|
|
| Line 44: |
Line 44: |
| **impairment of reproductive functions | | **impairment of reproductive functions |
| **increased cancer risk | | **increased cancer risk |
| | |
| | === Summary === |
|
| |
|
| ;Human health effects caused by dioxins | | ;Human health effects caused by dioxins |
| Dioxins are persistent environmental pollutants and they accumulate in the food chain. Dioxins cause a large variety of effects in laboratory animals. They are carcinogenic at large doses, and they also cause developmental defects. The evidence of human effects has been more limited, because the exposure levels have been much lower than in animal tests. However, an increased cancer risk has been observed after high industrial occupational exposures. In addition, mild tooth mineralisation defects have been observed in children in Finland, even after typical exposures of the 1980's. Children are exposed to dioxins mostly via mother's milk. The dioxin levels have been decreasing since then, and no tooth defects have been observed at the current exposure levels. | | Dioxins are persistent environmental pollutants and they accumulate in the food chain. Dioxins cause a large variety of effects in laboratory animals. They are carcinogenic at large doses, and they also cause developmental defects. The evidence of human effects has been more limited, because the exposure levels have been much lower than in animal tests. However, an increased cancer risk has been observed after high industrial occupational exposures. In addition, mild tooth mineralisation defects have been observed in children in Finland, even after typical exposures of the 1980's. Children are exposed to dioxins mostly via mother's milk. The dioxin levels have been decreasing since then, and no tooth defects have been observed at the current exposure levels. |
|
| |
|
| <t2b index="Exposure agent,Trait,Response metric,Exposure route,Exposure metric,Exposure unit,ERF parameter,Observation" locations="Threshold,ERF" desc="Description" unit="-"> | | <t2b index="Exposure agent,Trait,Response metric,Exposure route,Exposure metric,Exposure unit,ERF parameter,Scaling,Observation" locations="Threshold,ERF" desc="Description" unit="-"> |
| TEQ|Cancer, total|Lifetime probability|Ingestion|Intake|pg /kg /d|CSF bw|0|0.000156|US EPA. From ERF of dioxin: 156000 (mg/kg/d)^-1 | | TEQ|Cancer, total|Lifetime probability|Ingestion|Intake|pg /kg /d|CSF|BW|0|0.000156|US EPA. From ERF of dioxin: 156000 (mg/kg/d)^-1 |
| logTEQ|Developmental dental defects incl. agenesis|Yes/No according to "Developmental Defects of Enamel Index" |Ingestion etc. (as it was in Seveso)|log(TCDD serum concentration+1)|ng/kg in fat|ERS|0|0.26 +- 0.12|Alaluusua et al. 2004; PL Gradowska PhD thesis 2013. From ERF of TCDD. Resulting distribution based on one simulation. Weibull(alfa=0.2925,beta=2.192) | | logTEQ|Developmental dental defects incl. agenesis|Yes/No according to "Developmental Defects of Enamel Index" |Ingestion etc. (as it was in Seveso)|log(TCDD serum concentration+1)|ng/kg in fat|ERS|None|0|0.26 +- 0.12|Alaluusua et al. 2004; PL Gradowska PhD thesis 2013. From ERF of TCDD. Resulting distribution based on one simulation. Weibull(alfa=0.2925,beta=2.192) |
| logTEQ|Tooth defect|Yes/No according to "Developmental Defects of Enamel Index" |Placenta and mother's milk|log(TCDD serum concentration+1)|log(pg/g in fat)|ERS|0|0:0.06:0.12|Alaluusua et al. 2004 data with PL Gradowska PhD thesis 2013 approach but we used the response function y = k x + b (see below) | | logTEQ|Tooth defect|Yes/No according to "Developmental Defects of Enamel Index" |Placenta and mother's milk|log(TCDD serum concentration+1)|log(pg/g in fat)|ERS|None|0|0:0.06:0.12|Alaluusua et al. 2004 data with PL Gradowska PhD thesis 2013 approach but we used the response function y = k x + b (see below) |
| TEQ|Dental defect|Yes/No according to "Developmental Defects of Enamel Index" |Placenta and mother's milk|Ingestion|pg/d|ERS|0|0.001382|Alaluusua et al. 1996 | | TEQ|Dental defect|Yes/No according to "Developmental Defects of Enamel Index" |Placenta and mother's milk|Ingestion|pg/d|ERS|None|0|0.001382|Alaluusua et al. 1996 |
| TEQ|Cancer|Morbidity|Ingestion|Intake|pg /kg /d|UR bw|0|0.000032; 0.000035; 0.00016|From ERFs of environmental pollutants | | TEQ|Cancer|Morbidity|Ingestion|Intake|pg /kg /d|UR|BW|0|0.000032; 0.000035; 0.00016|From ERFs of environmental pollutants |
| TEQ|Dioxin recommendation|Tolerable daily intake|Ingestion|Intake|pg /kg /d|TDI bw|0|1|Lower limit of 1-4 pg/kg/d | | TEQ|Dioxin recommendation|Tolerable daily intake|Ingestion|Intake|pg /kg /d|T |
| </t2b>
| |
| | |
| ERF of dioxin on cancer is indexed by age. It applies to adult age categories, > 18 years (gender combined).
| |
| | |
| === Cancer ===
| |
| | |
| The U.S. Environmental Protection Agency (US EPA) calculated an oral cancer slope factor (CSF) for 2,3,7,8 - TCDD (the most toxic dioxin compound). This CFS equals to 156000 (mg/kg bw-d)<sup>-1</sup> and represents the upper 95<sup>th</sup> percentile estimate of the probability of developing cancer per unit intake of chemical over a lifetime.
| |
| <ref>US EPA 2000. Guidance for assessing chemical contaminant data for use in fish advisories. Volume 1: Fish sampling and analysis, 3rd edition. http://www.epa.gov/waterscience/fish/advice/volume1/</ref>
| |
| <ref name="epadioxincsf"> U.S.EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisory. Volume 2: Risk Assessment and Fish Consumption Limits, 3rd Edition. 2000. Table 3-1. [http://www.epa.gov/waterscience/fish/guidance.html Open access Internet file] [http://ytoswww/yhteiset/Huippuyksikko/Kirjallisuus/Fish_and_health/EPAFishAdvisory/ Intranet file]</ref>
| |
| | |
| Evidence concerning cancer risk is mainly from animal studies, and dioxins are probably quite weak carcinogens in humans. Hormesis type of dose-response is suspected. Evidence concerning other health effects is inconsistent.
| |
| | |
| In this specific case ([[:op_fi:Hämeenkyrön jätteenpolttolaitos]]):
| |
| * MSWI is likely to increase background dioxin exposure (additional low exposure)
| |
| *the risk of accidental exposure is low (dioxin emissions will increase only if burning process is working improperly)
| |
| *health effects of long-term exposure are relevant
| |
| *effects on development and endocrine functions are more relevant than the risk of cancer
| |
| * The health effects of low doses should be modelled from animal and human data. Eg. Alaluusua et al. (1996) have studied tooth development. In a study by Miettinen et al. (2005)<ref name="miettinen2005"/>, exposure to 0.5 μg TCDD/kg body weight on GD 15 resulted in maternal adipose tissue concentration of 2185 pg/g fat. In that study, linear extrapolation of the data predicts a maternal adipose tissue concentration of 100-120 pg/g fat after exposure to 0.03 μg TCDD/kg body weight. This estimated maternal adipose tissue concentration is sufficient to induce developmental dental defects in rat offspring, and is similar to the highest values measured in the Finnish average population (PCDD/F 145.5 pg WHO-TEQ/g fat (Kiviranta et al. 2005).
| |
| | |
| Sensitive subgroups: foetuses, newborns, young females (women below or at childbearing age), individuals with high fish consumption (e.g. fishermen), individuals working in incineration plants etc.
| |
| | |
| Tolerable daily intake (TDI): 1-4 pg/kg body weight
| |
| | |
| U.S.EPA estimates the dioxin and other cancer potencies by using cancer slope factors (CSFs). CSF for dioxins is 156000 kg*d/mg.<ref name="epadioxincsf"/> This estimate has often been considered as overestimate, as the aim is to produce conservative assessments (where false positive is better than a false negative). It has also been suggested that dioxin cancer response is secondary to toxicity and has a threshold, below which cancer risk does not exist. World health organisation has approached dioxin risks from another endpoint, namely developmental defects. WHO has assessed that there is a threshold below which developmental defect risk is negligible. A tolerable daily intake is 1-4 pg/kg/d, and the average Finnish average is close to this or slightly below. In this draft assessment we will use both approaches. The U.S.EPA approach gives a higher value for risks and thus produces the plausible upper end of the confidence interval.
| |
| | |
| Effective dose resulting in a 0.01 increase in lifetime risk of cancer mortality (ED<sub>01</sub>): 45 ng/kg body weight (95% CI 21-324 ng/kg body weight).
| |
| | |
| = 0.01/(45 pg/(kg body weight * 200 g body fat/kg body weight))
| |
| | |
| = 0.044 /(pg/g body fat)
| |
| | |
| === Dental defects ===
| |
| | |
| :''Some of the content was previously in [http://heande.opasnet.org/heande/index.php?title=ERF_of_dioxin_on_dental_aberrations&oldid=9151 Heande].
| |
| '''Exposure-response functions for tooth defects caused by TCDD (study-specific)''' describes study-specific exposure-response functions for either enamel defects in molars or missing or smaller molars.
| |
| | |
| What is the quantitative relationship between exposure to TCDD during infancy and childhood and the risk (probability) of developmental dental defects described as defects of tooth enamel? Exposure is expressed in terms of concentration of TCDD in serum lipid.
| |
| | |
| ==== Seveso children ====
| |
| | |
| Serum dioxin concentration vs. dental defects
| |
| (Alaluusua et al. 2004, Seveso children study)<ref name="Alaluusua">Alaluusua, S., Calderara, P., Gerthoux, P.M., Lukinmaa, P-L., Kovero, O., Needham, L., Patterson, D.G., Tuomisto, J., and Mocarelli, P. (2004) Developmental dental aberrations after the dioxin accident in Seveso. Environ Health Perspect. 112, 1313-8.</ref>
| |
| <ref name="patrycjathesis">PL Gradowska PhD thesis 2013</ref>
| |
| *ERS = 0.26 (± 0.12)
| |
| | |
| Transformation between serum concentration and intake:
| |
| | |
| :<math>C_s = \frac{I * t_{1/2} * f}{a * ln2 * BF%},</math>
| |
| | |
| where
| |
| C<sub>s</sub> = serum concentration of dioxin in pg/g fat
| |
| I = average daily intake of dioxin in pg/kg/day
| |
| t<sub>1/2</sub> = the half-life of dioxin (2737.5 d = 7.5 a)
| |
| f = fraction of ingested dioxin actually absorbing from the gut (0.80)
| |
| BF% = body fat percentage
| |
| a percentage of total daily dietary intake of dioxins that come from fish (0.86).
| |
| | |
| The previous equation applies in a single individual. In the case of dental aberrations, the main exposure comes from the mother during pregnancy and breast feeding. For this, we use
| |
| | |
| :<math>C_{s,i} = \frac{I_{a,m} * t_{1/2,m} * f_m * FE}{ln2 * BF_i},</math>
| |
| | |
| where
| |
| C<sub>s,i</sub> = serum concentration of dioxin in the infant in pg/g fat
| |
| I<sub>a,m</sub> = average daily intake of dioxin of the mother in absolute amounts pg/day
| |
| t<sub>1/2,m</sub> = the half-life of dioxin in the mother (2737.5 d = 7.5 a)
| |
| f<sub>m</sub> = fraction of ingested dioxin actually absorbing from the gut in the mother (0.80)
| |
| FE = fraction of mother's dioxin load that is transported to the infant during breast feeding (0.25) Vartiainen et al. REF
| |
| BF = body fat amount in the infant (into which the dioxin is evenly distributed) during the period when tooth are sensitive
| |
| to defects and the exposure at its highest (ca. six months of age) (1 kg)
| |
| | |
| '''ERF of dioxin on dental aberrations''' is a continuous random variable indexed by age. It applies to the first two age groups of the Beneris population (0-2 and 2-18 years, gender combined). It has been agreed that this ERF can be applied to WHO-TEQ concentration of dioxin (PCDD/F) and dioxin-like PCB in body fat.
| |
| | |
| <ref>Kattainen, H., Tuukkanen, J., Simanainen, U., Tuomisto, J.T., Kovero, O., Lukinmaa, P-L., Alaluusua, S., Tuomisto, J., and Viluksela, M. (2001) In utero/lactational 2,3,7,8-tetrachlorodibenzo-p-dioxin exposure impairs molar tooth development in rats. Toxicol Appl Pharmacol. 174, 216-24.</ref>
| |
| <ref>Alaluusua et al. Eur J Oral Sci. 1996 Oct-Dec;104(5-6):493-7. </ref>
| |
| <ref name="miettinen2005">Miettinen HM et al. Toxicol Sci. 2005 Jun;85(2):1003-12.</ref>
| |
| | |
| Probability distribution of ERF of dioxin on dental aberrations was created based data on dioxin accident in Seveso in 1976 extracted from study by Alaluusua et al. <ref name="Alaluusua" /> This data is summarized in a table below.
| |
| | |
| '''Table:''' Developmental defects of enamel in individuals who were children (< 5 years of age) at the time of the Seveso accident by exposure group.
| |
| | |
| {| {{prettytable}}
| |
| |-
| |
| | '''Exposure group'''
| |
| | '''Number of exposed individuals'''
| |
| | '''Number of enamel defect cases after 25 years since Seveso accident'''
| |
| | '''Serum TCDD concentration range (pq/g lipid)'''
| |
| | '''Mean serum TCDD concentration (pq/g lipid)'''
| |
| | '''Risk (%)'''
| |
| |-
| |
| | '''non-ABR zone'''
| |
| | 39
| |
| | 10
| |
| |
| |
| | 40.5
| |
| | 26
| |
| |-
| |
| | '''Exposed group 1'''
| |
| | 10
| |
| | 1
| |
| | 31-226
| |
| | 128.5
| |
| | 10
| |
| |-
| |
| | '''Exposed group 2'''
| |
| | 11
| |
| | 5
| |
| | 238-592
| |
| | 415
| |
| | 45
| |
| |-
| |
| | '''Exposed group 3'''
| |
| | 15
| |
| | 9
| |
| | 700-26000
| |
| | 3000
| |
| | 60
| |
| |}
| |
| | |
| It has been assumed that the TCDD exposure in children from the non-ABR zone follows lognormal distribution with mean 40.5 (ng/kg in fat) and geometric standard deviation 4 while the exposure in the remaining groups is log-uniformly distributed over the range of serum levels reported in the table. The log-logistic model (with constant term) was chosen to model the relation between the log-transformed and scaled serum TCDD level and the probability of developmental defects of enamel. The independent variable used was ln(serum TCDD level+1). Probability distribution of the coefficient for the independent variable was constructed using the following approach. Let ni denote number of children in exposure group i and ri be the number of observed enamel defect cases in group i, i= 1,...,4.
| |
| | |
| #Sample ni exposures from distribution of TCDD serum level in group i. Denote these exposures as xj, j=1,...,75.
| |
| #Assign ri responses randomly to ni people.
| |
| #Fit the dose-response model to simulated data, call it model p0.
| |
| #Compute predicted probability for every person in the study, i.e. compute p0(xj).
| |
| #Re-sample the response of each person assuming that the probability that person j responds is p0(xj), j=1,...,75.
| |
| #Re-fit the dose-response model.
| |
| #Iterate steps 5-6 100 times.
| |
| #Repeat steps 1 - 7 1500 times.
| |
| #Create the density histogram of simulated estimates of regression coefficient (only positive values are kept).
| |
| #Fit parametric probability density function to the histogram.
| |
| | |
| The units used:
| |
| *(ng/kg in fat)<sup>-1</sup>
| |
| *(ln(ng/kg fat))<sup>-1</sup>
| |
| | |
| The approach described above was used to produce two different functions. First, Gradowska 2013 used logistic regression with exposure transformation log(concentration + 1). See the trait ''Developmental dental defects incl. agenesis'' in the data table. Second, a linear function P(y) = intercept + beta * x was used; P(y) is the probability of tooth defect, x is exposure (with the same transformation) and beta is the slope coefficient. See the trait ''Tooth defect''. In this case it was assumed that there is a non-dioxin-related background that does not affect the magnitude of dioxin effect.
| |
| | |
| {{hidden|
| |
| <pre>
| |
| cases <- c(10, 1, 5, 9) # Number of children with tooth defects in different populations
| |
| children <- c(39, 10, 11, 15) # Total number of children
| |
| out <- data.frame()
| |
| | |
| for(k in 1:1500) {
| |
| r <- c(
| |
| rbinom(children[1], 1, cases[1] / children[1]),
| |
| rbinom(children[2], 1, cases[2] / children[2]),
| |
| rbinom(children[3], 1, cases[3] / children[3]),
| |
| rbinom(children[4], 1, cases[4] / children[4])
| |
| )
| |
| | |
| x <- c( # serum dioxin concentrations distributions in different groups
| |
| exp(rnorm(children[1], log(40.5) - 0.5 * log(4)^2, log(4))),
| |
| exp(runif(children[2], log(31), log(226))),
| |
| exp(runif(children[3], log(238), log(592))),
| |
| exp(runif(children[4], log(700), log(26000)))
| |
| )
| |
| | |
| # for(L in 1:100) { # Does not converge nicely so this is skipped
| |
| fit <- lm(r ~ log(x + 1))
| |
| # p <- pmax(0, fit$coefficients[1] + fit$coefficients[2] * log(x + 1))
| |
| # r <- rbinom(length(r), 1, p)
| |
| # }
| |
| | |
| out <- rbind(out, data.frame(Intercept = fit$coefficients[1], Slope = fit$coefficients[2]))
| |
| }
| |
| | |
| rownames(out) <- 1:nrow(out)
| |
| out <- out[out$Slope >= 0 , ]
| |
| | |
| xi <- exp(runif(nrow(out), 0, log(1000)))
| |
| plott <- data.frame(x = xi, y = out$Slope * log(xi + 1))
| |
| ggplot(plott, aes(x = x, y = y))+geom_point()+scale_x_log10()+geom_smooth()
| |
| ggplot(plott, aes(x = x, y = y))+geom_point()+geom_smooth()
| |
| hist(out$Slope)
| |
| # A fairly good fit for slope is Triangular(0, 0.06, 0.12) using log(TEQ pg/g in serum fat + 1) as the exposure parameter and yes/no dental defects as response)
| |
| </pre>
| |
| }}
| |
| | |
| * A previous attempt to model dental defects is [http://en.opasnet.org/en-opwiki/index.php?title=ERF_of_dioxin&oldid=34117#Dental_defects here]. Note that the hidden box only shows well in the edit mode.
| |
| | |
| ==== Finnish children ====
| |
| | |
| Alaluusua and coworkers studied children from Finnish general population born in 1987.
| |
| <ref name="alaluusua1996">Alaluusua S, Lukinmaa PL, Vartiainen T, Partanen M, Torppa J, Tuomisto J. Polychlorinated dibenzo-p-dioxins and dibenzofurans via mother's milk may cause developmental defects in the child's teeth. Environ Toxicol Pharmacol. 1996 May 15;1(3):193-7. [http://www.ncbi.nlm.nih.gov/pubmed/21781681]</ref>
| |
| <ref name="alaluusua1999">Alaluusua S, Lukinmaa PL, Torppa J, Tuomisto J, Vartiainen T. Developing teeth as biomarker of dioxin exposure. Lancet. 1999 Jan 16;353(9148):206. [http://www.ncbi.nlm.nih.gov/pubmed/9923879]</ref>
| |
| They estimated dioxin exposure by using area under curve:
| |
| | |
| :<math>AUC = \frac{C (1 - e^{-k_e t})}{k_e},</math>
| |
| | |
| where
| |
| AUC = area under the curve (pg a /g)
| |
| C = concentration in mother's milk (pg /g)
| |
| k<sub>e</sub> = mother's elimination rate for dioxin during lactation (0.2877 /a)
| |
| t = time of nursing (a)
| |
| | |
| {| {{prettytable}}
| |
| |+'''Mineralization defects of the permanent first molars.
| |
| !Outcome
| |
| !colspan="3"| Number of children with exposure (pg*year/g milk fat)
| |
| |----
| |
| ! ||Low exposure (<8.0)||Moderate exposure (8.0-16)||High exposure (>16)
| |
| |----
| |
| | Normal || 22 ||41 || 22
| |
| |----
| |
| | Mild defect in only one tooth || 1 || 5 || 2
| |
| |----
| |
| |Moderate defect or mild defect in more than one tooth || 0 || 3 || 4
| |
| |----
| |
| | Severe defect || 0 || 0 || 2
| |
| |----
| |
| | All || 23 || 49 || 30
| |
| |}
| |
| | |
| We need to convert the AUC to mother's dioxin daily intake.
| |
| | |
| :<math>C_{s,m} = \frac{I_{a,m} * t_{1/2,m} * f_m}{ln2 * BF_i},</math>
| |
| | |
| where
| |
| C<sub>s,m</sub> = dioxin concentration in serum (or fat or milk) in the mother (pg/g fat)
| |
| I<sub>a,m</sub> = average daily intake of dioxin of the mother in absolute amounts pg/day
| |
| t<sub>1/2,m</sub> = the half-life of dioxin in the mother when not nursing (2737.5 d = 7.5 a)
| |
| f<sub>m</sub> = fraction of ingested dioxin actually absorbing from the gut in the mother (0.80)
| |
| BF<sub>m</sub> = body fat amount in the mother (into which the dioxin is evenly distributed)
| |
| | |
| When C<sub>s,m</sub> from this equation is put into the previous equation, we can solve I<sub>a,m</sub>:
| |
| | |
| :<math>I_{a,m} = \frac{ln2 * BF * AUC * k_e}{t_{1/2,m} * f_m (1 - e^{-k_e t)}},</math>
| |
| | |
| where we assume an average value of 0.5 a for t because we don't have data about the actual length of nursing.
| |
| | |
| Using this equation the estimated AUCs for Finnish children (4, 12, and 20 pg*a/g for groups <8.0, 8.0-16, and >16) result in long-term intakes of 38, 114, and 190 pg/d, respectively. Therefore, we can use these values in a regression analysis to find a dose-response between mother's long-term daily intake of dioxin and probability of tooth defect. The linear slope from the highest and lowest group is (0.25 - 0.04)/(190-38) = 0.001382 (pg/d)<sup>-1</sup>.
| |
| | |
| === PCB and cancer ===
| |
| | |
| {| {{prettytable}}
| |
| |-
| |
| |
| |
| | '''Upper bound slope factor'''
| |
| | '''Central-estimate slope factor'''
| |
| |-
| |
| | '''High risk and persistence'''
| |
| | 2.0
| |
| | 1.0
| |
| |-
| |
| | '''Low risk and persistence'''
| |
| | 0.4
| |
| | 0.3
| |
| |-
| |
| | '''Lowest risk and persistence'''
| |
| | 0.07
| |
| | 0.04
| |
| |}
| |
| | |
| In Beneris slope factor of 2 is used.
| |
| | |
| ERF of PCB on cancer indexed by variable age. It applies to adults, i.e. > 18 years old (gender combined).
| |
| | |
| The U.S. Environmental Protection Agency (US EPA) recommends using cancer slope factors (CSFs) when evaluating potential cancer risks of PCB mixtures.<ref>IRIS. US EPA. http://www.epa.gov/iris/subst/0294.htm</ref> There are three tiers of CSFs for environmental PCBs that depend on the exposure pathway. These are: high risk and persistence, low risk and persistence, lowest risk and persistence. In each of these tiers EPA reports central and upper bound estimate of CSF. In general, central estimate slope factors are used to estimate a typical individual’s risk while upper-bound slope assure that this risk is not likely to be underestimated if the underlying model is correct.
| |
| | |
| According to the US EPA exposures via food chain are associated with the highest risk and persistence. Therefore CSFs from the first tier are recommended to be used when estimating cancer risks from food chain pathways.
| |
| | |
| (mg/kg bw/d)<sup>-1</sup>
| |
| | |
| === Calculations ===
| |
| | |
| <rcode name="initiate" label="Initiate ovariables" embed=1 store=1>
| |
| library(OpasnetUtils)
| |
| | |
| d <- opbase.data("Op_en5823")
| |
| #d[["Exposure agent"]] <- "TEQ"
| |
| d$Obs <- NULL
| |
| colnames(d) <- gsub(" ", "_", colnames(d))
| |
| d$Result <- ifelse(d$Result == "", "0", as.character(d$Result))
| |
| | |
| ERF <- Ovariable("ERF", data = d[d$Observation == "ERF", colnames(d) != "Observation"])
| |
| | |
| threshold <- Ovariable("Threshold", data = d[d$Observation == "Threshold", colnames(d) != "Observation"])
| |
| | |
| #test1 <- EvalOutput(ERF)
| |
| #oprint(test1)
| |
| #test2 <- EvalOutput(threshold)
| |
| #oprint(test2)
| |
| #cat(colnames(test1@output)[test1@marginal])
| |
| #cat("\n")
| |
| #cat(colnames(test2@output)[test2@marginal])
| |
| #cat("\n")
| |
| objects.store(ERF, threshold)
| |
| cat("Ovariables ERF, threshold stored.\n")
| |
| </rcode>
| |
|
| |
|
| == See also == | | == See also == |