R 2.2.0 - en to zh_CN translation catalogue (688 entries)

en: models are not all fitted to the same number of observations
zh_CN: 模型拟合时没有用同样大小的数据集

en: empty model supplied
zh_CN: 提供的模型是空的

en: 'acf' must be of length two or more
zh_CN: 'acf'的长度必需是一或二

en: object not interpretable as a factor
zh_CN: 不能把对象解释成因子

en: cannot fit models without level ('alpha' must not be 0 or FALSE)
zh_CN: 无法拟合没有水平的模型('alpha'不能为0或FALSE)

en: 'alpha', 'beta' and 'gamma' must be within the unit interval
zh_CN: 'alpha', 'beta'和'gamma'必需在单位区间内

en: data must be non-zero for multiplicative Holt-Winters
zh_CN: 乘法Holt-Winters的数据不能是零

en: need at least 2 periods to compute seasonal start values
zh_CN: 计算季节性初始值至少需要两个周期的数据

en: invalid length(x)
zh_CN: length(x)无效

en: optimization difficulties: %s
zh_CN: 优化难度：%s

en: optimization failure
zh_CN: 优化失败

en: time series has no or less than 2 periods
zh_CN: 时间序列的周期不能小于二

en: the series is entirely NA
zh_CN: 时间序列里所有的值都是NA

en: frequency must be a positive integer >= 2 for BSM
zh_CN: 用BSM时頻率必需是大于等于二的正整数

en: only implemented for univariate time series
zh_CN: 只适用于单变量时间序列

en: 'x' must be numeric
zh_CN: 'x'必需为数值

en: the first value of the time series must not be missing
zh_CN: 时间序列的第一个数值不能是缺失值

en: all parameters were fixed
zh_CN: 所有参数值都是固定的

en: possible convergence problem: 'optim' gave code = %d and message %s
zh_CN: 可能遇到了收敛问题：'optim'提供了code=%d和信息%s

en: no factors in the fitted model
zh_CN: 拟合模型里没有因子

en: 'which' specified no factors
zh_CN: 'which'没有指定因子

en: 'which' specified some non-factors which will be dropped
zh_CN: 'which'内的非因子忽略不用

en: 'sampleT' and 'nser' must be integer
zh_CN: 'sampleT'和'nser'必需都是整数

en: 'lag.max' must be at least 0
zh_CN: 'lag.max'至少必需为零

en: 'lag.max' must be at least 1
zh_CN: 'lag.max'至少必需为一

en: NAs in 'x'
zh_CN: 'x'里不能有NA

en: x$lag must have at least 1 column
zh_CN: x$lag至少需要有一列

en: can use ci.type="ma" only if first lag is 0
zh_CN: 只有在第一个滞后为0时才能用ci.type="ma"

en: univariate time series only
zh_CN: 只适用于单变量时间序列

en: no terms in scope
zh_CN: 范围里没有项

en: no terms in scope for adding to object
zh_CN: 加入对象时范围内没有项

en: number of rows in use has changed: remove missing values?
zh_CN: 行数有变化：是不是删除了缺失值?

en: attempting model selection on an essentially perfect fit is nonsense
zh_CN: 对几乎是完全拟合再进行模型选择是没有用的

en: F test assumes quasi%s family
zh_CN: F检验假設quasi%s分布族

en: no 'add1' method implemented for "mlm" models
zh_CN: "mlm"模型还没有实现'add1'方法

en: scope is not a subset of term labels
zh_CN: 范围不是项标记集的子集

en: no 'drop1' method for "mlm" models
zh_CN: "mlm" 模型没有'drop1'方法

en: F test assumes 'quasi%s' family
zh_CN: F检验假設'quasi%s'分布族

en: AIC is not defined for this model, so 'step' cannot proceed
zh_CN: 这个模型没有AIC定义，因此不能用'step'这一功能

en: AIC is -infinity for this model, so 'step' cannot proceed
zh_CN: 这个模型AIC的值是负无限的，因此'step'不能继续进行

en: 'A' must be an array or table
zh_CN: 'A'必需是矩阵或是数据框

en: length of FUN, %d,
     does not match the length of the margins, %d
zh_CN: FUN的长度, %d,
    与边MARGIN的长度, %d不相符

en: no rows to aggregate
zh_CN: 没有可用来聚合的行

en: 'by' must be a list
zh_CN: 'by'必需是列表

en: arguments must have same length
zh_CN: 参数的长度必需一样

en: cannot change frequency from %g to %g
zh_CN: 无法将頻率从%g改到%g

en: 'x' must be coefficient matrix/data frame
zh_CN: 'x'必需是系数矩阵/数据框

en: option "show.coef.Pvalues" is invalid: assuming TRUE
zh_CN: 选项"show.coef.Pvalues"无效: 假设成TRUE

en: 'P.values' is TRUE, but 'has.Pvalue' is not
zh_CN: 'P.values'值是TRUE, 但'has.Pvalue'不是

en: wrong k / cs.ind
zh_CN: k/cs.ind有错误

en: option "show.signif.stars" is invalid: assuming TRUE
zh_CN: 选项"show.signif.stars"无效: 假设成TRUE

en: 'anova' object must have colnames
zh_CN: 'anova'对象必需有列名

en: 'conf.level' must be a single number between 0 and 1
zh_CN: 'conf.level'必需是一个数值在零到一之间的数字

en: not enough 'x' observations
zh_CN: 'x'观察值数量不足

en: not enough 'y' observations
zh_CN: 'y'观察值数量不足

en: samples differ in location: cannot compute confidence set, returning NA
zh_CN: 样本的位置不一样：无法计算置信集, 因此返回NA

en: cannot compute confidence set, returning NA
zh_CN: 无法计算置信集, 返回NA

en: cannot compute asymptotic confidence set or estimator
zh_CN: 无法计算渐近置信集或估计量

en: cannot compute estimate, returning NA
zh_CN: 无法计算估计量, 返回NA

en: cannot compute exact p-value with ties
zh_CN: 无法精确计算带连结的p值

en: cannot compute exact confidence interval with ties
zh_CN: 有连结时无法计算精确的置信区间

en: 'formula' missing or incorrect
zh_CN: 缺失'formula'或有错误

en: grouping factor must have exactly 2 levels
zh_CN: 分组因子只能有两层

en: weights are not supported in a multistratum aov() fit
zh_CN: 不支持加权多层aov()拟合

en: Error() model is singular
zh_CN: Error()模型奇异

en: the 'split' argument must be a list
zh_CN: 'split'参数必需是列表

en: 'coef' must define a contrast, i.e., sum to 0
zh_CN: 'coef'必需设定对比, 如, 总和为零

en: 'coef' must have same length as 'contrast.obj'
zh_CN: 'coef'的长度必需同'contrast.obj'长度一样

en: each element of '%s' must be logical
zh_CN: '%s'的所有单元都必需是逻辑值

en: the contrast defined is empty (has no TRUE elements)
zh_CN: 设定的对比是空的(不含任何TRUE值)

en: columns of 'contrast.obj' must define a contrast (sum to zero)
zh_CN: 'contrast.obj'的列必需设定成对比(总和为零)

en: no degrees of freedom for residuals
zh_CN: 残差没有自由度

en: 'object' does not include an error 'qr' component
zh_CN: 'object'不包含误差'qr'组件

en: Refitting model to allow projection
zh_CN: 重新拟合模型以提供投影

en: columns of 'contrast.obj' must define a contrast(sum to zero)
zh_CN: 'contrast.obj'列必需设定对比(总和为零)

en: collapsing to unique 'x' values
zh_CN: 折拢'x'成相互不同的值

en: invalid interpolation method
zh_CN: 内插方法无效

en: need at least two non-NA values to interpolate
zh_CN: 调用内插至少需要两个非NA值的数据

en: zero non-NA points
zh_CN: 没有非NA数据点

en: 'approx' requires n >= 1
zh_CN: 'approx'需要n大于等于一

en: 'order.max' must be >= 1
zh_CN: 'order.max'必需大于等于一

en: 'n.ahead' must be at least 1
zh_CN: 'n.ahead'至少必需为一

en: number of series in 'object' and 'newdata' do not match
zh_CN: 'object'和'newdata'里的序列数目不同

en: 'se.fit' not yet implemented for multivariate models
zh_CN: 'se.fit'没有为多变量的模型实现

en: MLE only implemented for univariate series
zh_CN: MLE只为单变量序列实现

en: 'order.max' must be >= 0
zh_CN: 'order.max'必需大于等于零

en: 'order.max' must be < 'n.used'
zh_CN: 'order.max'必需小于'n.used'

en: 'order' must be a non-negative numeric vector of length 3
zh_CN: 'order'必需是长度为三的非负数值向量

en: 'seasonal' must be a list with component 'order'
zh_CN: 'seasonal'必需是带'order'组件的列表

en: 'seasonal$order' must be a non-negative numeric vector of length 3
zh_CN: 'seasonal$order'必需是长度为三的非负数值向量

en: lengths of 'x' and 'xreg' do not match
zh_CN: 'x'和'xreg'的长度不一样

en: wrong length for 'fixed'
zh_CN: 'fixed'长度有误

en: some AR parameters were fixed: setting transform.pars = FALSE
zh_CN: 一些AR参数是固定的：把transform.pars设成FALSE

en: too few non-missing observations
zh_CN: 非缺失的观察量数目不足

en: 'init' is of the wrong length
zh_CN: 'init'长度有误

en: non-stationary AR part
zh_CN: 非平穩AR部分

en: non-stationary seasonal AR part
zh_CN: 非平穩季节性AR部分

en: possible convergence problem: optim gave code = %d
zh_CN: 可能遇到了收敛问题：optim信息code=%d

en: non-stationary AR part from CSS
zh_CN: CSS里有非平穩的AR部分

en: non-stationary seasonal AR part from CSS
zh_CN: CSS里有非平穩季节性的AR部分

en: 'xreg' and 'newxreg' have different numbers of columns
zh_CN: 'xreg'和'newxreg'有不同数目的列

en: MA part of model is not invertible
zh_CN: 模型的MA部分非可逆

en: seasonal MA part of model is not invertible
zh_CN: 模型季节性的MA部分非可逆

en: converting non-invertible initial MA values
zh_CN: 转换非可逆的MA初始值

en: some ARMA parameters were fixed: setting transform.pars = FALSE
zh_CN: 一些ARMA参数是固定的：把transform.pars设成FALSE

en: 'xreg' is collinear
zh_CN: 'xreg'是共线的

en: NAs present: setting 'delta' to -1
zh_CN: 出现了NA：请把'delta'设成-1

en: transformed ARMA parameters were fixed
zh_CN: 变换过的ARMA参数是固定的

en: need at least 2 data points
zh_CN: 至少需要两个数据点

en: invalid 'x'
zh_CN: 'x'无效

en: invalid 'nb'
zh_CN: 'nb'无效

en: no solution in the specified range of bandwidths
zh_CN: 在设定的頻寬值域内找不到解答

en: increasing bw.SJ() search interval (%d) to [%.4g,%.4g]
zh_CN: 增加bw.SJ()的搜索间隔(%d) 至[%.4g,%.4g]

en: minimum occurred at one end of the range
zh_CN: 最小值存在于值域的边界上

en: 'x' must be a list with at least 2 elements
zh_CN: 'x'必需是至少有两个单元的列表

en: 'x' and 'g' must have the same length
zh_CN: 'x'和'g'的长度必需相同

en: all observations are in the same group
zh_CN: 所有的观察量都属于同一个组

en: there must be at least 2 observations in each group
zh_CN: 每组至少需要有两个观察量

en: 'x' must be nonnegative and integer
zh_CN: 'x'必需是非负的整数

en: 'n' must be a positive integer >= 'x'
zh_CN: 'n'必需是大于等于'x'的正整数

en: incorrect length of 'x'
zh_CN: 'x'长度不正确

en: 'p' must be a single number between 0 and 1
zh_CN: 'p'必需是在零到一之间的数值

en: length of choices must be 2
zh_CN: 选择长度必需为二

en: object '%s' has no scores
zh_CN: 对象'%s'没有分数

en: 'scale' is outside [0, 1]
zh_CN: 'scale'在[0, 1]值域外

en: biplots are not defined for complex PCA
zh_CN: 复数的PCA画不出biplot

en: unequal number of rows in 'cancor'
zh_CN: 'cancor'里的行数不一样

en: dimension 0 in 'x' or 'y'
zh_CN: 'x'或'y'的维度为零

en: 'x' has rank 0
zh_CN: 'x'的秩为零

en: 'y' has rank 0
zh_CN: 'y'的秩为零

en: 'x' and 'y' must have the same length
zh_CN: 'x'和'y'的长度必需相同

en: 'x' and 'y' must have at least 2 levels
zh_CN: 'x'和'y'至少有两个水平

en: all entries of 'x' must be nonnegative and finite
zh_CN: 'x'所有的值都必需是非负的有限值

en: at least one entry of 'x' must be positive
zh_CN: 'x'里至少要包含一个正值

en: cannot compute simulated p-value with zero marginals
zh_CN: 无法用零邊緣计算模拟p值

en: 'x' must at least have 2 elements
zh_CN: 'x'至少有两个单元

en: 'x' and 'p' must have the same number of elements
zh_CN: 'x'和'p'必需包含同样数目的单元

en: probabilities must be non-negative.
zh_CN: 概率必需为非负值。

en: probabilities must sum to 1.
zh_CN: 概率总和必需为一。

en: Chi-squared approximation may be incorrect
zh_CN: Chi-squared近似算法有可能不准

en: NA values not allowed in 'd'
zh_CN: 'd'里不允许有NA值

en: distances must be result of 'dist' or a square matrix
zh_CN: 距离必需是'dist'计算出结果或是正方形矩阵

en: 'k' must be in {1, 2, ..  n - 1}
zh_CN: 'k'必需是{1, 2, ..  n - 1}中的一个

en: only %d of the first %d eigenvalues are > 0
zh_CN: 前%2$d个特征值里只有%1$d个大于零

en: initial value is not in the interior of the feasible region
zh_CN: 初始值不是在可行区域内

en: Barrier algorithm ran out of iterations and did not converge
zh_CN: 壁垒算法超过了迭代但没有收敛

en: Objective function increased at outer iteration %d
zh_CN: 目标函数在外部循环增加了%d

en: Objective function decreased at outer iteration %d
zh_CN: 目标函数在外部循环减少了%d

en: contrasts not defined for %d degrees of freedom
zh_CN: 对比不适用于%d自由度

en: orthogonal polynomials cannot be represented accurately enough for %d degrees of freedom
zh_CN: 因为自由度为%d，所以无法准确地表达正交多項式

en: 'scores' argument is of the wrong length
zh_CN: 'scores'参数长度对对

en: 'scores' must all be different numbers
zh_CN: 'scores'内包含的数值不能都是相同的

en: 'degree' must be at least 1
zh_CN: 'degree'至少为一

en: missing values are not allowed in 'poly'
zh_CN: 'poly'里不能有缺失值

en: 'degree' must be less than number of unique points
zh_CN: 'degree'必需小于唯一数据点的数目

en: must supply one or more vectors
zh_CN: 至少要提供一或多个向量

en: arguments must have the same length
zh_CN: 参数的长度必需一样

en: contrasts apply only to factors
zh_CN: 对比只适用于因子

en: contrasts can be applied only to factors with 2 or more levels
zh_CN: 对比只适用于有两个或多于两个水平的因子

en: wrong number of contrast matrix rows
zh_CN: 对比矩阵的行数有误

en: singular contrast matrix
zh_CN: 奇异对比矩阵

en: numeric contrasts or contrast name expected
zh_CN: 需要数值对比或对比的名称

en: %s needs package 'Matrix' correctly installed
zh_CN: %s需要正确安装的'Matrix'程序包

en: not enough degrees of freedom to define contrasts
zh_CN: 设定对比的自由度不足

en: baseline group number out of range
zh_CN: 基線组在值域外

en: invalid 'use' argument
zh_CN: 'use'参数无效

en: supply both 'x' and 'y' or a matrix-like 'x'
zh_CN: 要么同时设定'x'和'y'，要么只设定矩阵式的'x'

en: 'y' must be numeric
zh_CN: 'y'必需是数值

en: 'x' is empty
zh_CN: 'x'是空的

en: both 'x' and 'y' must be non-empty
zh_CN: 'x'和'w'不能是空的

en: cannot handle 'pairwise.complete.obs'
zh_CN: 不知如何處理'pairwise.complete.obs'

en: 'V' is not a square numeric matrix
zh_CN: 'V'不是正方形的数值矩阵

en: 'x' must be a numeric vector
zh_CN: 'x'必需是数值向量

en: 'y' must be a numeric vector
zh_CN: 'y'必需是数值向量

en: not enough finite observations
zh_CN: 有限值的观察量不足

en: invalid formula
zh_CN: 公式无效

en: 'x' must be a matrix or a data frame
zh_CN: 'x'必需是矩阵或是数据框

en: 'x' must contain finite values only
zh_CN: 'x'只能包含有限值

en: length of 'wt' must equal the number of rows in 'x'
zh_CN: 'wt'的长度必需同'x'的行数相同

en: weights must be non-negative and not all zero
zh_CN: 权数必需为非负值，而且不能全部都为零

en: length of 'center' must equal the number of columns in 'x'
zh_CN: 'center'的长度必需同'x'的列数相同

en: invalid 'tree' ('merge' component)
zh_CN: 'tree'无效('merge'组件)

en: either 'k' or 'h' must be specified
zh_CN: 'k'和'h'中必需设定一个

en: the 'height' component of 'tree' is not sorted (increasingly)
zh_CN: 'tree'里的'height'组件没有排序(由小到大)

en: elements of 'k' must be between 1 and %d
zh_CN: 'k'的单元必需在一和%d之间

en: dendrogram node with non-positive #{branches}
zh_CN: 系統樹圖节点有非正#{分支}

en: midcache() of non-binary dendrograms only partly implemented
zh_CN: 非二分系統樹圖的midcache()只实现了一部分

en: non-leaf subtree of length 0
zh_CN: 长度为零的非叶子树

en: 'order.dendrogram' requires a dendrogram
zh_CN: 'order.dendrogram'必需用在系統樹圖上

en: 'reorder.dendrogram' requires a dendrogram
zh_CN: 'reorder.dendrogram'必需用在系統樹圖上

en: invalid (length 0) node in dendrogram
zh_CN: 系統樹圖里的节点无效(长度为零)

en: dendrogram non-leaf node with non-positive #{branches}
zh_CN: 系統樹圖里有非正#{分支}的非叶节点

en: 'height' must be at least %g, the maximal height of its components
zh_CN: 'height'值必需至少是%g，其所有组件里最大的高度值

en: 'X' is not a dendrogram
zh_CN: 'X'不是系統樹圖

en: 'x' must be a numeric matrix
zh_CN: 'x'必需为数值矩阵

en: 'x' must have at least 2 rows and 2 columns
zh_CN: 'x'至少有两行和两列

en: 'margins' must be a numeric vector of length 2
zh_CN: 'margins'必需是长度为二的数值向量

en: row dendrogram ordering gave index of wrong length
zh_CN: 行系統樹圖排序索引结果的长度出了错

en: Colv = "Rowv" but nrow(x) != ncol(x)
zh_CN: Colv = "Rowv" 但 nrow(x) != ncol(x)

en: column dendrogram ordering gave index of wrong length
zh_CN: 行系統樹圖排序索引结果的长度出了错

en: 'ColSideColors' must be a character vector of length ncol(x)
zh_CN: 'ColSideColors'必需是长度为ncol(x)的字节向量

en: 'RowSideColors' must be a character vector of length nrow(x)
zh_CN: 'RowSideColors'必需是长度为nrow(x)的字节向量

en: argument 'x' must be numeric
zh_CN: 参数'x'必需是数值

en: 'x' and 'weights' have unequal length
zh_CN: 'x'和'weights'的长度必需相同

en: 'x' contains missing values
zh_CN: 'x'内含缺失值

en: 'weights' must all be finite
zh_CN: 'weights'的值必需都是有限的

en: 'weights' must not be negative
zh_CN: 'weights'不能是负数的

en: sum(weights) != 1  -- will not get true density
zh_CN: sum(weights) != 1，因此无法取得真密度函数

en: need at least 2 points to select a bandwidth automatically
zh_CN: 自动选择頻寬至少需要两个数据点

en: unknown bandwidth rule
zh_CN: 没有这样的頻寬规则

en: non-finite 'bw'
zh_CN: 'bw'是无限值

en: 'bw' is not positive.
zh_CN: 'bw'不是正数。

en: non-finite 'from'
zh_CN: 'from'是无限值

en: non-finite 'to'
zh_CN: 'to'是无限值

en: invalid formula in deriv
zh_CN: deriv里的公式无效

en: 'x' is not a vector
zh_CN: 'x'不是向量

en: bad value for 'lag' or 'differences'
zh_CN: 'lag'或'differences'的值有错误

en: 'xi' does not have the right length
zh_CN: 'xi'的长度不正确

en: incorrect dimensions for 'xi'
zh_CN: 'xi'的尺寸不正确

en: 'x' is not a vector or matrix
zh_CN: 'x'不是向量或矩阵

en: invalid distance method
zh_CN: 距离方法无效

en: ambiguous distance method
zh_CN: 距离方法不明确

en: non-square matrix
zh_CN: 非正方形矩阵

en: specify 'rate' or 'scale' but not both
zh_CN: 'rate'和'scale'两个中只能指定其中一个

en: x[] and prob[] must be equal length vectors.
zh_CN: x[]和prob[]必需是长度相等的向量。

en: 'x' must be non-negative
zh_CN: 'x'必需为非负数

en: size != sum(x), i.e. one is wrong
zh_CN: size != sum(x), 两个中间至少有一个有误

en: 'prob' and 'mu' both specified
zh_CN: 'prob'和'mu'不能同时设定

en: some terms will have NAs due to the limits of the method
zh_CN: 因为这个方法的限制，有些项的值为NA

en: 'x' must have 1 or more non-missing values
zh_CN: 'x'必需含一个或多个非缺失值

en: wrong embedding dimension
zh_CN: 嵌入维度有误

en: 'covmat' is not a valid covariance list
zh_CN: 'covmat'这个协方差列表有错误

en: neither 'x' nor 'covmat' supplied
zh_CN: 'x'和'covmat'都没有被设定

en: response not allowed in formula
zh_CN: 公式里不能有响应

en: factor analysis applies only to numerical variables
zh_CN: 因子分析只适用于数值变量

en: 'covmat' is of unknown type
zh_CN: 'covmat'的类型不正确

en: requested scores without an 'x' matrix
zh_CN: 被要求的分数没有'x'矩阵

en: factor analysis requires at least three variables
zh_CN: 因子分析至少需要三个变量

en: no starting values supplied
zh_CN: 没有设定初始值

en: %s link not recognised
zh_CN: 没有这样%s的鏈結

en: negative values not allowed for the 'Poisson' family
zh_CN: 'Poisson'分布族不能用负数

en: ignoring prior weights
zh_CN: 先验重量忽略不用

en: negative values not allowed for the 'quasiPoisson' family
zh_CN: 'quasiPoisson'分布族不能用负数

en: cannot find valid starting values: please specify some
zh_CN: 找不到有效的初始值: 请指定

en: y values must be 0 <= y <= 1
zh_CN: y值必需满足0 <= y <= 1

en: cannot simulate from non-integer prior.weights
zh_CN: 无法用非整数的prior.weights进行模拟

en: using weights as shape parameters
zh_CN: 用重量作为形状参数

en: positive values only are allowed for the 'inverse.gaussian' family
zh_CN: 'inverse.gaussian'分布族只允许正值

en: using weights as inverse variances
zh_CN: 用重量作逆方差

en: 'variance' "%s" is invalid: possible values are "mu(1-mu)", "mu", "mu^2", "mu^3" and "constant"
zh_CN: 'variance' "%s"无效：只能用"mu(1-mu)", "mu", "mu^2", "mu^3"和"constant"其中之一

en: length mismatch in convolution
zh_CN: 卷积中的长度不相符

en: missing values in 'filter'
zh_CN: 'filter'里有缺失值

en: 'filter' is longer than time series
zh_CN: 'filter'的长度比时间序列的长度要长

en: argument 'sides' must be 1 or 2
zh_CN: 参数'sides'必需是一或二

en: 'circular' must be logical and not NA
zh_CN: 'circular'必需是逻辑值，不能是NA

en: length of 'init' must equal length of 'filter'
zh_CN: 'init'的长度必需等于'filter'的长度

en: 'x' must have at least 2 rows and columns
zh_CN: 'x'至少必需有两行和两列

en: 'x' has entries too large to be integer
zh_CN: 'x'的正数值太大

en: if 'x' is not a matrix, 'y' must be given
zh_CN: 如果'x'不是矩阵的话, 一定得有'y'值

en: 'mult' must be integer >= 2, typically = 30
zh_CN: 'mult'必需是大于等于二的正数，一般等于30

en: alternative must be "two.sided", "less" or "greater"
zh_CN: 必需在"two.sided", "less"或"greater"中选一个

en: 'or' must be a single number between 0 and Inf
zh_CN: 'or'必需是在零和Inf之间的一个数值

en: need 2 or more non-zero row marginals
zh_CN: 至少需要多于两个非零行边

en: need 2 or more non-zero column marginals
zh_CN: 至少需要多于两个非零列边

en: 'hybrid' is ignored for a 2 x 2 table
zh_CN: 'hybrid'对2x2的表无用

en: all groups must contain data
zh_CN: 所有的组都必需包含数据

en: not enough observations
zh_CN: 观察量不足

en: 'y', 'groups' and 'blocks' must have the same length
zh_CN: 'y', 'groups'和'blocks'的长度必需相同

en: not an unreplicated complete block design
zh_CN: 不是非折疊完全区组设计

en: 'formula' missing
zh_CN: 缺失'formula'

en: nothing to tabulate
zh_CN: 没有可列表的数据

en: 'formula' must have both left and right hand sides
zh_CN: 'formula'的公式左右两边都必需存在

en: interactions are not allowed
zh_CN: 不允许有交互作用项

en: 'formula' has '.' in both left and right hand sides
zh_CN: 'formula'的公式左右两边都有'.'

en: incorrect variable names in rhs of formula
zh_CN: 公式右边变量名称不正确

en: incorrect variable names in lhs of formula
zh_CN: 公式左边变量名称不正确

en: cannot use dots in formula with given data
zh_CN: 按照所提供的数据无法在公式里用点符号

en: 'x' must be an "ftable" object
zh_CN: 'x'必需是"ftable"对象

en: wrong method
zh_CN: 方法有误

en: 'file' must be a character string or connection
zh_CN: 'file'必需是字符串或链结

en: 'row.var.names' missing
zh_CN: 缺失'row.var.names'值

en: 'col.vars' missing or incorrect
zh_CN: 缺失'col.vars'值或它设定得不正确

en: 'family' not recognized
zh_CN: 'family'设定得不正确

en: invalid 'method' argument
zh_CN: 'method'参数无效

en: 'weights' must be a numeric vector
zh_CN: 'weights'必需是数值向量

en: negative weights not allowed
zh_CN: 权数不能是负的

en: number of offsets is %d should equal %d (number of observations)
zh_CN: 偏置的个数是%d，应该同观察量的个数%d相等

en: fitting to calculate the null deviance did not converge -- increase 'maxit'?
zh_CN: 拟合计算零变异时没有回归—增加'maxit'吗？

en: value of 'epsilon' must be > 0
zh_CN: 'epsilon'的值必需大于零

en: maximum number of iterations must be > 0
zh_CN: 迭代的最高值数目必需大于零

en: 'family' argument seems not to be a valid family object
zh_CN: 'family'参数的值不是正确的分布族对象

en: invalid linear predictor values in empty model
zh_CN: 空模型里的线形従属変数值无效

en: invalid fitted means in empty model
zh_CN: 空模型的拟合平均值无效

en: length of 'start' should equal %d and correspond to initial coefs for %s
zh_CN: 'start'的长度应该是%d，同%s的初始系数相一致

en: NAs in V(mu)
zh_CN: V(mu) 里有NA

en: 0s in V(mu)
zh_CN: V(mu) 里有零

en: NAs in d(mu)/d(eta)
zh_CN: d(mu)/d(eta) 里有NA

en: no observations informative at iteration %d
zh_CN: 观察量不含信息，于迭代%d

en: non-finite coefficients at iteration %d
zh_CN: 迭代%d时出现了无限大的系数值

en: singular fit encountered
zh_CN: 遇到了奇异拟合

en: no valid set of coefficients has been found: please supply starting values
zh_CN: 找不到有效的系数：请提供初始值

en: step size truncated due to divergence
zh_CN: 因为算法分歧将步驟大小縮短

en: inner loop 1; cannot correct step size
zh_CN: 内循環一; 无法校正步驟大小

en: step size truncated: out of bounds
zh_CN: 縮短了步驟大小：在值域外

en: inner loop 2; cannot correct step size
zh_CN: 内循環二; 无法校正步驟大小

en: glm.fit: algorithm did not converge
zh_CN: glm.fit:算法没有聚合

en: glm.fit: algorithm stopped at boundary value
zh_CN: glm.fit:算法在边界值停止

en: glm.fit: fitted probabilities numerically 0 or 1 occurred
zh_CN: glm.fit:拟合概率算出来是数值零或一

en: glm.fit: fitted rates numerically 0 occurred
zh_CN: glm.fit:拟合速率算出来是数值零

en: the following arguments to 'anova.glm' are invalid and dropped:
zh_CN: 以下'anova.glm'的参数无效因此忽略不用:

en: using F test with a '%s' family is inappropriate
zh_CN: 将F检验用于%s分布族是不正确的

en: using F test with a fixed dispersion is inappropriate
zh_CN: 将F检验用于固定疏散的分布是不正确的

en: models with response %s removed because response differs from model 1
zh_CN: 带响应%s的模型被删除了，因为响应同模型1不相对

en: models were not all fitted to the same size of dataset
zh_CN: 模型拟合时没有用同样大小的数据集

en: observations with zero weight not used for calculating dispersion
zh_CN: 计算离散度时零权数的观察量忽略不用

en: invalid clustering method
zh_CN: 聚类方法无效

en: ambiguous clustering method
zh_CN: 聚类方法不明确

en: invalid dissimilarities
zh_CN: dissimilarities无效

en: size cannot be NA nor exceed 65536
zh_CN: 大小不能是NA，出不能超出65536

en: must have n >= 2 objects to cluster
zh_CN: 用群集时必需有n >= 2的对象

en: invalid length of members
zh_CN: 成员长度无效

en: argument 'x' cannot be coerced to class %s
zh_CN: 无法将'x'参数強制转换成%s类别

en: Consider providing an as.hclust.%s() method
zh_CN: 请考虑提供as.hclust.%s()这一方法

en: need dendrograms where all leaves have labels
zh_CN: 需要所有叶子都有标记的系統樹圖

en: 'k' and 'h' must be a scalar
zh_CN: 'k'和'h'必需都是纯量

en: specify exactly one of 'k' and 'h'
zh_CN: 'k'和'h'两者只能指定一个

en: k must be between 2 and %d
zh_CN: k必需在二和%d之间

en: specify exactly one of 'which' and 'x'
zh_CN: 'which'和'x'两者只能指定一个

en: all elements of 'which' must be between 1 and %d
zh_CN: 'which'的所有单元值都必需在一和%d之间

en: invalid parameter values
zh_CN: 参数值无效

en: missing values not allowed
zh_CN: 不能有缺失值

en: 'r' is less than 1
zh_CN: 'r'小于一

en: 'm' is less than 1
zh_CN: 'm'小于一

en: 'r' is less than 0
zh_CN: 'r'小于零

en: 'm' must be numeric with non-negative integers
zh_CN: 'm'必需是非负的整数

en: unknown named kernel
zh_CN: 没有这样的内核名

en: 'coef' must be a vector
zh_CN: 'coef'必需是向量

en: 'coef' does not have the correct length
zh_CN: 'coef'的长度不正确

en: coefficients do not add to 1
zh_CN: 系数总和不是一

en: 'k' is not a kernel
zh_CN: 'k'不是内核

en: 'x' is shorter than kernel 'k'
zh_CN: 'x'比内核'k'要短

en: 'kernapply' is not available for object 'x'
zh_CN: 'kernapply'不适用于对象'x'

en: 'x' is not a kernel
zh_CN: 'x'不是内核

en: empty cluster: try a better set of initial centers
zh_CN: 空的群集: 请试试更精确一点的初始中心集

en: number of cluster centres must lie between 1 and nrow(x)
zh_CN: 群集中心数目必需在一和nrow(x) 这间

en: invalid nrow(x)
zh_CN: nrow(x)无效

en: invalid ncol(x)
zh_CN: ncol(x)无效

en: 'centers' must be a number or a matrix
zh_CN: 'centers'必需是个数字或矩阵

en: more cluster centers than distinct data points.
zh_CN: 群集中心的数目要比数据点数目多。

en: initial centers are not distinct
zh_CN: 初始中心的值有些是相同的

en: more cluster centers than data points
zh_CN: 群集中心的数目要比数据点数目多

en: 'iter.max' must be positive
zh_CN: 'iter.max'必需是正数

en: must have same number of columns in 'x' and 'centers'
zh_CN: 'x'和'centers'的列数必需相等

en: not enough 'x' data
zh_CN: 'x'数据不足

en: not enough 'y' data
zh_CN: 'y'数据不足

en: p-value will be approximate in the presence of ties
zh_CN: 并列的时候P-值将近似

en: 'y' must be numeric or a function or a string naming a valid function
zh_CN: 'y'必需是数值，或是个函数，或是指定适当函数的字符串

en: numeric y must be supplied.
    For density estimation use density()
zh_CN: y数值必需存在。
    用density()来估计密度

en: 'k' is not an integer
zh_CN: 'k'不是整数

en: method = '%s' is not supported. Using 'qr'
zh_CN: 不支持method = '%s'。用'qr'

en: number of offsets is %d, should equal %d (number of observations)
zh_CN: 偏置的个数为%d，应该为%d (观察量数目)

en: 'x' must be a matrix
zh_CN: 'x'必需是矩阵

en: 0 (non-NA) cases
zh_CN: 没有(非NA)案例可用

en: incompatible dimensions
zh_CN: 维度不匹配

en: missing or negative weights not allowed
zh_CN: 权数不允许是缺失值或负值

en: invalid 'lm' object:  no 'terms' component
zh_CN: 'lm'对象无效: 不包含'term'组件

en: calling summary.lm(<fake-lm-object>) ...
zh_CN: 调用summary.lm(<fake-lm-object>) ...

en: residual degrees of freedom in object suggest this is not an "lm" fit
zh_CN: 对象的残差自由度表明这不是"lm" 拟合

en: lm object does not have a proper 'qr' component.
     Rank zero or should not have used lm(.., qr=FALSE).
zh_CN: lm对象的'qr'组件不正确。
     其秩零，要么不应该调用lm(.., qr=FALSE).

en: family '%s' not implemented
zh_CN: 分布族'%s'还没有实现

en: calling anova.lm(<fake-lm-object>) ...
zh_CN: 调用anova.lm(<fake-lm-object>) ...

en: ANOVA F-tests on an essentially perfect fit are unreliable
zh_CN: 对几乎是完全拟合进行ANOVA F-检验的结果不会可靠

en: calling predict.lm(<fake-lm-object>) ...
zh_CN: 调用predict.lm(<fake-lm-object>) ...

en: predictions on current data refer to _future_ responses
zh_CN: 用当前数据得到的预测结果对_未来_响应有用

en: assuming prediction variance inversely proportional to weights used for fitting
zh_CN: 假设预测的方差同在拟和时所用的衡量成负比

en: Assuming constant prediction variance even though model fit is weighted
zh_CN: 虽然模型在拟和时用了衡量，但在预测时仍假定方差不变

en: 'weights' as formula should be one-sided
zh_CN: 'weights'作为公式来用只能在公式的一边

en: 'object' has no 'effects' component
zh_CN: 'object'没有'effects'组件

en: the 'se.fit' argument is not yet implemented for "mlm" objects
zh_CN: 'se.fit'参数还没有为"mlm"对象实现

en: invalid model QR matrix
zh_CN: 模型QR矩阵无效

en: non-NA residual length does not match cases used in fitting
zh_CN: 非NA残差的长度与拟合时所用的案例不相符

en: predictors must all be numeric
zh_CN: 従属変数必需都是数值

en: 'degree' must be 0, 1 or 2
zh_CN: 'degree'必需是0, 1或2

en: both 'span' and 'enp.target' specified: 'span' will be used
zh_CN: 'span'和'enp.target'同时有设定：只用'span'的值

en: invalid 'control' argument
zh_CN: 'control'参数无效

en: invalid NCOL(X)
zh_CN: NCOL(X)无效

en: only 1-4 predictors are allowed
zh_CN: 只允许有一到四个従属変数

en: invalid 'y'
zh_CN: 'y'无效

en: specified the square of a factor predictor to be dropped when degree = 1
zh_CN: degree = 1时不能忽略因子従属変数的二次方

en: specified the square of a predictor to be dropped with only one numeric predictor
zh_CN: 只有一个数值従属変数时不能忽略従属変数的二次方

en: specified parametric for all predictors
zh_CN: 所有従属変数都被设定为参数

en: first argument must be a "loess" object
zh_CN: 第一个参数必需为"loess"对象

en: no models to compare
zh_CN: 没有可用来比较的模型

en: extra arguments discarded
zh_CN: 多余的参数忽略不用

en: 'logLik.lm' does not support multiple responses
zh_CN: 'logLik.lm'不支持多响应

en: no "nobs" attribute is available
zh_CN: 没有"nobs"这个属性

en: no 'nobs' method is available
zh_CN: 没有适用于'nobs'的方法

en: 'start' and 'table' must be same length
zh_CN: 'start'和'table'的长度必需相等

en: number of weights = %d should equal %d (number of responses)
zh_CN: 权数的数目 = %d但应等于%d (响应数目)

en: 'X' matrix was collinear
zh_CN: 'X'是共线矩阵

en: missing observations deleted
zh_CN: 删除了缺失观察量

en: observations with 0 weight not used in calculating standard deviation
zh_CN: 计算标准差时不用零权数的观察量

en: observations with 0 weights not used
zh_CN: 权数为零的观察量忽略不用

en: 'low' and 'high' cannot be both TRUE
zh_CN: 'low'和'high'不能同时都为TRUE

en: need multiple responses
zh_CN: 需要多个响应

en: object must be of class %s or %s
zh_CN: 对象必需是%s或%s的类别

en: residuals have rank %d < %d
zh_CN: 残差的秩 %d < %d

en: NAs are not allowed
zh_CN: 不能有NA

en: each dimension in table must be >= 2
zh_CN: 表的每个维度都必需 >= 2

en: 'x' must be a 3-dimensional array
zh_CN: 'x'必需是三维数组

en: if 'x' is not an array, 'y' must be given
zh_CN: 如果'x'不是数组的话, 必需提供'y'值

en: if 'x' is not an array, 'z' must be given
zh_CN: 如果'x'不是数组的话, 必需提供'z'值

en: 'x', 'y', and 'z' must have the same length
zh_CN: 'x', 'y'和'z'的长度必需相等

en: sample size in each stratum must be > 1
zh_CN: 每个阶层里的样本大小都必需大于一

en: 'x' must be square with at least two rows and columns
zh_CN: 'x'必需是正方形的至少有两列和两行的矩阵

en: 'x' and 'y' must have the same number of levels (minimum 2)
zh_CN: 'x'和'y'必需有相同数目的水平(最少要有两层)

en: need numeric data
zh_CN: 需要数值数据

en: 'mlm' objects with weights are not supported
zh_CN: 目前不支持带权数的'mlm'对象

en: X does not define a subspace of M
zh_CN: X不设定M的子空间

en: residuals have rank %s < %s
zh_CN: 残差的秩%s < %s

en: 'model.tables' is not implemented for multiple responses
zh_CN: 'model.tables'没有为多响应实现

en: type '%s' is not implemented yet
zh_CN: 还没有实现'%s'类型

en: this fit does not inherit from "lm"
zh_CN: 这拟和值不是由"lm"继承来的

en: 'cterms' argument must match terms in model object
zh_CN: 'cterms'参数必需同模型对象里的项匹配

en: Design is unbalanced - use se.contrast() for se's
zh_CN: 设计不平衡 - 用se.contrast()算标准误

en: design is unbalanced so cannot proceed
zh_CN: 设计不平衡，因此运算无法继续

en: Standard error information not returned as design is unbalanced. 
    Standard errors can be obtained through 'se.contrast'.
zh_CN: 因为设计是不平衡的，没有返回标准误信息。
    但可以通过'se.contrast'来计算标准误。

en: SEs for type '%s' are not yet implemented
zh_CN: 还没有为'%s'类型实现标准误的算法

en: na.action must be a function
zh_CN: na.action必需为函数

en: non-factors ignored: %s
zh_CN: 非因子忽略不用: %s

en: eff.aovlist: non-orthogonal contrasts would give an incorrect answer
zh_CN: eff.aovlist: 用非正交对比来算的结果会出错

en: cannot create a formula from a zero-column data frame
zh_CN: 无法用空数据框来建立公式

en: no terms component nor attribute
zh_CN: 没有项组件也没有属性

en: 'termobj' must be a object of class %s
zh_CN: 'termobj'必需是类别为%s的对象

en: variable '%s' was fitted with type "%s" but type "%s" was supplied
zh_CN: 变量'%s'是由"%s"类别拟合而成，与提供的类别"%s"不匹配

en: variables %s were specified with different types from the fit
zh_CN: 设定的变量%s的类别同拟合的类别不相符

en: 'data' must be a data.frame, not a matrix or an array
zh_CN: 'data'必需是数据框, 不能是矩阵或数组

en: variable '%s' is not a factor
zh_CN: 变量'%s'不是因子

en: 'offset' must be numeric
zh_CN: 'offset'必需为数值

en: model frame and formula mismatch in model.matrix()
zh_CN: model.matrix()里的模型框和公式不匹配

en: variable '%s' is absent, its contrast will be ignored
zh_CN: 没有变量'%s'，因此它的对比忽略不用

en: using type = "numeric" with a factor response will be ignored
zh_CN: 在因子响应上用type="numeric"的这一选项不会有效果

en: invalid response type
zh_CN: 响应类型无效

en: invalid 'data' argument
zh_CN: 'data'参数无效

en: all times contain an NA
zh_CN: 所有时间都含NA

en: missing values in object
zh_CN: 对象里有缺失值

en: 'print.level' must be in {0,1,2}
zh_CN: 'print.level'必需是{0,1,2}

en: 'interval' must be a vector of length 2
zh_CN: 'interval'必需是长度为二的数值向量

en: lower < upper  is not fulfilled
zh_CN: 没有满足lower小于upper这个条件

en: f.lower = f(lower) is NA
zh_CN: f.lower = f(lower)的值为NA

en: f.upper = f(upper) is NA
zh_CN: f.upper = f(upper)的值为NA

en: f() values at end points not of opposite sign
zh_CN: 位于极点边的f()值之正负号不相反

en: 'control' argument must be a named list
zh_CN: 'control'参数必需是列表

en: logical 'hessian' argument not allowed.  See documentation.
zh_CN: 逻辑值的`hessian'参数是不可以的。请参考文档。

en: 'params' has wrong length
zh_CN: 'params'的长度有误

en: 'varying' must be in seq_along(pars)
zh_CN: 'varying'必需在seq_along(pars) 中

en: 'varying' has wrong length
zh_CN: 'varying'长度有误

en: 'varying' must be logical, integer or character
zh_CN: 'varying'必需是逻辑值，整数值或字符

en: invalid argument to 'getProfile'
zh_CN: 'getProfile'参数无效

en: cannot recognize parameter name
zh_CN: 没有这样的参数名

en: levels truncated to positive values only
zh_CN: 水平被縮短成正值

en: setVarying : 'vary' length must match length of parameters
zh_CN: setVarying : 'vary'的长度必需同参数的长度相符

en: singular gradient matrix at initial parameter estimates
zh_CN: 参数初始估计值里有奇异梯度矩阵

en: 'data' must be a list or an environment
zh_CN: 'data'必需是列表或是环境

en: no starting values specified
zh_CN: 没有设定初始值

en: parameters without starting value in 'data': %s
zh_CN: 'data'里的参数没有被设定初始值: %s

en: no parameters to fit
zh_CN: 没有可用来拟合的参数值

en: argument 'subset' will be ignored
zh_CN: 参数'subset'忽略不用

en: argument 'na.action' will be ignored
zh_CN: 参数'na.action'忽略不用

en: upper and lower bounds ignored unless algorithm = "port"
zh_CN: 除非algorithm = "port"，否则上限或下限将忽略不用

en: cannot calculate REML log-likelihood for "nls" objects
zh_CN: 无法为"nls"对象计算REML log-likelihood

en: anova is only defined for sequences of "nls" objects
zh_CN: anova只适用于"nls"对象序列

en: 'anova' is only defined for sequences of "nls" objects
zh_CN: 'anova'只适用于"nls"对象序列

en: formula '%s' must be of the form '~expr'
zh_CN: 公式'%s'的形式必需是'~expr'

en: '%s' cannot be of mode '%s'
zh_CN: '%s'不可能有'%s'状态

en: a two-sided formula is required
zh_CN: 需要双边公式

en: not enough groups
zh_CN: 组个数不足

en: bounds can only be used with method L-BFGS-B (or Brent)
zh_CN: 只有在用L-BFGS-B(或Brent)方法时才能定优化界限

en: unknown names in control:
zh_CN: 控制里没有这样的名称

en: read the documentation for 'trace' more carefully
zh_CN: 请小心阅读'trace'文档

en: 'trace != 0' needs 'REPORT >= 1'
zh_CN: 'trace != 0'需要'REPORT >= 1'

en: method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and 'abstol'
zh_CN: L-BFGS-B方法用'factr'(和'pgtol') ，而不是'reltol'和'abstol'

en: one-dimensional optimization by Nelder-Mead is unreliable:
    use "Brent" or optimize() directly
zh_CN: 用Nelder-Mead方法来算一维优化不很可靠：
    用"Brent"或直接用optimize()

en: method = "Brent" is only available for one-dimensional optimization
zh_CN: method = "Brent" 只适用于一维优化

en: 'lower' and 'upper' must be finite values
zh_CN: 'lower'和'upper'必需都是有限值

en: pooling of SD is incompatible with paired tests
zh_CN: 成双检验与合并SD不兼容

en: 'x' must have 2 columns
zh_CN: 'x'必需有两列

en: 'x' and 'n' must have the same length
zh_CN: 'x'和'n'的长度必需相等

en: too few groups
zh_CN: 组数不足

en: not plotting observations with leverage one:
      %s
zh_CN: 杠杆效果为一的点不会被画出来:
      %s

en: use only with "lm" objects
zh_CN: 只对"lm"对象适用

en: 'which' must be in 1:6
zh_CN: 'which'必需在1:6之间

en: 'id.n' must be in {1,..,%d}
zh_CN: 'id.n'必需在{1,..,%d}中

en: y is empty or has only NAs
zh_CN: y是空的或只含NA

en: hat values (leverages) are all = %s
     and there are no factor predictors; no plot no. 5
zh_CN: 帽子值（杠杆）都 = %s
    没有因子预测；没有第五幅画表

en: 'x' and 'T' have incompatible length
zh_CN: 'x'和'T'的长度不相容

en: 'x' must be finite, nonnegative, and integer
zh_CN: 'x'必需是有限的非负整数

en: 'T' must be nonnegative
zh_CN: 'T'必需为非负数

en: not enough data
zh_CN: 数据不足

en: the case k > 2 is unimplemented
zh_CN: 还没有实现k > 2的例子

en: 'r' must be a single positive number
zh_CN: 'r'必需是单独正数

en: exactly one of 'n', 'delta', 'sd', 'power', and 'sig.level' must be NULL
zh_CN: 'n', 'delta', 'sd', 'power',和'sig.level'之中只能有一个取NULL值

en: exactly one of 'n', 'p1', 'p2', 'power', and 'sig.level' must be NULL
zh_CN: 'n', 'p1', 'p2', 'power',和'sig.level'之中只能有一个取NULL值

en: exactly one of 'groups', 'n', 'between.var', 'within.var', 'power', and 'sig.level' must be NULL
zh_CN: 'groups', 'n', 'between.var', 'within.var', 'power',和'sig.level'之中只能有一个取NULL值

en: number of groups must be at least 2
zh_CN: 组数至少为2

en: number of observations in each group must be at least 2
zh_CN: 每组的观察量数目至少为2

en: 'nterms' is missing with no default
zh_CN: 缺失'nterms'的值，也缺失默認值

en: 'ppr' applies only to numerical variables
zh_CN: 'ppr'只适用于数值变量

en: mismatched 'x' and 'y'
zh_CN: 'x'和'y'不匹配

en: wrong number of columns in 'x'
zh_CN: 'x'里的列数有误

en: cannot rescale a constant/zero column to unit variance
zh_CN: 不能将常数的列/全部为零的列的方差重新调整为一

en: PCA applies only to numerical variables
zh_CN: PCA只适用于数值变量

en: no scores are available: refit with 'retx=TRUE'
zh_CN: 没有分数: 用'retx=TRUE'重新进行拟合

en: 'newdata' must be a matrix or data frame
zh_CN: 'newdata'必需是矩阵或是数据框

en: 'newdata' does not have named columns matching one or more of the original columns
zh_CN: 'newdata'的列名同原始数据的列名没有任何相同的

en: 'newdata' does not have the correct number of columns
zh_CN: 'newdata'列的数目不正确

en: both 'x' and 'covmat' were supplied: 'x' will be ignored
zh_CN: 同时提供了'x'和'covmat': 'x'忽略不用

en: 'princomp' can only be used with more units than variables
zh_CN: 'princomp'只能在单位比变量多的情况下使用

en: cannot use 'cor = TRUE' with a constant variable
zh_CN: 'cor=TRUE'不能和常数变量同时使用

en: covariance matrix is not non-negative definite
zh_CN: 协方差矩阵不是非负定矩阵

en: argument does not include a 'qr' component
zh_CN: 参数不包含'qr'组件

en: argument does not include an 'effects' component
zh_CN: 参数不包含'effects'组件

en: 'proj' is not implemented for multiple responses
zh_CN: 'proj'还没有为多响应实现

en: table 'x' should have 2 entries
zh_CN: 'x'表至少应该有两项

en: elements of 'n' must be positive
zh_CN: 'n'的单元必需都是正的

en: elements of 'x' must be nonnegative
zh_CN: 'x'的单元必需都是非负的

en: elements of 'x' must not be greater than those of 'n'
zh_CN: 'x'的单元不能比'n'的单元大

en: 'p' must have the same length as 'x' and 'n'
zh_CN: 'p'的长度必需同'x'和'n'的相等

en: elements of 'p' must be in (0,1)
zh_CN: 'p'的单元必需在(0,1)里

en: missing values and NaN's not allowed if 'na.rm' is FALSE
zh_CN: 'na.rm'如果设为FALSE的话不允许有缺失值和NaN

en: 'probs' outside [0,1]
zh_CN: 'probs'在[0,1]外

en: arguments 'r' and 'c' must have the same sums
zh_CN: 参数'r'和'c'的和必需是一样的

en: 'ref' must be of length one
zh_CN: 'ref'的长度必需是一

en: 'ref' must be an existing level
zh_CN: 'ref'必需是存在的水平

en: ref = %d must be in 1L:%d
zh_CN: ref = %d必需在1L:%d里

en: failed to guess time-varying variables from their names
zh_CN: 没有能够从它们的名称中来猜测时变参数

en: 'varying' arguments must be the same length
zh_CN: 'varying'参数的长度必需相同

en: there are records with missing times, which will be dropped.
zh_CN: 含缺失时间的记录忽略不用。

en: some constant variables (%s) are really varying
zh_CN: 某些恆量变量(%s)其实在变动

en: no 'reshapeWide' attribute, must specify 'varying'
zh_CN: 没有'reshapeWide'这个属性，必需指定varying'

en: 'varying' must be nonempty list or vector
zh_CN: 'varying'必需是非空列表或向量

en: length of 'v.names' does not evenly divide length of 'varying'
zh_CN: 'v.names'的长度不能整除'varying'的长度

en: length of 'varying' must be the product of length of 'v.names' and length of 'times'
zh_CN: 'varying'的长度必需是'v.names'和'times'长度的乘積

en: 'k' must be positive
zh_CN: 'k'必需是正数

en: 'k' must be odd!  Changing 'k' to %d
zh_CN: 'k'只能是奇数! 把'k'改成%d

en: 'k' is bigger than 'n'!  Changing 'k' to %d
zh_CN: 'k'比'n'大! 把'k'改成%d

en: bandwidth 'k' must be >= 1 and odd!
zh_CN: 頻寬'k'必需大于等于一而且还必需是奇数!

en: argument 'object' has an impossible length
zh_CN: 参数'object'的长度不可能取这个值

en: no 'getInitial' method found for "%s" objects
zh_CN: 找不到"%s" 对象的'getInitial'方法

en: sample size must be between 3 and 5000
zh_CN: 样本大小必需在3和5000之间

en: all 'x' values are identical
zh_CN: 所有'x'的值都是一样的

en: attempt to smooth non-numeric values
zh_CN: 不能平滑非数值数据

en: attempt to smooth NA values
zh_CN: 不能平滑NA值

en: invalid 'endrule' argument
zh_CN: 'endrule'参数无效

en: invalid 'control.spar'
zh_CN: 'control.spar'无效

en: missing or infinite values in inputs are not allowed
zh_CN: 输入里是不允许有缺失值或无限值

en: invalid number of points
zh_CN: 点的数目无效

en: lengths of 'x' and 'w' must match
zh_CN: 'x'和'w'的长度必需相等

en: all weights should be non-negative
zh_CN: 所有的权数都必需是非负值

en: some weights should be positive
zh_CN: 至少有一些权数应该是正的

en: 'tol' must be strictly positive and finite
zh_CN: 'tol'必需是有限的正数

en: need at least four unique 'x' values
zh_CN: 至少需要四个相互不同的'x'值

en: 'cv' must not be NA when 'df' is specified
zh_CN: 当'df'有定义时'cv'不能是NA

en: cross-validation with non-unique 'x' values seems doubtful
zh_CN: 用有重复的'x'来做交叉验证好象不太对

en: 'all.knots' is TRUE; 'nknots' specification is disregarded
zh_CN: 'all.knots'的值为TRUE；因此'nknots'就忽略不用

en: 'nknots' must be numeric (in {1,..,n})
zh_CN: 'nknots'必需是数值(在{1,..,n}中)

en: 'nknots' must be at least 1
zh_CN: 'nknots'必需至少为一

en: cannot use more inner knots than unique 'x' values
zh_CN: 内部结点数止比相互不同的'x'数目要多

en: 'spar' must be of length 1
zh_CN: 'spar'的长度必需是一

en: NA lev[]; probably smoothing parameter 'spar' way too large!
zh_CN: NA lev[];很可能平滑参数'spar'的值太大了!

en: setting df = 1  __use with care!__
zh_CN: 设定df = 1， _小心使用!_

en: need result of smooth.spline(keep.data = TRUE)
zh_CN: 需要的是smooth.spline(keep.data=TRUE)的运算结果

en: type = "partial" is not yet implemented
zh_CN: 还没有实现type="partial"的算法

en: not a valid "smooth.spline" object
zh_CN: "smooth.spline"对象不正确

en: 'span' must be between 0 and 1.
zh_CN: 'span'必需在零和一之间。

en: 'y' must be numeric vector
zh_CN: 'y'必需是数值向量

en: number of observations in 'x' and 'y' must match.
zh_CN: 'x'和'y'里的观察量数目必需相等。

en: number of weights must match number of observations.
zh_CN: 权数数目必需和观察量数目相同。

en: 'x' must be between 0 and 1 for periodic smooth
zh_CN: 做周期平滑时'x'必需在零和一之间

en: no finite observations
zh_CN: 没有有限观察量

en: 'p' must be between 0 and 0.5
zh_CN: 'p'必需在0和0.5之间

en: length of 'p' must be 1 or equal the number of columns of 'x'
zh_CN: 'p'的长度必需是零或等于'x'的列数

en: 'x' must be a time series or an ar() fit
zh_CN: 'x'必需是时间序列或ar()拟合

en: must specify 'spans' or a valid kernel
zh_CN: 必需指定'spans'或正确的内核

en: coverage probability out of range [0,1)
zh_CN: 覆蓋概率在值域[0,1) 外

en: spline: first and last y values differ - using y[1] for both
zh_CN: spline: 第一个和最后一个y的值不相等-两个用y[1]

en: 'y' must be increasing or decreasing
zh_CN: 'y'必需增加或减少

en: 'spline' requires n >= 1
zh_CN: 'spline'需要n大于等于一

en: spline: first and last y values differ - using y[1L] for both
zh_CN: spline: 第一个和最后一个y的值不相等-因此都用y[1L] 来算

en: 'deriv' must be between 0 and 3
zh_CN: 'deriv'必需在零和三之间

en: 'x' must be *strictly* increasing (non - NA)
zh_CN: 'x'必需*严格*增加(非NA)

en: stepfun: 'x' must be ordered increasingly
zh_CN: stepfun: 'x'必需从小到大排序

en: 'x' must have length >= 1
zh_CN: 'x'的长度必需大于等于一

en: 'y' must be one longer than 'x'
zh_CN: 'y'必需比'x'长

en: no 'as.stepfun' method available for 'x'
zh_CN: 没有适用于'x'的'as.stepfun'方法

en: not a valid step function
zh_CN: 步驟函数不正确

en: 'plot.stepfun' called with wrong type of argument 'x'
zh_CN: 調用'plot.stepfun'时参数'x'的类型有误

en: only univariate series are allowed
zh_CN: 只允许单变量序列

en: series is not periodic or has less than two periods
zh_CN: 序列没有周期，或其周期小于二

en: unknown string value for s.window
zh_CN: s.window所设定的字符串值不正确

en: 'cutpoints' must be unique in 0 < cuts < 1, but are =
zh_CN: 'cutpoints'必需在0 < cuts < 1之间，而相互必需不同, 但实际上是 =

en: 'cutpoints' must be unique, but are =
zh_CN: 'cutpoints'相互必需不同，但实际上是 =

en: 'x' must be between -1 and 1
zh_CN: 'x'必需在-1和1之间

en: 'x' must be between %s and %s
zh_CN: 'x'必需在%s和%s之间

en: number of 'cutpoints' must be one less than number of symbols
zh_CN: 'cutpoints'的数目必需比符号的数目小一个

en: number of 'cutpoints' must be one more than number of symbols
zh_CN: 'cutpoints'的数目必需比符号的数目多一个

en: must have 2 'symbols' for logical 'x' argument
zh_CN: 逻辑'x'参数必需有两个'symbols'

en: invalid 'abbr.colnames'
zh_CN: 'abbr.colnames'无效

en: 'mu' must be a single number
zh_CN: 'mu'必需是单一数字

en: 'y' is missing for paired test
zh_CN: 配对检验时缺失设定'y'

en: data are essentially constant
zh_CN: 数据是恆量

en: 'main' must be TRUE, FALSE, NULL or character (vector).
zh_CN: 'main'必需是TRUE, FALSE, NULL或字符(向量) 。

en: x is not a vector or univariate time series
zh_CN: x不是向量或单变量时间序列

en: singularities in regression
zh_CN: 迴歸产生了奇异

en: 'ts' object must have one or more observations
zh_CN: 'ts'对象至少必需有一个或多个观察量

en: 'start' cannot be after 'end'
zh_CN: 'start'不能大于'end'

en: no time series supplied
zh_CN: 没有设定时间序列

en: not all series have the same frequency
zh_CN: 不是所有的序列都有相同的頻率

en: non-intersecting series
zh_CN: 非相叉序列

en: non-time series not of the correct length
zh_CN: 非时间序列的长度不正确

en: time series contains internal NAs
zh_CN: 时间序列含内部NA

en: series is corrupt, with no 'tsp' attribute
zh_CN: 系列不正确：缺少'tsp'这一属性

en: series is corrupt: length %d with 'tsp' implying %d
zh_CN: 系列不正确：长度为%d的'tsp'意味着%d

en: cannot plot more than 10 series as "multiple"
zh_CN: 无法将多于10个序列画成"multiple"

en: scatter plots only for univariate time series
zh_CN: 散佈圖只适用于单变量时间序列

en: 'xy.labels' must be logical or character
zh_CN: 'xy.labels'必需是逻辑值或是字符

en: 'frequency' not changed
zh_CN: 'frequency'没有变化

en: bad value for 'start'
zh_CN: 'start'的值不正确

en: 'start' value not changed
zh_CN: 'start'值没有变化

en: bad value for 'end'
zh_CN: 'end'值不正确

en: 'end' value not changed
zh_CN: 'end'值没有变化

en: 'start' > 'end'
zh_CN: 'start'大于'end'

en: extending time series when replacing values
zh_CN: 替換时将时间序列延长

en: times to be replaced do not match
zh_CN: 需要替换的时间不相符

en: no replacement values supplied
zh_CN: 没有设定替换值

en: too many replacement values supplied
zh_CN: 提供了太多的替换值

en: number of values supplied is not a sub-multiple of the number of values to be replaced
zh_CN: 设定的值数目不是要替换值数目的倍数

en: only replacement of elements is allowed
zh_CN: 只能提供替换值

en: 'model' must be list
zh_CN: 'model'必需是列表

en: 'n' must be strictly positive
zh_CN: 'n'必需是严格的正数

en: 'ar' part of model is not stationary
zh_CN: 模型的'ar'部分不平穩

en: burn-in 'n.start' must be as long as 'ar + ma'
zh_CN: 算法老化时'n.start'必需同'ar + ma'的大小一样

en: 'model$order' must be of length 3
zh_CN: 'model$order'的长度必需为3

en: inconsistent specification of 'ar' order
zh_CN: 'ar'度数设定有矛盾

en: inconsistent specification of 'ma' order
zh_CN: 'ma'度数设定有矛盾

en: number of differences must be a positive integer
zh_CN: 差異数目必需是正整数

en: need an object with call component
zh_CN: 需要一个有調用组件的对象

en: 'ratio' must be a single positive number
zh_CN: 'ratio'必需是单一的正数

en: 'x' and 'w' must have the same length
zh_CN: 'x'和'w'的长度必需相同

en: requested conf.level not achievable
zh_CN: 无法达到所要求的conf.level

en: cannot compute confidence interval when all observations are tied
zh_CN: 所有观察数据值都是一样的话无法计算精确的置信区间

en: %s applies only to two-way tables
zh_CN: %s只适用于双向表

en: too few distinct input values to fit an asymptotic regression model
zh_CN: 无法拟合渐近迴歸模型，因为相互不同的输入值太少

en: cannot fit an asymptotic regression model to these data
zh_CN: 无法用这些数据来拟合渐近迴歸模型

en: too few observations to fit an asymptotic regression model
zh_CN: 观察量数目太小，无法拟合渐近迴歸模型

en: too few distinct input values to fit the 'asympOff' model
zh_CN: 因为相互不同的输入值太少，无法拟合'asympOff'模型

en: too few distinct input values to fit the 'asympOrig' model
zh_CN: 因为相互不同的输入值太少，无法拟合'asympOrig'模型

en: too few distinct input values to fit a biexponential
zh_CN: 因为相互不同的输入值太少，无法拟合biexponential

en: must have length of response = length of second argument to 'SSfol'
zh_CN: 响应的长度必需同'SSfol'的第二个参数的长度相同

en: must have at least 4 observations to fit an 'SSfol' model
zh_CN: 至少要有四个观察量才能拟合'SSfol'模型

en: too few distinct input values to fit a four-parameter logistic
zh_CN: 因为相互不同的输入值太少，无法拟合四个参数的逻辑斯特模型

en: too few distinct input values to fit a logistic model
zh_CN: 因为相互不同的输入值太少，无法拟合逻辑斯特模型

en: too few distinct input values to fit a Michaelis-Menten model
zh_CN: 因为相互不同的输入值太少，无法拟合Michaelis-Menten模型

en: too few distinct input values to fit the Gompertz model
zh_CN: 因为相互不同的输入值太少，无法拟合Gompertz模型

en: too few distinct input values to fit the Weibull growth model
zh_CN: 因为相互不同的输入值太少，无法拟合Weibull成长模型

en: all 'x' values must be non-negative to fit the Weibull growth model
zh_CN: 所有'x'的值都必需是非负的才能拟合Weibull成长模型

en: using the %d/%d row from a combined fit | using the %d/%d rows from a combined fit
zh_CN[0]: 从组合拟合中用%d/%d行

en: lower scope has term %s not included in model | lower scope has terms %s not included in model
zh_CN[0]: 模型里下范围没有这些词条%s

en: upper scope has term %s not included in model | upper scope has terms %s not included in model
zh_CN[0]: 模型里上范围没有这些词条%s

en: there are %d Error terms: only 1 is allowed | there are %d Error terms: only 1 is allowed
zh_CN[0]: 有%d个错误项，但最多只允许有一个

en: unknown name %s in the 'split' list | unknown names %s in the 'split' list
zh_CN[0]: 'split'列表里的名称%s不正确

en: %d factor is too many for %d variables | %d factors are too many for %d variables
zh_CN[0]: %2$d变量不能有%1$d这么多的因子

en: 'start' must have %d row | 'start' must have %d rows
zh_CN[0]: 'start'必需有%d行

en: unable to optimize from this starting value | unable to optimize from these starting values
zh_CN[0]: 无法用这些初始值进行最佳化运算

en: X matrix has rank %d, but only %d observation | X matrix has rank %d, but only %d observations
zh_CN[0]: X矩阵的秩为%d, 但只有%d个观察量

en: did not converge in %d iteration | did not converge in %d iterations
zh_CN[0]: %d迭代仍没有聚合

en: %d missing value deleted | %d missing values deleted
zh_CN[0]: 删除了%d个缺失值

en: medpolish() did not converge in %d iteration | medpolish() did not converge in %d iterations
zh_CN[0]: medpolish()经过%d个迭代仍没有聚合

en: 'newdata' had %d row | 'newdata' had %d rows
zh_CN[0]: 'newdata'必需有%d行

en: but variable found had %d row | but variables found have %d rows
zh_CN[0]: 但变量里有%d行

en: factor %s has new level %s | factor %s has new levels %s
zh_CN[0]: 因子%s里出现了新的水平%s

en: %d observation deleted due to missingness | %d observations deleted due to missingness
zh_CN[0]: 因为不存在，%d个观察量被删除了

en: _NOT_ converged in %d iteration | _NOT_ converged in %d iterations
zh_CN[0]: %d迭代*没有*聚合

en: unrecognized control element named %s ignored | unrecognized control elements named %s ignored
zh_CN[0]: 没有这个叫%s名称的控制元素

en: fitting parameter %s without any variables | fitting parameters %s without any variables
zh_CN[0]: 没有任何变量来拟合参数%s

en: parameter %s does not occur in the model formula | parameters %s do not occur in the model formula
zh_CN[0]: 参数%s没有在模型公式里出现过

en: %d observation with NA, NaN or Inf deleted | %d observations with NAs, NaNs and/or Infs deleted
zh_CN[0]: 删除了含NA, NaN或Inf的%d个观察量

en: 'start.innov' is too short: need %d point | 'start.innov' is too short: need %d points
zh_CN[0]: 'start.innov'太短了，需要%d个点
