plot.vgc(zipfR)
plot.vgc()所属R语言包：zipfR
Plot Vocabulary Growth Curves (zipfR)
图词汇生长曲线（zipfR）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Plot a vocabulary growth curve (i.e., V(N) or V_m(N) against N), or a comparison of several vocabulary growth curves.
绘制一个词汇生长曲线（即，V(N)或V_m(N)对N），或几个词汇增长曲线的比较。
用法----------Usage----------
## S3 method for class 'vgc'
plot(x, y, ...,
m=NA, add.m=NULL, N0=NULL,
conf.level=.95, conf.style=c("ticks", "lines"),
log=c("", "x", "y", "xy"),
bw=zipfR.par("bw"),
xlim=NULL, ylim=NULL,
xlab="N", ylab="V(N)", legend=NULL,
main="Vocabulary Growth",
lty=NULL, lwd=NULL, col=NULL)
参数----------Arguments----------
参数：x, y, ...
one or more objects of class vgc, representing observed or expected vocabulary growth curves to be plotted
一个或多个对象类vgc，表示观察到的或预期的词汇生长曲线被绘制
参数：m
a single integer m in the range 1 … 9. If specified, graphs will be plotted for V_m(N) instead of V(N) (the default). Note that all vgc objects to be plotted must contain the necessary data in this case.
一个整数m的范围内1 … 9。如果指定，图形将被绘制为V_m(N)，而不是V(N)（默认）。请注意，要绘制的所有vgc对象必须包含在这种情况下，必要的数据。
参数：add.m
a vector of integers in the range 1 … 9. If specified, graphs for V_m(N) will be added as thin lines to the default V(N) curve, for all specified frequency classes m. This option cannot be combined with the m option above. See "Details" below.
整数的范围1 … 9的向量。如果指定，图形V_m(N)将增加一条，作为细线的默认V(N)曲线，所有指定的频度等级m。此选项不能结合m上面的选项。请参阅下面的“详细信息”。
参数：N0
if specified, draw a dashed vertical line at N=N_0, indicating the sample size where a LNRE model has been estimated (this is never done automatically)
如果指定的话，画一条垂直虚线N=N_0，表示一个LNRE模型估计的样本量（这是从来没有自动完成）
参数：log
a character string specifying the axis or axes for which logarithmic scale is to be used ("x", "y", or "xy"), similar to the log argument of plot.default. By default, both axes use linear scale (also see "Details" below).
一个字符串指定要使用对数刻度（"x"，"y"或"xy"），类似的log参数<X轴或轴>。默认情况下，两个轴的线性度（请参阅下面的“详细信息”）。
参数：conf.level
confidence level for confidence intervals around expected vocabulary growth curves (see "Details" below). The default value of .95 produces 95%-confidence intervals. Set to NA in order to suppress confidence interval markers.
预期词汇增长曲线的置信区间的置信水平（见下面的“详细信息”）。 生产.95的默认值95％置信区间。设置为NA，以抑制置信区间标记。
参数：conf.style
if "ticks", confidence intervals are indicated by vertical lines at each data point in the vgc object (default). If "lines", confidence intervals are indicated by thin curves above and below the VGC (which may be difficult to see when plotting multiple VGCs). Notice that confidence intervals might be so narrow as to be invisible in plots (one way to visualize them in such case might be to set an extremely conservative confidence level, such as .9999).
如果"ticks"，置信区间，在每个数据点的垂直线在vgc对象（默认）表示。如果"lines"，置信区间表示由薄曲线的上方和下方的VGC（这可能是难以看到绘图时多个VGCS）。请注意，置信区间是如此狭窄的图是不可见的（单程想象他们在这种情况下，可能是设置一个非常保守的置信水平，如.9999）。
参数：bw
if TRUE, draw plot in B/W style (default is the global zipfR.par setting)
如果TRUE，画在B / W风格的图（默认是全球zipfR.par）
参数：xlim, ylim
visible range on x- and y-axis. The default values are automatically determined to fit the selected data in the plot.
可见光范围在x-和y-轴。默认值是自动确定，以适应所选数据中的图。
参数：xlab, ylab
labels for the x-axis and y-axis. The default values nicely typeset mathematical expressions. The y-axis label also distinguishes between observed and expected vocabulary growth curves, as well as between V(N) and V_m(N).
的x-轴和y-轴的标签。的默认值很好地排版的数学表达式。还可以区分观察到的和预期的词汇增长曲线之间，以及V(N)和V_m(N)y轴的标签。
参数：main
a character string or expression specifying a main title for the plot
字符串或表达式，指定一个主标题为图
参数：legend
optional vector of character strings or expressions, specifying labels for a legend box, which will be drawn in the lower right-hand corner of the screen. If legend is given, its length must correspond to the number of VGCs in the plot.
可选的矢量字符的字符串或表达式，指定一个传奇框的标签，这将是在较低的屏幕的右上角绘制。如果legend，它的长度必须符合VGCS中的图。
参数：lty, lwd, col
style vectors that can be used to override the global styles defined by zipfR.par. If these vectors are specified, they must contain at least as many elements as there are VGCs in the plot: the values are not automatically recycled.
风格向量，可以用于覆盖全局样式定义的zipfR.par。如果指定了这些向量，他们必须至少包含许多元素有VGCS中的图：值不会自动回收。
Details
详细信息----------Details----------
By default, standard vocabulary growth curves are plotted for all specified vgc objects, i.e. graphs of V(N) against N. If m is specified, growth curves for hapax legomena or other frequency classes are shown instead, i.e. graphs of V_m(N) against N. In this case, all vgc objects must contain the necessary data for V_m(N).
默认情况下，标准的词汇生长曲线绘制所有指定的vgc对象，即图V(N)对N。如果m指定，生长曲线hapax legomena或其他频率类，而不是即图的V_m(N)对N。在这种情况下，所有的“vgc对象必须包含必要的数据V_m(N)。
Alternatively, the option add.m can be used to display growth curves for one or more spectrum elements in addition to the standard VGCs. These growth curves are plotted as thinner lines, otherwise matching the styles of the main curves. Since such plots can become fairly confusing and there is no finer control over the styles of the additional curves, it is generally not recommended to make use of the add.m option.
可替换地，选项add.m可以用于为一个或多个频谱中的元素除了标准VGCS显示生长曲线。这些生长曲线被绘制成较细的线，否则主曲线匹配的样式。由于这种图可以变得相当混乱，有没有更精确地控制额外的曲线的样式，它一般不建议使用add.m选项。
Confidence intervals are indicated for expected vocabulary growth curves with variance data, either by short vertical lines (conf.style="ticks", the default) or by thin curves above and below the main growth curve (conf.style="lines"). The size of the confidence intervals is controlled by the conf.level parameter (default: 95%). Set conf.level=NA in order to suppress the confidence interval indicators.
置信区间预期的词汇增长曲线方差数据显示，无论是短的垂直线（conf.style="ticks"，默认值），或由薄曲线的上方和下方是主要的增长曲线（conf.style="lines"）。置信区间的大小的控制由conf.level参数（默认值：95％）。设置conf.level=NA为了抑制的置信区间指标。
In y-logarithmic plots, data points with V(N) = 0 or V_m(N) = 0 are drawn outside the plot region (below the bottom margin) rather than skipped.
在y轴的对数图中，数据点V(N) = 0或V_m(N) = 0引起外界的图区域（下面的底部边缘），而不是跳过。
Line and point styles can be defined globally with zipfR.par. They can be overridden locally with the optional parameters lty, lwd and col, but this should only be used when absolutely necessary. In most cases, it is more advisable to change the global settings temporarily for a sequence of plots.
在全球范围内zipfR.par线和点样式可以定义。他们可以覆盖本地的可选参数lty，lwd和col，但是这应该只用在绝对必要的。在大多数情况下，它是更明智的全局设置临时改变为一个序列图。
The bw parameter is used to switch between B/W and color modes. It can also be set globally with zipfR.par.
bw参数使用B /黑白和彩色模式之间切换。它可以在全球范围内也可以设置zipfR.par。
参见----------See Also----------
vgc, lnre, lnre.vgc, plot.tfl, plot.spc, zipfR.par, zipfR.plotutils
vgc，lnre，lnre.vgc，plot.tfl，plot.spc，zipfR.par，zipfR.plotutils
实例----------Examples----------
## load Our Mutual Friend spectrum and empirical vgc[＃加载我们共同的朋友谱和经验VGC]
data(DickensOurMutualFriend.emp.vgc)
data(DickensOurMutualFriend.spc)
## plot empirical V and V1 growth[＃图的经验V和V1增长的]
plot(DickensOurMutualFriend.emp.vgc,add.m=1)
## use log scale for y-axis[使用log规模为y轴]
plot(DickensOurMutualFriend.emp.vgc,add.m=1,log="y")
## binomially interpolated vgc at same points as[＃二项式相同的点插值VGC]
## empirical vgc[＃经验VGC]
omf.bin.vgc <- vgc.interp(DickensOurMutualFriend.spc,N(DickensOurMutualFriend.emp.vgc))
## compare empirical and interpolated vgc, also with[＃比较经验和插值VGC，也]
## thinner lines, and in black and white[＃稀释剂线，并在黑色和白色]
plot(DickensOurMutualFriend.emp.vgc,omf.bin.vgc,legend=c("observed","interpolated"))
plot(DickensOurMutualFriend.emp.vgc,omf.bin.vgc,legend=c("observed","interpolated"),lwd=c(1,1))
plot(DickensOurMutualFriend.emp.vgc,omf.bin.vgc,legend=c("observed","interpolated"),bw=TRUE)
## load Great Expectations spectrum and use it to[＃加载远大前程频谱，并用它来]
## compute ZM model[＃计算ZM模型]
data(DickensGreatExpectations.spc)
ge.zm <- lnre("zm",DickensGreatExpectations.spc)
## expected V of Great Expectations at sample[＃V的“远大前程”在样品]
## sizes of OMF's interpolated vgc[＃大小的OMF的插值VGC]
ge.zm.vgc <- lnre.vgc(ge.zm,N(omf.bin.vgc))
## compare interpolated OMF Vs and inter/extra-polated[＃比较OMF与内插间/额外的插入]
## GE Vs, with a vertical line at sample size[＃GE Vs时，样本大小的一条垂直线，在与]
## used to compute GE model[，＃用来计算GE模型]
plot(omf.bin.vgc,ge.zm.vgc,N0=N(ge.zm),legend=c("OMF","GE"))
## load Italian ultra- prefix data and compute zm model[＃加载意大利超前缀的数据和计算ZM模型]
data(ItaUltra.spc)
ultra.zm <- lnre("zm",ItaUltra.spc)
## compute vgc up to about twice the sample size[＃计算VGC大约两倍的样本大小]
## with variance of V[与方差的V＃]
ultra.zm.vgc <- lnre.vgc(ultra.zm,(1:100)*70, variances=TRUE)
## plot with confidence intervals derived from variance in[＃图来自方差的置信区间]
## vgc (with larger datasets, ci will typically be almost[＃VGC（大的数据集，词通常会几乎]
## invisible)[＃隐形）]
plot(ultra.zm.vgc)
## use more conservative confidence level, and plot [＃使用更保守的置信水平，和图]
## the intervals as lines[＃间隔线]
plot(ultra.zm.vgc,conf.level=.99,conf.style="lines")
## suppress ci plotting, and insert different title and labels[＃抑制CI策划，并插入不同的标题和标签]
plot(ultra.zm.vgc,conf.level=NA,main="ultra-",xlab="sample sizes",ylab="types")
## load Brown adjective spectrum[＃加载布朗的形容词频谱]
## (about 80k tokens) [（约80K记号）]
data(BrownAdj.spc)
## binomially interpolated curve of V and V_1 to V_5[＃二项式拟合曲线的V和V_1 V_5]
BrownAdj.bin.vgc <- vgc.interp(BrownAdj.spc,(1:100)*800,m.max=5)
## plot with V and 5 spectrum elements[＃图V和5光谱元素]
plot(BrownAdj.bin.vgc,add.m=c(1:5))
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。
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发表于 2012-10-2 07:48:59
