gofTestCensored {EnvStats} | R Documentation |
Perform a goodness-of-fit test to determine whether a data set appears to come from a normal distribution, lognormal distribution, or lognormal distribution (alternative parameterization) based on a sample of data that has been subjected to Type I or Type II censoring.
gofTestCensored(x, censored, censoring.side = "left", test = "sf",
distribution = "norm", est.arg.list = NULL,
prob.method = "hirsch-stedinger", plot.pos.con = 0.375,
keep.data = TRUE, data.name = NULL, censoring.name = NULL)
x |
numeric vector of observations.
Missing ( |
censored |
numeric or logical vector indicating which values of |
censoring.side |
character string indicating on which side the censoring occurs. The possible
values are |
test |
character string defining which goodness-of-fit test to perform. Possible values are:
The Shapiro-Wilk test is only available for singly censored data. See the DETAILS section for more information. |
distribution |
a character string denoting the abbreviation of the assumed distribution.
Only continous distributions are allowed. See the help file for
The results for the goodness-of-fit test are
identical for Also, the results for the goodness-of-fit test are
identical for |
est.arg.list |
a list of arguments to be passed to the function estimating the distribution
parameters. For example, if |
prob.method |
character string indicating what method to use to compute the plotting positions
(empirical probabilities) when The |
plot.pos.con |
numeric scalar between 0 and 1 containing the value of the plotting position
constant to use when |
keep.data |
logical scalar indicating whether to return the original data. The
default value is |
data.name |
optional character string indicating the name for the data used for argument |
censoring.name |
optional character string indicating the name for the data used for argument |
Let \underline{x} = c(x_1, x_2, \ldots, x_N)
denote a vector of N
observations from from some distribution with cdf F
.
Suppose we want to test the null hypothesis that
F
is the cdf of a normal (Gaussian) distribution with
some arbitrary mean \mu
and standard deviation \sigma
against the
alternative hypothesis that F
is the cdf of some other distribution. The
table below shows the random variable for which F
is the assumed cdf, given
the value of the argument distribution
.
Value of | Random Variable for | |
distribution | Distribution Name | which F is the cdf |
"norm" | Normal | X |
"lnorm" | Lognormal (Log-space) | log(X) |
"lnormAlt" | Lognormal (Untransformed) | log(X) |
Assume n
(0 < n < N
) of these observations are known and c
(c=N-n
) of these observations are all censored below (left-censored) or
all censored above (right-censored) at k
fixed censoring levels
T_1, T_2, \ldots, T_k; \; k \ge 1 \;\;\;\;\;\; (1)
For the case when k \ge 2
, the data are said to be Type I
multiply censored. For the case when k=1
,
set T = T_1
. If the data are left-censored
and all n
known observations are greater
than or equal to T
, or if the data are right-censored and all n
known observations are less than or equal to T
, then the data are
said to be Type I singly censored (Nelson, 1982, p.7), otherwise
they are considered to be Type I multiply censored.
Let c_j
denote the number of observations censored below or above censoring
level T_j
for j = 1, 2, \ldots, k
, so that
\sum_{i=1}^k c_j = c \;\;\;\;\;\; (2)
Let x_{(1)}, x_{(2)}, \ldots, x_{(N)}
denote the “ordered” observations,
where now “observation” means either the actual observation (for uncensored
observations) or the censoring level (for censored observations). For
right-censored data, if a censored observation has the same value as an
uncensored one, the uncensored observation should be placed first.
For left-censored data, if a censored observation has the same value as an
uncensored one, the censored observation should be placed first.
Note that in this case the quantity x_{(i)}
does not necessarily represent
the i
'th “largest” observation from the (unknown) complete sample.
Note that for singly left-censored data:
x_{(1)} = x_{(2)} = \cdots = x_{(c)} = T \;\;\;\;\;\; (3)
and for singly right-censored data:
x_{(n+1)} = x_{(n+2)} = \cdots = x_{(N)} = T \;\;\;\;\;\; (4)
Finally, let \Omega
(omega) denote the set of n
subscripts in the
“ordered” sample that correspond to uncensored observations.
Shapiro-Wilk Goodness-of-Fit Test for Singly Censored Data (test="sw"
)
Equation (8) in the help file for gofTest
shows that for the case of
complete ordered data \underline{x}
, the Shapiro-Wilk
W
-statistic is the same as
the square of the sample product-moment correlation between the vectors
\underline{a}
and \underline{x}
:
W = r(\underline{a}, \underline{x})^2 \;\;\;\;\;\; (5)
where
r(\underline{x}, \underline{y}) = \frac{\sum_{i=1}^N (x_i - \bar{x})(y_i - \bar{y})}{[\sum_{i=1}^n (x_i - \bar{x})^2 \sum_{i=1}^n (y_i - \bar{y})^2]^{1/2}} \;\;\;\;\;\;\; (6)
and \underline{a}
is defined by:
\underline{a} = \frac{\underline{m}^T V^{-1}}{[\underline{m}^T V^{-1} V^{-1} \underline{m}]^{1/2}} \;\;\;\;\;\; (7)
where ^T
denotes the transpose operator, and \underline{m}
is the vector
of expected values and V
is the variance-covariance matrix of the order
statistics of a random sample of size N
from a standard normal distribution.
That is, the values of \underline{a}
are the expected values of the standard
normal order statistics weighted by their variance-covariance matrix, and
normalized so that
\underline{a}^T \underline{a} = 1 \;\;\;\;\;\; (8)
Computing Shapiro-Wilk W-Statistic for Singly Censored Data
For the case of singly censored data, following Smith and Bain (1976) and
Verrill and Johnson (1988), Royston (1993) generalizes the Shapiro-Wilk
W
-statistic to:
W = r(\underline{a}_{\Delta}, \underline{x}_{\Delta})^2 \;\;\;\;\;\; (9)
where for left singly-censored data:
\underline{a}_{\Delta} = (a_{c+1}, a_{c+2}, \ldots, a_{N}) \;\;\;\;\;\; (10)
\underline{x}_{\Delta} = (x_{(c+1)}, x_{(c+2)}, \ldots, x_{(N)}) \;\;\;\;\;\; (11)
and for right singly-censored data:
\underline{a}_{\Delta} = (a_1, a_2, \ldots, a_n) \;\;\;\;\;\; (12)
\underline{x}_{\Delta} = (x_{(1)}, x_{(2)}, \ldots, x_{(n)}) \;\;\;\;\;\; (13)
Just like the function gofTest
,
when test="sw"
, the function gofTestCensored
uses Royston's (1992a)
approximation for the coefficients \underline{a}
(see the help file for
gofTest
).
Computing P-Values for the Shapiro-Wilk Test
Verrill and Johnson (1988) show that the asymptotic distribution of the statistic
in Equation (9) above is normal, but the rate of convergence is
“surprisingly slow” even for complete samples. They provide a table of
empirical percentiles of the distribution for the W
-statistic shown in
Equation (9) above for several sample sizes and percentages of censoring.
Based on the tables given in Verrill and Johnson (1988), Royston (1993) approximated
the 90'th, 95'th, and 99'th percentiles of the distribution of the z-statistic
computed from the W
-statistic. (The distribution of this z-statistic is
assumed to be normal, but not necessarily a standard normal.) Denote these
percentiles by Z_{0.90}
, Z_{0.95}
, and Z_{0.99}
. The true mean and
standard deviation of the z-statistic are estimated by the intercept and slope,
respectively, from the linear regression of Z_{\alpha}
on
\Phi^{-1}(\alpha)
for \alpha
= 0.9, 0.95, and 0.99, where \Phi
denotes the cumulative distribution function of the standard normal distribution.
The p-value associated with this test is then computed as:
p = 1 - \Phi(\frac{z - \mu_z}{\sigma_z}) \;\;\;\;\;\; (14)
Note: Verrill and Johnson (1988) produced their tables based on Type II censoring.
Royston's (1993) approximation to the p-value of these tests, however, should be
fairly accurate for Type I censored data as well.
Testing Goodness-of-Fit for Any Continuous Distribution
The function gofTestCensored
extends the Shapiro-Wilk test that
accounts for censoring to test for goodness-of-fit for any continuous
distribution by using the idea of Chen and Balakrishnan (1995),
who proposed a general purpose approximate goodness-of-fit test based on
the Cramer-von Mises or Anderson-Darling goodness-of-fit tests for normality.
The function gofTestCensored
modifies the approach of
Chen and Balakrishnan (1995) by using the same first 2 steps, and then
applying the Shapiro-Wilk test that accounts for censoring:
Let \underline{x} = x_1, x_2, \ldots, x_n
denote the vector of
n
ordered observations, ignoring censoring status.
Compute cumulative probabilities for each x_i
based on the
cumulative distribution function for the hypothesized distribution. That is,
compute p_i = F(x_i, \hat{\theta})
, where F(x, \theta)
denotes the
hypothesized cumulative distribution function with parameter(s) \theta
,
and \hat{\theta}
denotes the estimated parameter(s) using an estimation
method that accounts for censoring (e.g., assuming a Gamma
distribution with alternative parameterization, call the function
link{egammaAltCensored}
).
Compute standard normal deviates based on the computed cumulative
probabilities:
y_i = \Phi^{-1}(p_i)
Perform the Shapiro-Wilk goodness-of-fit test (that accounts for
censoring) on the y_i
's.
Shapiro-Francia Goodness-of-Fit Test (test="sf"
)
Equation (15) in the help file for gofTest
shows that for the complete
ordered data \underline{x}
, the Shapiro-Francia W'
-statistic is the
same as the squared Pearson correlation coefficient associated with a normal
probability plot.
Computing Shapiro-Francia W'-Statistic for Censored Data
For the case of singly censored data, following Smith and Bain (1976) and
Verrill and Johnson (1988), Royston (1993) extends the computation of the
Weisberg-Bingham Approximation to the W'
-statistic to the case of singly
censored data:
\tilde{W}' = r(\underline{c}_{\Delta}, \underline{x}_{\Delta})^2 \;\;\;\;\;\; (14)
where for left singly-censored data:
\underline{c}_{\Delta} = (c_{c+1}, c_{c+2}, \ldots, c_{N}) \;\;\;\;\;\; (15)
\underline{x}_{\Delta} = (x_{(c+1)}, x_{(c+2)}, \ldots, x_{(N)}) \;\;\;\;\;\; (16)
and for right singly-censored data:
\underline{a}_{\Delta} = (a_1, a_2, \ldots, a_n) \;\;\;\;\;\; (17)
\underline{x}_{\Delta} = (x_{(1)}, x_{(2)}, \ldots, x_{(n)}) \;\;\;\;\;\; (18)
and \underline{c}
is defined as:
\underline{c} = \frac{\underline{\tilde{m}}}{[\underline{\tilde{m}}' \underline{\tilde{m}}]^{1/2}} \;\;\;\;\;\; (19)
where
\tilde{m}_i = \Phi^{-1}(\frac{i - (3/8)}{n + (1/4)}) \;\;\;\;\;\; (20)
and \Phi
denotes the standard normal cdf. Note: Do not confuse the elements
of the vector \underline{c}
with the scalar c
which denotes the number
of censored observations. We use \underline{c}
here to be consistent with the
notation in the help file for gofTest
.
Just like the function gofTest
,
when test="sf"
, the function gofTestCensored
uses Royston's (1992a)
approximation for the coefficients \underline{c}
(see the help file for
gofTest
).
In general, the Shapiro-Francia test statistic can be extended to multiply
censored data using Equation (14) with \underline{c}_{\Delta}
defined as
the orderd values of c_i
associated with uncensored observations, and
\underline{x}_{\Delta}
defined as the ordered values of x_i
associated with uncensored observations:
\underline{c}_{\Delta} = \cup_{i \in \Omega} \;\; c_{(i)} \;\;\;\;\;\; (21)
\underline{x}_{\Delta} = \cup_{i \in \Omega} \;\; x_{(i)} \;\;\;\;\;\; (22)
and where the plotting positions in Equation (20) are replaced with any of the
plotting positions available in ppointsCensored
(see the description for the argument prob.method
).
Computing P-Values for the Shapiro-Francia Test
Verrill and Johnson (1988) show that the asymptotic distribution of the statistic
in Equation (14) above is normal, but the rate of convergence is
“surprisingly slow” even for complete samples. They provide a table of
empirical percentiles of the distribution for the \tilde{W}'
-statistic shown
in Equation (14) above for several sample sizes and percentages of censoring.
As for the Shapiro-Wilk test, based on the tables given in Verrill and Johnson (1988),
Royston (1993) approximated the 90'th, 95'th, and 99'th percentiles of the
distribution of the z-statistic computed from the \tilde{W}'
-statistic.
(The distribution of this z-statistic is
assumed to be normal, but not necessarily a standard normal.) Denote these
percentiles by Z_{0.90}
, Z_{0.95}
, and Z_{0.99}
. The true mean and
standard deviation of the z-statistic are estimated by the intercept and slope,
respectively, from the linear regression of Z_{\alpha}
on
\Phi^{-1}(\alpha)
for \alpha
= 0.9, 0.95, and 0.99, where \Phi
denotes the cumulative distribution function of the standard normal distribution.
The p-value associated with this test is then computed as:
p = 1 - \Phi(\frac{z - \mu_z}{\sigma_z}) \;\;\;\;\;\; (23)
Note: Verrill and Johnson (1988) produced their tables based on Type II censoring.
Royston's (1993) approximation to the p-value of these tests, however, should be
fairly accurate for Type I censored data as well, although this is an area that
requires further investigation.
Testing Goodness-of-Fit for Any Continuous Distribution
The function gofTestCensored
extends the Shapiro-Francia test that
accounts for censoring to test for goodness-of-fit for any continuous
distribution by using the idea of Chen and Balakrishnan (1995),
who proposed a general purpose approximate goodness-of-fit test based on
the Cramer-von Mises or Anderson-Darling goodness-of-fit tests for normality.
The function gofTestCensored
modifies the approach of
Chen and Balakrishnan (1995) by using the same first 2 steps, and then
applying the Shapiro-Francia test that accounts for censoring:
Let \underline{x} = x_1, x_2, \ldots, x_n
denote the vector of
n
ordered observations, ignoring censoring status.
Compute cumulative probabilities for each x_i
based on the
cumulative distribution function for the hypothesized distribution. That is,
compute p_i = F(x_i, \hat{\theta})
where F(x, \theta)
denotes the
hypothesized cumulative distribution function with parameter(s) \theta
,
and \hat{\theta}
denotes the estimated parameter(s) using an estimation
method that accounts for censoring (e.g., assuming a Gamma
distribution with alternative parameterization, call the function
link{egammaAltCensored}
).
Compute standard normal deviates based on the computed cumulative
probabilities:
y_i = \Phi^{-1}(p_i)
Perform the Shapiro-Francia goodness-of-fit test (that accounts for
censoring) on the y_i
's.
Probability Plot Correlation Coefficient (PPCC) Goodness-of-Fit Test (test="ppcc"
)
The function gofTestCensored
computes the PPCC test statistic using Blom
plotting positions. It can be shown that the square of this statistic is equivalent
to the Weisberg-Bingham Approximation to the Shapiro-Francia W'
-test
(Weisberg and Bingham, 1975; Royston, 1993). Thus the PPCC goodness-of-fit test
is equivalent to the Shapiro-Francia goodness-of-fit test.
a list of class "gofCensored"
containing the results of the goodness-of-fit
test. See the help files for gofCensored.object
for details.
The Shapiro-Wilk test (Shapiro and Wilk, 1965) and the Shapiro-Francia test (Shapiro and Francia, 1972) are probably the two most commonly used hypothesis tests to test departures from normality. The Shapiro-Wilk test is most powerful at detecting short-tailed (platykurtic) and skewed distributions, and least powerful against symmetric, moderately long-tailed (leptokurtic) distributions. Conversely, the Shapiro-Francia test is more powerful against symmetric long-tailed distributions and less powerful against short-tailed distributions (Royston, 1992b; 1993).
In practice, almost any goodness-of-fit test will not reject the null hypothesis
if the number of observations is relatively small. Conversely, almost any goodness-of-fit
test will reject the null hypothesis if the number of observations is very large,
since “real” data are never distributed according to any theoretical distribution
(Conover, 1980, p.367). For most cases, however, the distribution of “real” data
is close enough to some theoretical distribution that fairly accurate results may be
provided by assuming that particular theoretical distribution. One way to asses the
goodness of the fit is to use goodness-of-fit tests. Another way is to look at
quantile-quantile (Q-Q) plots (see qqPlotCensored
).
Steven P. Millard (EnvStats@ProbStatInfo.com)
Birnbaum, Z.W., and F.H. Tingey. (1951). One-Sided Confidence Contours for Probability Distribution Functions. Annals of Mathematical Statistics 22, 592-596.
Blom, G. (1958). Statistical Estimates and Transformed Beta Variables. John Wiley and Sons, New York.
Conover, W.J. (1980). Practical Nonparametric Statistics. Second Edition. John Wiley and Sons, New York.
Dallal, G.E., and L. Wilkinson. (1986). An Analytic Approximation to the Distribution of Lilliefor's Test for Normality. The American Statistician 40, 294-296.
D'Agostino, R.B. (1970). Transformation to Normality of the Null Distribution of g1
.
Biometrika 57, 679-681.
D'Agostino, R.B. (1971). An Omnibus Test of Normality for Moderate and Large Size Samples. Biometrika 58, 341-348.
D'Agostino, R.B. (1986b). Tests for the Normal Distribution. In: D'Agostino, R.B., and M.A. Stephens, eds. Goodness-of Fit Techniques. Marcel Dekker, New York.
D'Agostino, R.B., and E.S. Pearson (1973). Tests for Departures from Normality.
Empirical Results for the Distributions of b2
and \sqrt{b1}
.
Biometrika 60(3), 613-622.
D'Agostino, R.B., and G.L. Tietjen (1973). Approaches to the Null Distribution of \sqrt{b1}
.
Biometrika 60(1), 169-173.
Fisher, R.A. (1950). Statistical Methods for Research Workers. 11'th Edition. Hafner Publishing Company, New York, pp.99-100.
Gibbons, R.D., D.K. Bhaumik, and S. Aryal. (2009). Statistical Methods for Groundwater Monitoring, Second Edition. John Wiley & Sons, Hoboken.
Kendall, M.G., and A. Stuart. (1991). The Advanced Theory of Statistics, Volume 2: Inference and Relationship. Fifth Edition. Oxford University Press, New York.
Royston, J.P. (1992a). Approximating the Shapiro-Wilk W-Test for Non-Normality. Statistics and Computing 2, 117-119.
Royston, J.P. (1992b). Estimation, Reference Ranges and Goodness of Fit for the Three-Parameter Log-Normal Distribution. Statistics in Medicine 11, 897-912.
Royston, J.P. (1992c). A Pocket-Calculator Algorithm for the Shapiro-Francia Test of Non-Normality: An Application to Medicine. Statistics in Medicine 12, 181-184.
Royston, P. (1993). A Toolkit for Testing for Non-Normality in Complete and Censored Samples. The Statistician 42, 37-43.
Ryan, T., and B. Joiner. (1973). Normal Probability Plots and Tests for Normality. Technical Report, Pennsylvannia State University, Department of Statistics.
Shapiro, S.S., and R.S. Francia. (1972). An Approximate Analysis of Variance Test for Normality. Journal of the American Statistical Association 67(337), 215-219.
Shapiro, S.S., and M.B. Wilk. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika 52, 591-611.
Verrill, S., and R.A. Johnson. (1987). The Asymptotic Equivalence of Some Modified Shapiro-Wilk Statistics – Complete and Censored Sample Cases. The Annals of Statistics 15(1), 413-419.
Verrill, S., and R.A. Johnson. (1988). Tables and Large-Sample Distribution Theory for Censored-Data Correlation Statistics for Testing Normality. Journal of the American Statistical Association 83, 1192-1197.
Weisberg, S., and C. Bingham. (1975). An Approximate Analysis of Variance Test for Non-Normality Suitable for Machine Calculation. Technometrics 17, 133-134.
gofTest
, gofCensored.object
,
print.gofCensored
, plot.gofCensored
,
shapiro.test
, Normal, Lognormal,
enormCensored
, elnormCensored
,
elnormAltCensored
, qqPlotCensored
.
# Generate 30 observations from a gamma distribution with
# parameters mean=10 and cv=1 and then censor observations less than 5.
# Then test the hypothesis that these data came from a gamma
# distribution using the Shapiro-Wilk test.
#
# The p-value for the complete data is p = 0.86, while
# the p-value for the censored data is p = 0.52.
# (Note: the call to set.seed lets you reproduce this example.)
set.seed(598)
dat <- sort(rgammaAlt(30, mean = 10, cv = 1))
dat
# [1] 0.5313509 1.4741833 1.9936208 2.7980636 3.4509840
# [6] 3.7987348 4.5542952 5.5207531 5.5253596 5.7177872
#[11] 5.7513827 9.1086375 9.8444090 10.6247123 10.9304922
#[16] 11.7925398 13.3432689 13.9562777 14.6029065 15.0563342
#[21] 15.8730642 16.0039936 16.6910715 17.0288922 17.8507891
#[26] 19.1105522 20.2657141 26.3815970 30.2912797 42.8726101
dat.censored <- dat
censored <- dat.censored < 5
dat.censored[censored] <- 5
# Results for complete data:
#---------------------------
gofTest(dat, test = "sw", dist = "gammaAlt")
#Results of Goodness-of-Fit Test
#-------------------------------
#
#Test Method: Shapiro-Wilk GOF Based on
# Chen & Balakrisnan (1995)
#
#Hypothesized Distribution: Gamma
#
#Estimated Parameter(s): mean = 12.4248552
# cv = 0.7901752
#
#Estimation Method: MLE
#
#Data: dat
#
#Sample Size: 30
#
#Test Statistic: W = 0.981471
#
#Test Statistic Parameter: n = 30
#
#P-value: 0.8631802
#
#Alternative Hypothesis: True cdf does not equal the
# Gamma Distribution.
# Results for censored data:
#---------------------------
gof.list <- gofTestCensored(dat.censored, censored, test = "sw",
distribution = "gammaAlt")
gof.list
#Results of Goodness-of-Fit Test
#Based on Type I Censored Data
#-------------------------------
#
#Test Method: Shapiro-Wilk GOF
# (Singly Censored Data)
# Based on Chen & Balakrisnan (1995)
#
#Hypothesized Distribution: Gamma
#
#Censoring Side: left
#
#Censoring Level(s): 5
#
#Estimated Parameter(s): mean = 12.4911448
# cv = 0.7617343
#
#Estimation Method: MLE
#
#Data: dat.censored
#
#Censoring Variable: censored
#
#Sample Size: 30
#
#Percent Censored: 23.3%
#
#Test Statistic: W = 0.9613711
#
#Test Statistic Parameters: N = 30.0000000
# DELTA = 0.2333333
#
#P-value: 0.522329
#
#Alternative Hypothesis: True cdf does not equal the
# Gamma Distribution.
# Plot the results for the censored data
#---------------------------------------
dev.new()
plot(gof.list)
#==========
# Continue the above example, but now test the hypothesis that
# these data came from a lognormal distribution
# (alternative parameterization) using the Shapiro-Wilk test.
#
# The p-value for the complete data is p = 0.056, while
# the p-value for the censored data is p = 0.11.
# Results for complete data:
#---------------------------
gofTest(dat, test = "sw", dist = "lnormAlt")
#Results of Goodness-of-Fit Test
#-------------------------------
#
#Test Method: Shapiro-Wilk GOF
#
#Hypothesized Distribution: Lognormal
#
#Estimated Parameter(s): mean = 13.757239
# cv = 1.148872
#
#Estimation Method: mvue
#
#Data: dat
#
#Sample Size: 30
#
#Test Statistic: W = 0.9322226
#
#Test Statistic Parameter: n = 30
#
#P-value: 0.05626823
#
#Alternative Hypothesis: True cdf does not equal the
# Lognormal Distribution.
# Results for censored data:
#---------------------------
gof.list <- gofTestCensored(dat.censored, censored, test = "sw",
distribution = "lnormAlt")
gof.list
#Results of Goodness-of-Fit Test
#Based on Type I Censored Data
#-------------------------------
#
#Test Method: Shapiro-Wilk GOF
# (Singly Censored Data)
#
#Hypothesized Distribution: Lognormal
#
#Censoring Side: left
#
#Censoring Level(s): 5
#
#Estimated Parameter(s): mean = 13.0382221
# cv = 0.9129512
#
#Estimation Method: MLE
#
#Data: dat.censored
#
#Censoring Variable: censored
#
#Sample Size: 30
#
#Percent Censored: 23.3%
#
#Test Statistic: W = 0.9292406
#
#Test Statistic Parameters: N = 30.0000000
# DELTA = 0.2333333
#
#P-value: 0.114511
#
#Alternative Hypothesis: True cdf does not equal the
# Lognormal Distribution.
# Plot the results for the censored data
#---------------------------------------
dev.new()
plot(gof.list)
#----------
# Redo the above example, but specify the quasi-minimum variance
# unbiased estimator of the mean. Note that the method of
# estimating the parameters has no effect on the goodness-of-fit
# test (see the DETAILS section above).
gofTestCensored(dat.censored, censored, test = "sw",
distribution = "lnormAlt", est.arg.list = list(method = "qmvue"))
#Results of Goodness-of-Fit Test
#Based on Type I Censored Data
#-------------------------------
#
#Test Method: Shapiro-Wilk GOF
# (Singly Censored Data)
#
#Hypothesized Distribution: Lognormal
#
#Censoring Side: left
#
#Censoring Level(s): 5
#
#Estimated Parameter(s): mean = 12.8722749
# cv = 0.8712549
#
#Estimation Method: Quasi-MVUE
#
#Data: dat.censored
#
#Censoring Variable: censored
#
#Sample Size: 30
#
#Percent Censored: 23.3%
#
#Test Statistic: W = 0.9292406
#
#Test Statistic Parameters: N = 30.0000000
# DELTA = 0.2333333
#
#P-value: 0.114511
#
#Alternative Hypothesis: True cdf does not equal the
# Lognormal Distribution.
#----------
# Clean up
rm(dat, dat.censored, censored, gof.list)
graphics.off()
#==========
# Check the assumption that the silver data stored in Helsel.Cohn.88.silver.df
# follows a lognormal distribution and plot the goodness-of-fit test results.
# Note that the small p-value and the shape of the Q-Q plot
# (an inverted S-shape) suggests that the log transformation is not quite strong
# enough to "bring in" the tails (i.e., the log-transformed silver data has tails
# that are slightly too long relative to a normal distribution).
# Helsel and Cohn (1988, p.2002) note that the gross outlier of 560 mg/L tends to
# make the shape of the data resemble a gamma distribution.
dum.list <- with(Helsel.Cohn.88.silver.df,
gofTestCensored(Ag, Censored, test = "sf", dist = "lnorm"))
dum.list
#Results of Goodness-of-Fit Test
#Based on Type I Censored Data
#-------------------------------
#
#Test Method: Shapiro-Francia GOF
# (Multiply Censored Data)
#
#Hypothesized Distribution: Lognormal
#
#Censoring Side: left
#
#Censoring Level(s): 0.1 0.2 0.3 0.5 1.0 2.0 2.5 5.0
# 6.0 10.0 20.0 25.0
#
#Estimated Parameter(s): meanlog = -1.040572
# sdlog = 2.354847
#
#Estimation Method: MLE
#
#Data: Ag
#
#Censoring Variable: Censored
#
#Sample Size: 56
#
#Percent Censored: 60.7%
#
#Test Statistic: W = 0.8957198
#
#Test Statistic Parameters: N = 56.0000000
# DELTA = 0.6071429
#
#P-value: 0.03490314
#
#Alternative Hypothesis: True cdf does not equal the
# Lognormal Distribution.
dev.new()
plot(dum.list)
#----------
# Clean up
#---------
rm(dum.list)
graphics.off()
#==========
# Chapter 15 of USEPA (2009) gives several examples of looking
# at normal Q-Q plots and estimating the mean and standard deviation
# for manganese concentrations (ppb) in groundwater at five background wells.
# In EnvStats these data are stored in the data frame
# EPA.09.Ex.15.1.manganese.df.
# Here we will test whether the data appear to come from a normal
# distribution, then we will test to see whether they appear to come
# from a lognormal distribution.
#--------------------------------------------------------------------
# First look at the data:
#-----------------------
EPA.09.Ex.15.1.manganese.df
# Sample Well Manganese.Orig.ppb Manganese.ppb Censored
#1 1 Well.1 <5 5.0 TRUE
#2 2 Well.1 12.1 12.1 FALSE
#3 3 Well.1 16.9 16.9 FALSE
#...
#23 3 Well.5 3.3 3.3 FALSE
#24 4 Well.5 8.4 8.4 FALSE
#25 5 Well.5 <2 2.0 TRUE
longToWide(EPA.09.Ex.15.1.manganese.df,
"Manganese.Orig.ppb", "Sample", "Well",
paste.row.name = TRUE)
# Well.1 Well.2 Well.3 Well.4 Well.5
#Sample.1 <5 <5 <5 6.3 17.9
#Sample.2 12.1 7.7 5.3 11.9 22.7
#Sample.3 16.9 53.6 12.6 10 3.3
#Sample.4 21.6 9.5 106.3 <2 8.4
#Sample.5 <2 45.9 34.5 77.2 <2
# Now test whether the data appear to come from
# a normal distribution. Note that these data
# are multiply censored, so we'll use the
# Shapiro-Francia test.
#----------------------------------------------
gof.normal <- with(EPA.09.Ex.15.1.manganese.df,
gofTestCensored(Manganese.ppb, Censored, test = "sf"))
gof.normal
#Results of Goodness-of-Fit Test
#Based on Type I Censored Data
#-------------------------------
#
#Test Method: Shapiro-Francia GOF
# (Multiply Censored Data)
#
#Hypothesized Distribution: Normal
#
#Censoring Side: left
#
#Censoring Level(s): 2 5
#
#Estimated Parameter(s): mean = 15.23508
# sd = 30.62812
#
#Estimation Method: MLE
#
#Data: Manganese.ppb
#
#Censoring Variable: Censored
#
#Sample Size: 25
#
#Percent Censored: 24%
#
#Test Statistic: W = 0.8368016
#
#Test Statistic Parameters: N = 25.00
# DELTA = 0.24
#
#P-value: 0.004662658
#
#Alternative Hypothesis: True cdf does not equal the
# Normal Distribution.
# Plot the results:
#------------------
dev.new()
plot(gof.normal)
#----------
# Now test to see whether the data appear to come from
# a lognormal distribuiton.
#-----------------------------------------------------
gof.lognormal <- with(EPA.09.Ex.15.1.manganese.df,
gofTestCensored(Manganese.ppb, Censored, test = "sf",
distribution = "lnorm"))
gof.lognormal
#Results of Goodness-of-Fit Test
#Based on Type I Censored Data
#-------------------------------
#
#Test Method: Shapiro-Francia GOF
# (Multiply Censored Data)
#
#Hypothesized Distribution: Lognormal
#
#Censoring Side: left
#
#Censoring Level(s): 2 5
#
#Estimated Parameter(s): meanlog = 2.215905
# sdlog = 1.356291
#
#Estimation Method: MLE
#
#Data: Manganese.ppb
#
#Censoring Variable: Censored
#
#Sample Size: 25
#
#Percent Censored: 24%
#
#Test Statistic: W = 0.9864426
#
#Test Statistic Parameters: N = 25.00
# DELTA = 0.24
#
#P-value: 0.9767731
#
#Alternative Hypothesis: True cdf does not equal the
# Lognormal Distribution.
# Plot the results:
#------------------
dev.new()
plot(gof.lognormal)
#----------
# Clean up
#---------
rm(gof.normal, gof.lognormal)
graphics.off()