Generate missing data for simulation purposes

This function generates multivariate missing data under a MCAR, MAR or MNAR missing data mechanism. Imputation of data sets containing missing values can be performed with mice.

Usage

ampute(
  data,
  prop = 0.5,
  patterns = NULL,
  freq = NULL,
  mech = "MAR",
  weights = NULL,
  std = TRUE,
  cont = TRUE,
  type = NULL,
  odds = NULL,
  bycases = TRUE,
  run = TRUE
)

Arguments

data: A complete data matrix or data frame. Values should be numeric. Categorical variables should have been transformed to dummies.
prop: A scalar specifying the proportion of missingness. Should be a value between 0 and 1. Default is a missingness proportion of 0.5.
patterns: A matrix or data frame of size #patterns by #variables where 0 indicates that a variable should have missing values and 1 indicates that a variable should remain complete. The user may specify as many patterns as desired. One pattern (a vector) is possible as well. Default is a square matrix of size #variables where each pattern has missingness on one variable only (created with ampute.default.patterns). After the amputation procedure, md.pattern can be used to investigate the missing data patterns in the data.
freq: A vector of length #patterns containing the relative frequency with which the patterns should occur. For example, for three missing data patterns, the vector could be c(0.4, 0.4, 0.2), meaning that of all cases with missing values, 40 percent should have pattern 1, 40 percent pattern 2 and 20 percent pattern 3. The vector should sum to 1. Default is an equal probability for each pattern, created with ampute.default.freq.
mech: A string specifying the missingness mechanism, either "MCAR" (Missing Completely At Random), "MAR" (Missing At Random) or "MNAR" (Missing Not At Random). Default is a MAR missingness mechanism.
weights: A matrix or data frame of size #patterns by #variables. The matrix contains the weights that will be used to calculate the weighted sum scores. For a MAR mechanism, the weights of the variables that will be made incomplete should be zero. For a MNAR mechanism, these weights could have any possible value. Furthermore, the weights may differ between patterns and between variables. They may be negative as well. Within each pattern, the relative size of the values are of importance. The default weights matrix is made with ampute.default.weights and returns a matrix with equal weights for all variables. In case of MAR, variables that will be amputed will be weighted with 0. For MNAR, variables that will be observed will be weighted with 0. If the mechanism is MCAR, the weights matrix will not be used.
std: Logical. Whether the weighted sum scores should be calculated with standardized data or with non-standardized data. The latter is especially advised when making use of train and test sets in order to prevent leakage.
cont: Logical. Whether the probabilities should be based on a continuous or a discrete distribution. If TRUE, the probabilities of being missing are based on a continuous logistic distribution function. ampute.continuous will be used to calculate and assign the probabilities. These probabilities will then be based on the argument type. If FALSE, the probabilities of being missing are based on a discrete distribution (ampute.discrete) based on the odds argument. Default is TRUE.
type: A string or vector of strings containing the type of missingness for each pattern. Either "LEFT", "MID", "TAIL" or '"RIGHT". If a single missingness type is given, all patterns will be created with the same type. If the missingness types should differ between patterns, a vector of missingness types should be given. Default is RIGHT for all patterns and is the result of ampute.default.type.
odds: A matrix where #patterns defines the #rows. Each row should contain the odds of being missing for the corresponding pattern. The number of odds values defines in how many quantiles the sum scores will be divided. The odds values are relative probabilities: a quantile with odds value 4 will have a probability of being missing that is four times higher than a quantile with odds 1. The number of quantiles may differ between the patterns, specify NA for cells remaining empty. Default is 4 quantiles with odds values 1, 2, 3 and 4 and is created by ampute.default.odds.
bycases: Logical. If TRUE, the proportion of missingness is defined in terms of cases. If FALSE, the proportion of missingness is defined in terms of cells. Default is TRUE.
run: Logical. If TRUE, the amputations are implemented. If FALSE, the return object will contain everything except for the amputed data set.

Value

Returns an S3 object of class mads (multivariate amputed data set)

Details

This function generates missing values in complete data sets. Amputation of complete data sets is useful for the evaluation of imputation techniques, such as multiple imputation (performed with function mice in this package).

The basic strategy underlying multivariate imputation was suggested by Don Rubin during discussions in the 90's. Brand (1997) created one particular implementation, and his method found its way into the FCS paper (Van Buuren et al, 2006).

Until recently, univariate amputation procedures were used to generate missing data in complete, simulated data sets. With this approach, variables are made incomplete one variable at a time. When more than one variable needs to be amputed, the procedure is repeated multiple times.

With the univariate approach, it is difficult to relate the missingness on one variable to the missingness on another variable. A multivariate amputation procedure solves this issue and moreover, it does justice to the multivariate nature of data sets. Hence, ampute is developed to perform multivariate amputation.

The idea behind the function is the specification of several missingness patterns. Each pattern is a combination of variables with and without missing values (denoted by 0 and 1 respectively). For example, one might want to create two missingness patterns on a data set with four variables. The patterns could be something like: 0,0,1,1 and 1,0,1,0. Each combination of zeros and ones may occur.

Furthermore, the researcher specifies the proportion of missingness, either the proportion of missing cases or the proportion of missing cells, and the relative frequency each pattern occurs. Consequently, the data is split into multiple subsets, one subset per pattern. Now, each case is candidate for a certain missingness pattern, but whether the case will have missing values eventually depends on other specifications.

The first of these specifications is the missing mechanism. There are three possible mechanisms: the missingness depends completely on chance (MCAR), the missingness depends on the values of the observed variables (i.e. the variables that remain complete) (MAR) or on the values of the variables that will be made incomplete (MNAR).

When the user specifies the missingness mechanism to be "MCAR", the candidates have an equal probability of becoming incomplete. For a "MAR" or "MNAR" mechanism, weighted sum scores are calculated. These scores are a linear combination of the variables.

In order to calculate the weighted sum scores, the data is standardized. For this reason, the data has to be numeric. Second, for each case, the values in the data set are multiplied with the weights, specified by argument weights. These weighted scores will be summed, resulting in a weighted sum score for each case.

The weights may differ between patterns and they may be negative or zero as well. Naturally, in case of a MAR mechanism, the weights corresponding to the variables that will be made incomplete, have a 0. Note that this may be different for each pattern. In case of MNAR missingness, especially the weights of the variables that will be made incomplete are of importance. However, the other variables may be weighted as well.

It is the relative difference between the weights that will result in an effect in the sum scores. For example, for the first missing data pattern mentioned above, the weights for the third and fourth variables could be set to 2 and 4. However, weight values of 0.2 and 0.4 will have the exact same effect on the weighted sum score: the fourth variable is weighted twice as much as variable 3.

Based on the weighted sum scores, either a discrete or continuous distribution of probabilities is used to calculate whether a candidate will have missing values.

For a discrete distribution of probabilities, the weighted sum scores are divided into subgroups of equal size (quantiles). Thereafter, the user specifies for each subgroup the odds of being missing. Both the number of subgroups and the odds values are important for the generation of missing data. For example, for a RIGHT-like mechanism, scoring in one of the higher quantiles should have high missingness odds, whereas for a MID-like mechanism, the central groups should have higher odds. Again, not the size of the odds values are of importance, but the relative distance between the values.

The continuous distributions of probabilities are based on the logistic distribution function. The user can specify the type of missingness, which, again, may differ between patterns.

For an example and more explanation about how the arguments interact with each other, we refer to the vignette: Generate missing values with ampute.

References

Brand, J.P.L. (1999) Development, implementation and evaluation of multiple imputation strategies for the statistical analysis of incomplete data sets. pp. 110-113. Dissertation. Rotterdam: Erasmus University.

Schouten, R.M., Lugtig, P and Vink, G. (2018) Generating missing values for simulation purposes: A multivariate amputation procedure. Journal of Statistical Computation and Simulation, 88(15): 1909-1930. doi:10.1080/00949655.2018.1491577

Schouten, R.M. and Vink, G. (2018) The Dance of the Mechanisms: How Observed Information Influences the Validity of Missingness Assumptions. Sociological Methods and Research, 50(3): 1243-1258. doi:10.1177/0049124118799376

Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn, C.G.M., Rubin, D.B. (2006) Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76(12): 1049-1064. doi:10.1080/10629360600810434

Van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Boca Raton, FL.

Vink, G. (2016) Towards a standardized evaluation of multiple imputation routines.

Author

Rianne Schouten, Gerko Vink, Peter Lugtig, 2016

Examples

# start with a complete data set
compl_boys <- cc(boys)[1:3]

# Perform amputation with default settings
mads_boys <- ampute(data = compl_boys)
mads_boys$amp
#>         age   hgt   wgt
#> 3279  8.859 124.8  31.0
#> 3283  8.867 145.0  38.2
#> 3296  8.908 137.8  30.0
#> 3321  8.999 136.3  26.9
#> 3323  9.004 151.2  48.2
#> 3327  9.021 141.4  29.4
#> 3357  9.119 140.0  28.0
#> 3388  9.201 125.8    NA
#> 3398  9.234 139.8  35.6
#> 3409  9.270 140.4  32.0
#> 3416  9.303    NA  31.6
#> 3422  9.316 147.4  31.4
#> 3429  9.368 132.7  25.9
#> 3442  9.407 134.4    NA
#> 3449  9.426 146.0  36.5
#> 3455  9.451 136.0  27.5
#> 3460  9.459 142.7  30.8
#> 3481  9.511 144.5  30.3
#> 3484  9.514 140.3  27.8
#> 3486  9.514 138.0  31.0
#> 3494  9.524 140.9  32.7
#> 3525  9.582 134.0  27.5
#> 3533  9.604 139.7  32.6
#> 3547  9.631 139.7    NA
#> 3609  9.834 142.0  30.3
#> 3651  9.990 149.0  37.3
#> 3664 10.020 137.2  31.7
#> 3710 10.132 134.0  26.5
#> 3721     NA 139.3  30.6
#> 3724 10.160 141.3  39.5
#> 3727     NA 135.2  31.9
#> 3805 10.398 149.8  34.7
#> 3814 10.422 158.8  39.8
#> 3827 10.447 148.7  41.0
#> 3834 10.477 142.6  32.5
#> 3841 10.499 148.6  38.6
#> 3865 10.554 146.3  40.4
#> 3873 10.568 151.0  36.6
#> 3880 10.581 141.2  33.8
#> 3929 10.724 144.1  29.5
#> 3975 10.888 147.0  33.8
#> 3988 10.945 149.0  45.6
#> 3991     NA 145.1  36.2
#> 3994 10.967 137.4  29.6
#> 3995 10.970 151.2  39.2
#> 4006 11.003 134.3  29.1
#> 4009 11.011    NA  44.2
#> 4059 11.126    NA  32.7
#> 4066 11.143 135.1  25.0
#> 4067 11.143 148.3  41.5
#> 4070 11.156 163.0  44.5
#> 4072 11.159 144.5  49.7
#> 4102 11.222 151.8  44.4
#> 4122 11.288 159.4  43.4
#> 4173 11.446 147.9  42.2
#> 4174 11.446 152.8  43.1
#> 4186 11.482 148.7  37.2
#> 4211 11.545 153.2  42.5
#> 4238 11.605 155.2  36.7
#> 4240 11.611 151.0    NA
#> 4253 11.655 160.6  44.4
#> 4255 11.665 144.5  30.8
#> 4266 11.690 148.0    NA
#> 4293 11.759    NA  42.8
#> 4301 11.789 135.4  29.7
#> 4302 11.791 152.8  43.5
#> 4312 11.811 145.9  34.8
#> 4318 11.827 151.0  33.0
#> 4332 11.874 152.0    NA
#> 4349 11.926 156.5  44.5
#> 4399 12.071 151.1    NA
#> 4465 12.265 156.6  43.3
#> 4481 12.292 145.8  39.2
#> 4487 12.303    NA  44.0
#> 4505     NA 157.2  61.0
#> 4532 12.457 161.4    NA
#> 4552 12.501    NA  53.4
#> 4561 12.520 162.1  44.1
#> 4579 12.574 163.8  51.6
#> 4585 12.583    NA  52.6
#> 4591 12.599 155.0  39.0
#> 4646 12.741 172.0  79.5
#> 4682 12.821    NA  56.0
#> 4721 12.933 169.5  54.8
#> 4727 12.944 157.0  41.2
#> 4745     NA 148.7  31.1
#> 4748 12.993 155.9  42.3
#> 4752 12.996    NA  49.1
#> 4809 13.108 164.0  61.7
#> 4823 13.127 175.0  65.1
#> 4824 13.127    NA  57.8
#> 4825 13.130 156.4  40.8
#> 4847 13.188 168.1    NA
#> 4848 13.190 155.4  42.1
#> 4887 13.275 161.2  37.0
#> 4892 13.300 165.5  41.9
#> 4961 13.489 161.3    NA
#> 4994 13.552 157.7  46.2
#> 5039 13.631 179.0    NA
#> 5044 13.642 153.4  40.6
#> 5048     NA 175.4  74.8
#> 5064 13.686 168.7    NA
#> 5085 13.749 155.5  36.5
#> 5113 13.839 162.1  44.9
#> 5126 13.883 176.2  48.1
#> 5130 13.891 174.6  54.2
#> 5133 13.897 181.7  61.9
#> 5147 13.924 144.8    NA
#> 5159 13.938 156.9  50.0
#> 5206 14.045 170.0  54.7
#> 5219 14.069 170.6  59.2
#> 5228 14.083 172.1  50.9
#> 5247     NA 159.2  42.7
#> 5288 14.209 170.9    NA
#> 5293 14.220 165.5  48.0
#> 5327 14.297 153.2  44.3
#> 5335 14.308    NA  56.0
#> 5343 14.332 164.1  49.1
#> 5367 14.412    NA  54.2
#> 5410 14.527    NA  52.0
#> 5415 14.540 182.4  76.0
#> 5416 14.543 173.7  69.5
#> 5417     NA 176.4  51.0
#> 5420 14.546 167.3  61.0
#> 5478 14.669 182.9  57.2
#> 5496 14.721 169.1    NA
#> 5509     NA 168.6  47.6
#> 5520 14.811 173.8    NA
#> 5522 14.811    NA  66.5
#> 5539 14.844 172.7  64.3
#> 5551 14.863 169.1    NA
#> 5567 14.926 177.4    NA
#> 5585 14.967 174.1  88.0
#> 5598 14.997 181.2    NA
#> 5602     NA 188.0  91.6
#> 5610 15.025 185.5  62.7
#> 5612 15.028 178.5    NA
#> 5642 15.099 178.1  74.5
#> 5654 15.129 176.9  58.6
#> 5675 15.162 176.8  54.8
#> 5710 15.249 188.0    NA
#> 5714 15.266 175.2  62.5
#> 5763 15.411 173.3  54.1
#> 5764 15.416 187.2  80.6
#> 5789 15.474    NA  80.2
#> 5806 15.504 172.0    NA
#> 5823 15.542 171.0  50.0
#> 5830     NA 183.3  61.5
#> 5856 15.622    NA  70.5
#> 5857 15.630 174.3  52.6
#> 5858 15.633 186.0  67.0
#> 5879 15.663 172.7  58.6
#> 5880     NA 176.0  63.8
#> 5883 15.674 176.6    NA
#> 5947 15.838 177.0    NA
#> 5964 15.893 168.6  56.0
#> 5971     NA 176.2  57.5
#> 5975 15.912 180.0    NA
#> 5986     NA 167.8  62.2
#> 6005 15.989 187.8    NA
#> 6029     NA 186.7  70.6
#> 6033 16.062    NA  61.5
#> 6036     NA 184.4  68.5
#> 6037 16.068    NA  70.7
#> 6064 16.156    NA 113.0
#> 6083     NA 185.4  60.0
#> 6085 16.246 183.5  76.0
#> 6092     NA 177.9  57.0
#> 6117 16.355 171.0  59.1
#> 6132 16.402 173.6  54.5
#> 6138 16.427 195.5  69.0
#> 6141     NA 175.1  64.5
#> 6166     NA 188.0  62.5
#> 6185     NA 178.0  65.7
#> 6251 16.717 180.2    NA
#> 6253 16.720 192.8  88.3
#> 6262 16.741    NA  70.3
#> 6283     NA 184.3  77.0
#> 6343 16.966 182.4  63.7
#> 6361 16.999 179.0    NA
#> 6372 17.018    NA  65.5
#> 6416 17.117 183.2  69.3
#> 6482 17.333 180.3    NA
#> 6483     NA 183.9  66.3
#> 6528 17.440 171.4  71.7
#> 6539     NA 173.6  55.5
#> 6567 17.560    NA  71.2
#> 6611 17.678    NA  72.8
#> 6641 17.749    NA  94.9
#> 6647 17.757 196.2  81.0
#> 6686     NA 181.2  86.8
#> 6700 17.957 172.2    NA
#> 6756 18.121 171.6    NA
#> 6782 18.209 177.9    NA
#> 6789     NA 187.4  79.0
#> 6831     NA 193.6  69.2
#> 6858 18.453 170.5    NA
#> 6892 18.551    NA  71.7
#> 6923     NA 188.0  61.9
#> 6963 18.737 191.0    NA
#> 6964 18.743 192.0    NA
#> 6977 18.773 189.4    NA
#> 6981 18.792 174.8  56.0
#> 7001 18.850    NA  62.6
#> 7032 18.959    NA  76.2
#> 7066 19.060 180.8  93.8
#> 7068 19.063 175.0    NA
#> 7073 19.077 182.7    NA
#> 7101 19.148 186.5    NA
#> 7141 19.310 177.1    NA
#> 7152     NA 178.0  78.1
#> 7161 19.408 192.7    NA
#> 7173     NA 191.0  87.1
#> 7200 19.575 195.0  88.9
#> 7221 19.633    NA  75.0
#> 7240     NA 172.5  70.6
#> 7247     NA 177.0  65.5
#> 7293 19.926    NA 117.4
#> 7297 19.934 181.8    NA
#> 7319 20.010 170.0    NA
#> 7328 20.030 178.6  71.0
#> 7362     NA 188.7  89.4
#> 7396 20.281    NA  81.1

# Change default matrices as desired
my_patterns <- mads_boys$patterns
my_patterns[1:3, 2] <- 0

my_weights <- mads_boys$weights
my_weights[2, 1] <- 2
my_weights[3, 1] <- 0.5

# Rerun amputation
my_mads_boys <- ampute(
  data = compl_boys, patterns = my_patterns, freq =
    c(0.3, 0.3, 0.4), weights = my_weights, type = c("RIGHT", "TAIL", "LEFT")
)
my_mads_boys$amp
#>         age   hgt   wgt
#> 3279  8.859    NA  31.0
#> 3283  8.867 145.0  38.2
#> 3296  8.908    NA    NA
#> 3321  8.999    NA    NA
#> 3323     NA    NA  48.2
#> 3327  9.021 141.4  29.4
#> 3357  9.119    NA    NA
#> 3388  9.201    NA    NA
#> 3398  9.234    NA    NA
#> 3409  9.270 140.4  32.0
#> 3416  9.303 142.2  31.6
#> 3422  9.316 147.4  31.4
#> 3429  9.368 132.7  25.9
#> 3442  9.407    NA  27.0
#> 3449  9.426 146.0  36.5
#> 3455  9.451 136.0  27.5
#> 3460  9.459    NA  30.8
#> 3481     NA    NA  30.3
#> 3484  9.514    NA  27.8
#> 3486  9.514    NA    NA
#> 3494  9.524 140.9  32.7
#> 3525  9.582 134.0  27.5
#> 3533  9.604    NA    NA
#> 3547  9.631    NA  28.7
#> 3609  9.834    NA    NA
#> 3651     NA    NA  37.3
#> 3664 10.020    NA    NA
#> 3710 10.132 134.0  26.5
#> 3721 10.154 139.3  30.6
#> 3724 10.160    NA    NA
#> 3727 10.171    NA    NA
#> 3805 10.398 149.8  34.7
#> 3814 10.422 158.8  39.8
#> 3827 10.447 148.7  41.0
#> 3834 10.477    NA    NA
#> 3841 10.499    NA    NA
#> 3865 10.554 146.3  40.4
#> 3873 10.568    NA    NA
#> 3880 10.581    NA    NA
#> 3929 10.724 144.1  29.5
#> 3975 10.888 147.0  33.8
#> 3988 10.945 149.0  45.6
#> 3991 10.954 145.1  36.2
#> 3994 10.967    NA    NA
#> 3995 10.970 151.2  39.2
#> 4006 11.003 134.3  29.1
#> 4009 11.011 148.8  44.2
#> 4059 11.126 139.6  32.7
#> 4066 11.143    NA  25.0
#> 4067 11.143 148.3  41.5
#> 4070 11.156    NA  44.5
#> 4072 11.159    NA  49.7
#> 4102 11.222 151.8  44.4
#> 4122 11.288 159.4  43.4
#> 4173 11.446 147.9  42.2
#> 4174 11.446 152.8  43.1
#> 4186 11.482 148.7  37.2
#> 4211 11.545 153.2  42.5
#> 4238 11.605 155.2  36.7
#> 4240     NA    NA  33.8
#> 4253 11.655 160.6  44.4
#> 4255 11.665    NA    NA
#> 4266 11.690 148.0  35.2
#> 4293 11.759 153.2  42.8
#> 4301 11.789    NA    NA
#> 4302 11.791 152.8  43.5
#> 4312 11.811    NA    NA
#> 4318 11.827    NA    NA
#> 4332 11.874    NA    NA
#> 4349     NA    NA  44.5
#> 4399 12.071    NA    NA
#> 4465     NA    NA  43.3
#> 4481 12.292 145.8  39.2
#> 4487 12.303 161.4  44.0
#> 4505 12.375    NA  61.0
#> 4532 12.457    NA    NA
#> 4552 12.501 170.5  53.4
#> 4561 12.520    NA  44.1
#> 4579 12.574    NA  51.6
#> 4585 12.583    NA    NA
#> 4591 12.599    NA    NA
#> 4646 12.741    NA    NA
#> 4682 12.821    NA  56.0
#> 4721 12.933 169.5  54.8
#> 4727 12.944    NA    NA
#> 4745 12.991    NA    NA
#> 4748 12.993 155.9  42.3
#> 4752 12.996    NA    NA
#> 4809 13.108 164.0  61.7
#> 4823     NA    NA  65.1
#> 4824 13.127 180.0  57.8
#> 4825 13.130 156.4  40.8
#> 4847 13.188 168.1  53.4
#> 4848     NA    NA  42.1
#> 4887 13.275 161.2  37.0
#> 4892 13.300 165.5  41.9
#> 4961 13.489    NA    NA
#> 4994 13.552    NA    NA
#> 5039     NA    NA  54.9
#> 5044 13.642    NA    NA
#> 5048 13.656 175.4  74.8
#> 5064 13.686 168.7  46.1
#> 5085 13.749    NA    NA
#> 5113 13.839 162.1  44.9
#> 5126 13.883 176.2  48.1
#> 5130 13.891 174.6  54.2
#> 5133 13.897 181.7  61.9
#> 5147     NA    NA  35.1
#> 5159 13.938    NA    NA
#> 5206 14.045 170.0  54.7
#> 5219 14.069 170.6  59.2
#> 5228     NA    NA  50.9
#> 5247 14.121 159.2  42.7
#> 5288 14.209 170.9  54.8
#> 5293 14.220 165.5  48.0
#> 5327     NA    NA  44.3
#> 5335 14.308 171.0  56.0
#> 5343 14.332 164.1  49.1
#> 5367 14.412 165.5  54.2
#> 5410 14.527 160.7  52.0
#> 5415     NA    NA  76.0
#> 5416 14.543 173.7  69.5
#> 5417 14.543 176.4  51.0
#> 5420 14.546    NA    NA
#> 5478 14.669 182.9  57.2
#> 5496     NA    NA  50.7
#> 5509 14.762 168.6  47.6
#> 5520 14.811 173.8  61.7
#> 5522 14.811    NA  66.5
#> 5539 14.844 172.7  64.3
#> 5551     NA    NA  66.3
#> 5567 14.926 177.4  58.3
#> 5585 14.967 174.1  88.0
#> 5598 14.997 181.2  65.1
#> 5602 15.003 188.0  91.6
#> 5610 15.025 185.5  62.7
#> 5612 15.028    NA  54.1
#> 5642 15.099 178.1  74.5
#> 5654 15.129 176.9  58.6
#> 5675 15.162    NA    NA
#> 5710 15.249 188.0  89.0
#> 5714 15.266    NA  62.5
#> 5763 15.411 173.3  54.1
#> 5764 15.416 187.2  80.6
#> 5789 15.474    NA  80.2
#> 5806     NA    NA  52.3
#> 5823 15.542    NA    NA
#> 5830 15.556 183.3  61.5
#> 5856 15.622    NA  70.5
#> 5857 15.630 174.3  52.6
#> 5858     NA    NA  67.0
#> 5879     NA    NA  58.6
#> 5880 15.668 176.0  63.8
#> 5883 15.674 176.6  56.9
#> 5947 15.838 177.0  63.5
#> 5964     NA    NA  56.0
#> 5971 15.906 176.2  57.5
#> 5975 15.912 180.0  65.2
#> 5986     NA    NA  62.2
#> 6005 15.989 187.8  64.8
#> 6029 16.049 186.7  70.6
#> 6033 16.062 183.9  61.5
#> 6036 16.065 184.4  68.5
#> 6037 16.068 186.5  70.7
#> 6064     NA    NA 113.0
#> 6083 16.235 185.4  60.0
#> 6085     NA    NA  76.0
#> 6092 16.273 177.9  57.0
#> 6117 16.355 171.0  59.1
#> 6132 16.402 173.6  54.5
#> 6138     NA    NA  69.0
#> 6141 16.435 175.1  64.5
#> 6166 16.492    NA  62.5
#> 6185 16.544 178.0  65.7
#> 6251     NA    NA  66.4
#> 6253 16.720 192.8  88.3
#> 6262     NA    NA  70.3
#> 6283 16.807    NA    NA
#> 6343     NA    NA  63.7
#> 6361 16.999 179.0  68.1
#> 6372     NA    NA  65.5
#> 6416     NA    NA  69.3
#> 6482 17.333 180.3  76.8
#> 6483 17.336 183.9  66.3
#> 6528 17.440 171.4  71.7
#> 6539 17.467    NA  55.5
#> 6567     NA    NA  71.2
#> 6611 17.678 176.4  72.8
#> 6641     NA    NA  94.9
#> 6647 17.757 196.2  81.0
#> 6686 17.911    NA    NA
#> 6700 17.957    NA  64.5
#> 6756 18.121 171.6  58.4
#> 6782 18.209 177.9  63.4
#> 6789 18.220 187.4  79.0
#> 6831     NA    NA  69.2
#> 6858     NA    NA  53.5
#> 6892 18.551 193.0  71.7
#> 6923     NA    NA  61.9
#> 6963 18.737 191.0  81.3
#> 6964 18.743 192.0  99.0
#> 6977 18.773 189.4  69.6
#> 6981 18.792 174.8  56.0
#> 7001 18.850    NA  62.6
#> 7032 18.959 185.1  76.2
#> 7066     NA    NA  93.8
#> 7068 19.063    NA  72.4
#> 7073 19.077 182.7  70.0
#> 7101 19.148 186.5  71.9
#> 7141 19.310    NA  60.1
#> 7152     NA    NA  78.1
#> 7161 19.408 192.7 100.1
#> 7173 19.471    NA  87.1
#> 7200     NA    NA  88.9
#> 7221     NA    NA  75.0
#> 7240     NA    NA  70.6
#> 7247 19.739 177.0  65.5
#> 7293 19.926 192.3 117.4
#> 7297 19.934    NA    NA
#> 7319 20.010 170.0  68.8
#> 7328 20.030    NA  71.0
#> 7362 20.117    NA  89.4
#> 7396     NA    NA  81.1