animal have a certain probability to stay in it. cxy - c(cxy, rep(k,BB )) } # Sample one value in the selection array cxy # The larger BB ] is, the higer probability for the animal to move to cell k # Move to the new location and add 1 to the number of animal occurence at loc loc - some(cxy,1) location - location +1 }# Simulate the next move ## Estimation of Kernel Home-Range with 25%, 50% and 95% percentage # for home range contour estimation xy - data.frame(x,y) ud - kernelUD(xy) ver - getverticeshr(ud, 95) plot(ver, add=TRUE) ver - getverticeshr(ud, 50) plot(ver, add=TRUE) ver - getverticeshr(ud, 25) plot(ver, add=TRUE) Wednesday July 5, 2006 - 11:23am (EEST) Permanent Link | 0 Comments
R code to generate convex hulls around point clusters #Original code by Roger Bivand #Modified by Xiaohua Dai require(maptools) require(sp) require(amap) require(shapefiles) #reading point shape foodloc - readShapePoints(foodtree.shp) # yourloc - readShapePoints(yourshape.shp) xy - coordinates(foodloc) xy_clusts - hcluster(xy, method=euclidean, link=complete) # hcluster use twice less memory, as it doesn't store distance matrix # complete linkage hierarchical clustering plot(xy_clusts) # shows the clustering tree cl - cutree(xy_clusts, 200) # 200 is the number of clusters which_cl - tapply(1:nrow(xy), cl, function(i) xy ) chulls_cl - lapply(which_cl, function(x) x ) plot(xy) res - lapply(chulls_cl, polygon) n - length(chulls_cl) polygons - lapply(1:n, function(i) { chulls_cl ] - rbind(chulls_cl ], chulls_cl ] ) # the convex hulls do not join first and last points, so we copy here Polygons(list(Polygon(coords=chulls_cl ])), ID=i) }) out - SpatialPolygonsDataFrame(SpatialPolygons(polygons), data=data.frame(ID=1:n)) plot(out) # note standard-violating intersecting polygons! tempfile - tempfile() writePolyShape(out, tempfile) in_again - readShapePoly(tempfile) plot(in_again, border=blue, add=TRUE) #output test - read.shapefile(tempfile) write.shapefile(test,ptcluster) #Refer to: #http://www.google.com/search?hl=zh-CNq=%22outline+polygons+of+point+clumps%22+r-projectbtnG=Google+%E6%90%9C%E7%B4%A2lr= Wednesday July 5, 2006 - 12:34pm (EEST) Permanent Link | 0 Comments
LetsR来用R entomology 发表于 2005-6-16 17:27:00 Lets R 来用 R In bilingual English-Chinese What is R? R 是什么？ *R is not only a programming language; R is also a graphic statistical environment withplenty of easily-loaded packages. (I like it, same as theeasy-to-useextensions for ArcView) R 是程序语言， R 是具有大量易装载功能包的图形统计环境。我喜欢这点，如同 ArcView 中使用方便的扩展部件。 How to R? 怎么用 R *You can write your own scripts, you can also call a large number of powerful functions. 你可以自己写脚本，也可以调用大量有用函数。 Why to R? 为什么R * You can run R on UNIX, Windows and Mac OS R 可以运行于 UNIX, Windows 和 Mac 操作系统 * R is free: free of charge and free to use 你可以免费和自由的使用 R * R is a combination of functional programming and object-oriented programming R 是函数型程序设计与面向对象程序设计的综合体 * You need not to be a programmer; you can quickly be a programmer 你不必是程序员；你能够很快地成为程序员 * Many R users and big name statisticians around the world will answer your questions in maillists 你可以通过邮件列表向为数众多的 R 使用者和统计牛人咨询问题 * Where is R? R 在哪里 * Home page: http://www.R-project.org/ and many mirrors 主页与镜像 * Useful m ini-course for beginners: http://life.bio.sunysb.edu/~dstoebel/R/ 初学者快速入门教程 * R introduction in Chinese: http://www.biosino.org/pages/newhtm/r/schtml/ 中文 R 导论 * R resources for ecologists: http:// c r an. r -p r oject.o r g/web/ views /Envi r onmet r ics.html 生态学家的 R 资源 * Last update 2000.06.16 Xiaohua Dai @ ecoinformatics.blog.edu.cn 搜索引擎关键词： 统计软件R， R中文， 中文R， R语言
GIS-relatedpackagesinR entomology 发表于 2005-7-8 20:39:00 GIS-related packages in R: ade4 -- Analysis of Environmental Data : Exploratory and Euclidean methods in Environmental sciences adehabitat -- Analysis of habitat selection by animals fields -- Tools for spatial data GRASS -- Interface between GRASS 5.0 geographical information system and R Mapdata -- Extra Map Databases Mapproj -- Map Projections Maps -- Draw Geographical Maps Maptools -- tools for reading and handling shapefiles Maptree -- Mapping, pruning, and graphing tree models PBSmapping -- PBS Mapping 2 Shapefiles -- Read and Write ESRI Shapefiles Sp -- classes and methods for spatial data Spatial -- Functions for Kriging and Point Pattern Analysis Spatstat -- Spatial Point Pattern analysis, model-fitting and simulation Spdep -- Spatial dependence: weighting schemes, statistics and models etc.
R常见工具和网站 entomology发表于-2008-7-26 20:02:00 0 推荐 这是我学R几年来觉得最有用的工具和网站，先写一部分，以后想起来慢慢补充。 1 R Task Views --to install packages for a special task. 用于特定专业研究的包组合: http://cran.r-project.org/web/views/ 如生态学的 http://cran.r-project.org/web/views/Environmetrics.html 2 R Reference Card--as a printed guideat hand, just several pages, but many useful hints.R参考手册，只有几页，最简单的只有一页，可以打印出来随时参考： （1）一页版英文： http://cran.r-project.org/doc/contrib/Short-refcard.pdf （2）多页版英文： http://cran.r-project.org/doc/contrib/refcard.pdf （3）多页版中文： http://cran.r-project.org/doc/contrib/Liu-R-refcard.pdf 3 Tinn-R--to make the use of R easier in a graphic interface. 图形界面的R编辑器： http://sourceforge.net/projects/tinn-r 4 Rcmdr--R GUI inteface.R的GUI界面套件： http:// cran.r-project.org/web/packages/ Rcmdr /index.html http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/ 5 升级包的时候可以选择韩国的服务器，速度快，而且更新要比国内快得多。
标签: 统计
