WGCNA - error in modulePreservation(): duplicated row.names not allowed - r

I'm having some problems with the modulePreservation function of WGCNA (https://www.rdocumentation.org/packages/WGCNA/versions/1.63/topics/modulePreservation). When I use it with my multiData dataframes (gene expression from case and control groups), I'm having a error of duplicated row.names. In the past, I was able to perform this step with no problems at all.
(Ps. multiExpr = multiData ; control_colors = multiColor)
> modulePreservation(multiExpr, control_colors, dataIsExpr=T, referenceNetworks=1, nPermutations=100, randomSeed=1, quickCor=0, verbose=3, networkType="unsigned")
.checking data for excessive amounts of missing data..
Flagging genes and samples with too many missing values...
..step 1
Flagging genes and samples with too many missing values...
..step 1
..unassigned 'module' name: grey
..all network sample 'module' name: gold
..calculating observed preservation values
Error in `.rowNamesDF<-`(x, value = value) :
duplicate 'row.names' are not allowed
In addition: Warning message:
non-unique values when setting 'row.names': ‘A1CF’, ‘A2M’, ‘AADAC’, ‘AARS’, ‘AASDHPPT’, ‘ABCA12’, ‘ABCA3’, ‘ABCA4’, ‘ABCA8’, ‘ABCB11’, ‘ABCB4’, ‘ABCB9’, ‘ABCC1’, ‘ABCC3’, ‘ABCC6’, ‘ABCD2’, ‘ABCD4’, ‘ABCE1’, ‘ABCF3’, ‘ABCG1’, ‘ABCG2’, ‘ABHD10’, ‘ABHD2’, ‘ABHD4’, ‘ABHD6’, ‘ABI2’, ‘ABL1’, ‘ABL2’, ‘ACAA2’, ‘ACACA’, ‘ACACB’, ‘ACAD10’, ‘ACAD8’, ‘ACADL’, ‘ACADSB’, ‘ACADVL’, ‘ACAN’, ‘ACAP1’, ‘ACAT1’, ‘ACBD3’, ‘ACBD4’, ‘ACCN3’, ‘ACD’, ‘ACE2’, ‘ACHE’, ‘ACIN1’, ‘ACO2’, ‘ACOT7’, ‘ACOT8’, ‘ACOT9’, ‘ACOX3’, ‘ACOXL’, ‘ACPP’, ‘ACR’, ‘ACRV1’, ‘ACSF2’, ‘ACSL3’, ‘ACSL5’, ‘ACSL6’, ‘ACSM3’, ‘ACTB’, ‘ACTC1’, ‘ACTL6B’, ‘ACTL7A’, ‘ACTL8’, ‘ACTN2’, ‘ACTN3’, ‘ACTR1A’, ‘ACTR2’, ‘ACTR3B’, ‘ACTR5’, ‘ACTR8’, ‘ACVR1’, ‘ACVRL1�� [... truncated]
I already checked the row.names and colnames of my multiExpr files, and all are unique values:
sum(duplicated(row.names(multiExpr$Control$data)))
sum(duplicated(row.names(multiExpr$Case$data)))
sum(duplicated(colnames(multiExpr$Control$data)))
sum(duplicated(colnames(multiExpr$Case$data)))
My R version is R version 3.5.1 (2018-07-02)

It seems to be a bug in signedKME()
I've proposed a fix at https://www.biostars.org/p/339950/

Related

Is there a way to use is.finite in sparklyr?

I need to mutate a column so that non finite values are turned into zeroes.
I am using this code to do it:
df %>% mutate(column = replace(column, !is.finite(column), 0)
This usually works in base R, and I've seen that the replace function also does in Sparklyr. However, is.finite isn't implemented in Sparklyr and I get the following error:
Error: org.apache.spark.sql.AnalysisException: Undefined function: is.finite; line 14 pos 45
at org.apache.spark.sql.errors.QueryCompilationErrors$.noSuchFunctionError(QueryCompilationErrors.scala:1802)
at org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$$anonfun$apply$23.applyOrElse(Analyzer.scala:2049)
at org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$$anonfun$apply$23.applyOrElse(Analyzer.scala:2036)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:615)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:177)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:615)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$3(TreeNode.scala:620)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren(TreeNode.scala:1249)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren$(TreeNode.scala:1248)
at org.apache.spark.sql.catalyst.expressions.UnaryExpression.mapChildren(Expression.scala:519)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:620)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$3(TreeNode.scala:620)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at scala.collection.TraversableLike.map(TraversableLike.scala:286)
at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
at scala.collection.AbstractTraversable.map(Traversable.scala:108)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:729)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:620)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$3(TreeNode.scala:620)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren(TreeNode.scala:1249)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren$(TreeNode.scala:1248)
at org.apache.spark.sql.catalyst.expressions.UnaryExpression.mapChildren(Expression.scala:519)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:620)
at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$transformExpressionsDownWithPruning$1(QueryPlan.scala:159)
at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$1(QueryPlan.scala:200)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:177)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:200)
at org.apache.spark.sql.catalyst.plans.QueryPlan.recursiveTransform$1(QueryPlan.scala:211)
at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$3(QueryPlan.scala:216)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
at scala.collection.immutable.List.foreach(List.scala:431)
at scala.collection.TraversableLike.map(TraversableLike.scala:286)
at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
at scala.collection.immutable.List.map(List.scala:305)
at org.apache.spark.sql.catalyst.plans.QueryPlan.recursiveTransform$1(QueryPlan.scala:216)
at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$4(QueryPlan.scala:221)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:459)
at org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:221)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDownWithPruning(QueryPlan.scala:159)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsWithPruning(QueryPlan.scala:130)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveExpressionsWithPruning$1.applyOrElse(AnalysisHelper.scala:245)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveExpressionsWithPruning$1.applyOrElse(AnalysisHelper.scala:244)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$2(AnalysisHelper.scala:170)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:177)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$1(AnalysisHelper.scala:170)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDownWithPruning(AnalysisHelper.scala:168)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDownWithPruning$(AnalysisHelper.scala:164)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsWithPruning(AnalysisHelper.scala:99)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsWithPruning$(AnalysisHelper.scala:96)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveExpressionsWithPruning(AnalysisHelper.scala:244)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveExpressionsWithPruning$(AnalysisHelper.scala:242)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveExpressionsWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$.apply(Analyzer.scala:2036)
at org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$.apply(Analyzer.scala:2032)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:215)
at scala.collection.IndexedSeqOptimized.foldLeft(IndexedSeqOptimized.scala:60)
at scala.collection.IndexedSeqOptimized.foldLeft$(IndexedSeqOptimized.scala:68)
at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:38)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeBatch$1(RuleExecutor.scala:212)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$6(RuleExecutor.scala:284)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$RuleExecutionContext$.withContext(RuleExecutor.scala:327)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$5(RuleExecutor.scala:284)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$5$adapted(RuleExecutor.scala:274)
at scala.collection.immutable.List.foreach(List.scala:431)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:274)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:188)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:227)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$1(Analyzer.scala:223)
at org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:172)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:223)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:187)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:179)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:107)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:179)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:208)
at org.apache.spark.sql.catalyst.plans.logic

Incomplete Expression in R

I'm currently running a time series script in R-Markdown where I pass the values of Percent Use and another vector of the time values. I am processing the two separate vectors with the regular c() function within r. The percentage vector is able to be passed through when compilining like normal, however I am running into trouble with the date/time vector. The length of both vectors are 749, the percentage vector just has values 0-100 passed within them. THe date/time vector has strings passed into them as such:
dt=c('2022-06-19 14:05:00.0','2022-06-19 14:06:00.0', ....
If I only pass a few arguments into the dt vector, it will compile regularly, however, once I increase the size to around half of what it needs to be I start getting the following error:
Error: Incomplete expression: dt=c('2022-06-19 12:40:00.0','2022-06-19 12:41:00.0','2022-06-19 12:42:00.0','2022-06-19 12:43:00.0','2022-06-19 12:44:00.0','2022-06-19 12:45:00.0','2022-06-19 12:46:00.0','2022-06-19 12:47:00.0','2022-06-19 12:48:00.0','2022-06-19 12:49:00.0','2022-06-19 12:50:00.0','2022-06-19 12:51:00.0','2022-06-19 12:52:00.0','2022-06-19 12:53:00.0','2022-06-19 12:54:00.0','2022-06-19 12:55:00.0','2022-06-19 12:56:00.0','2022-06-19 12:57:00.0','2022-06-19 12:58:00.0','2022-06-19 12:59:00.0','2022-06-19 13:00:00.0','2022-06-19 13:01:00.0','2022-06-19 13:02:00.0','2022-06-19 13:03:00.0','2022-06-19 13:04:00.0','2022-06-19 13:05:00.0','2022-06-19 13:06:00.0','2022-06-19 13:07:00.0','2022-06-19 13:08:00.0','2022-06-19 13:09:00.0','2022-06-19 13:10:00.0','2022-06-19 13:11:00.0','2022-06-19 13:12:00.0','2022-06-19 13:13:00.0','2022-06-19 13:14:00.0','2022-06-19 13:15:00.0','2022-06-19 13:16:00.0','2022-06-19 13:17:00.0','2022-06-19 13:18:00.0','2022-06-19 13:19:00.0','2022
At first I believed it could be a parenthesis in the wrong place, however, there is no mistakes with that. I've looked at other articles with somewhat similar issues and have seen a concept of a maximum size vector allowed, however the percentage vector was able to pass all 700. Is there a way to bypass this error, I feel that it is a memory/storage issue with R.
The full code is a lot but it is:
dt=c('2022-06-19 12:40:00.0','2022-06-19 12:41:00.0','2022-06-19 12:42:00.0','2022-06-19 12:43:00.0','2022-06-19 12:44:00.0','2022-06-19 12:45:00.0','2022-06-19 12:46:00.0','2022-06-19 12:47:00.0','2022-06-19 12:48:00.0','2022-06-19 12:49:00.0','2022-06-19 12:50:00.0','2022-06-19 12:51:00.0','2022-06-19 12:52:00.0','2022-06-19 12:53:00.0','2022-06-19 12:54:00.0','2022-06-19 12:55:00.0','2022-06-19 12:56:00.0','2022-06-19 12:57:00.0','2022-06-19 12:58:00.0','2022-06-19 12:59:00.0','2022-06-19 13:00:00.0','2022-06-19 13:01:00.0','2022-06-19 13:02:00.0','2022-06-19 13:03:00.0','2022-06-19 13:04:00.0','2022-06-19 13:05:00.0','2022-06-19 13:06:00.0','2022-06-19 13:07:00.0','2022-06-19 13:08:00.0','2022-06-19 13:09:00.0','2022-06-19 13:10:00.0','2022-06-19 13:11:00.0','2022-06-19 13:12:00.0','2022-06-19 13:13:00.0','2022-06-19 13:14:00.0','2022-06-19 13:15:00.0','2022-06-19 13:16:00.0','2022-06-19 13:17:00.0','2022-06-19 13:18:00.0','2022-06-19 13:19:00.0','2022-06-19 13:20:00.0','2022-06-19 13:21:00.0','2022-06-19 13:22:00.0','2022-06-19 13:23:00.0','2022-06-19 13:24:00.0','2022-06-19 13:25:00.0','2022-06-19 13:26:00.0','2022-06-19 13:27:00.0','2022-06-19 13:28:00.0','2022-06-19 13:29:00.0','2022-06-19 13:30:00.0','2022-06-19 13:31:00.0','2022-06-19 13:32:00.0','2022-06-19 13:33:00.0','2022-06-19 13:34:00.0','2022-06-19 13:35:00.0','2022-06-19 13:36:00.0','2022-06-19 13:37:00.0','2022-06-19 13:38:00.0','2022-06-19 13:39:00.0','2022-06-19 13:40:00.0','2022-06-19 13:41:00.0','2022-06-19 13:42:00.0','2022-06-19 13:43:00.0','2022-06-19 13:44:00.0','2022-06-19 13:45:00.0','2022-06-19 13:46:00.0','2022-06-19 13:47:00.0','2022-06-19 13:48:00.0','2022-06-19 13:49:00.0','2022-06-19 13:50:00.0','2022-06-19 13:51:00.0','2022-06-19 13:52:00.0','2022-06-19 13:53:00.0','2022-06-19 13:54:00.0','2022-06-19 13:55:00.0','2022-06-19 13:56:00.0','2022-06-19 13:57:00.0','2022-06-19 13:58:00.0','2022-06-19 13:59:00.0','2022-06-19 14:00:00.0','2022-06-19 14:01:00.0','2022-06-19 14:02:00.0','2022-06-19 14:03:00.0','2022-06-19 14:04:00.0','2022-06-19 14:05:00.0','2022-06-19 14:06:00.0','2022-06-19 14:07:00.0','2022-06-19 14:08:00.0','2022-06-19 14:09:00.0','2022-06-19 14:10:00.0','2022-06-19 14:11:00.0','2022-06-19 14:12:00.0','2022-06-19 14:13:00.0','2022-06-19 14:14:00.0','2022-06-19 14:15:00.0','2022-06-19 14:16:00.0','2022-06-19 14:17:00.0','2022-06-19 14:18:00.0','2022-06-19 14:19:00.0','2022-06-19 14:20:00.0','2022-06-19 14:21:00.0','2022-06-19 14:22:00.0','2022-06-19 14:23:00.0','2022-06-19 14:24:00.0','2022-06-19 14:25:00.0','2022-06-19 14:26:00.0','2022-06-19 14:27:00.0','2022-06-19 14:28:00.0','2022-06-19 14:29:00.0','2022-06-19 14:30:00.0','2022-06-19 14:31:00.0','2022-06-19 14:32:00.0','2022-06-19 14:33:00.0','2022-06-19 14:34:00.0','2022-06-19 14:35:00.0','2022-06-19 14:36:00.0','2022-06-19 14:37:00.0','2022-06-19 14:38:00.0','2022-06-19 14:39:00.0','2022-06-19 14:40:00.0','2022-06-19 14:41:00.0','2022-06-19 14:42:00.0','2022-06-19 14:43:00.0','2022-06-19 14:44:00.0','2022-06-19 14:45:00.0','2022-06-19 14:46:00.0','2022-06-19 14:47:00.0','2022-06-19 14:48:00.0','2022-06-19 14:49:00.0','2022-06-19 14:50:00.0','2022-06-19 14:51:00.0','2022-06-19 14:52:00.0','2022-06-19 14:53:00.0','2022-06-19 14:54:00.0','2022-06-19 14:55:00.0','2022-06-19 14:56:00.0','2022-06-19 14:57:00.0','2022-06-19 14:58:00.0','2022-06-19 14:59:00.0','2022-06-19 15:00:00.0','2022-06-19 15:01:00.0','2022-06-19 15:02:00.0','2022-06-19 15:03:00.0','2022-06-19 15:04:00.0','2022-06-19 15:05:00.0','2022-06-19 15:06:00.0','2022-06-19 15:07:00.0','2022-06-19 15:08:00.0','2022-06-19 15:09:00.0','2022-06-19 15:10:00.0','2022-06-19 15:11:00.0','2022-06-19 15:12:00.0','2022-06-19 15:13:00.0','2022-06-19 15:14:00.0','2022-06-19 15:15:00.0','2022-06-19 15:16:00.0','2022-06-19 15:17:00.0','2022-06-19 15:18:00.0','2022-06-19 15:19:00.0','2022-06-19 15:20:00.0','2022-06-19 15:21:00.0','2022-06-19 15:22:00.0','2022-06-19 15:23:00.0','2022-06-19 15:24:00.0','2022-06-19 15:25:00.0','2022-06-19 15:26:00.0','2022-06-19 15:27:00.0','2022-06-19 15:28:00.0','2022-06-19 15:29:00.0','2022-06-19 15:30:00.0','2022-06-19 15:31:00.0','2022-06-19 15:32:00.0','2022-06-19 15:33:00.0','2022-06-19 15:34:00.0','2022-06-19 15:35:00.0','2022-06-19 15:36:00.0','2022-06-19 15:37:00.0','2022-06-19 15:38:00.0','2022-06-19 15:39:00.0','2022-06-19 15:40:00.0','2022-06-19 15:41:00.0','2022-06-19 15:42:00.0','2022-06-19 15:43:00.0','2022-06-19 15:44:00.0','2022-06-19 15:45:00.0','2022-06-19 15:46:00.0','2022-06-19 15:47:00.0','2022-06-19 15:48:00.0','2022-06-19 15:49:00.0','2022-06-19 15:50:00.0','2022-06-19 15:51:00.0','2022-06-19 15:52:00.0','2022-06-19 15:53:00.0','2022-06-19 15:54:00.0','2022-06-19 15:55:00.0','2022-06-19 15:56:00.0','2022-06-19 15:57:00.0','2022-06-19 15:58:00.0','2022-06-19 15:59:00.0','2022-06-19 16:00:00.0','2022-06-19 16:01:00.0','2022-06-19 16:02:00.0','2022-06-19 16:03:00.0','2022-06-19 16:04:00.0','2022-06-19 16:05:00.0','2022-06-19 16:06:00.0','2022-06-19 16:07:00.0','2022-06-19 16:08:00.0','2022-06-19 16:09:00.0','2022-06-19 16:10:00.0','2022-06-19 16:11:00.0','2022-06-19 16:12:00.0','2022-06-19 16:13:00.0','2022-06-19 16:14:00.0','2022-06-19 16:15:00.0','2022-06-19 16:16:00.0','2022-06-19 16:17:00.0','2022-06-19 16:18:00.0','2022-06-19 16:19:00.0','2022-06-19 16:20:00.0','2022-06-19 16:21:00.0','2022-06-19 16:22:00.0','2022-06-19 16:23:00.0','2022-06-19 16:24:00.0','2022-06-19 16:25:00.0','2022-06-19 16:26:00.0','2022-06-19 16:27:00.0','2022-06-19 16:28:00.0','2022-06-19 16:29:00.0','2022-06-19 16:30:00.0','2022-06-19 16:31:00.0','2022-06-19 16:32:00.0','2022-06-19 16:33:00.0','2022-06-19 16:34:00.0','2022-06-19 16:35:00.0','2022-06-19 16:36:00.0','2022-06-19 16:37:00.0','2022-06-19 16:38:00.0','2022-06-19 16:39:00.0','2022-06-19 16:40:00.0','2022-06-19 16:41:00.0','2022-06-19 16:42:00.0','2022-06-19 16:43:00.0','2022-06-19 16:44:00.0','2022-06-19 16:45:00.0','2022-06-19 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22:55:00.0','2022-06-19 22:56:00.0','2022-06-19 22:57:00.0','2022-06-19 22:58:00.0','2022-06-19 22:59:00.0','2022-06-19 23:00:00.0','2022-06-19 23:01:00.0','2022-06-19 23:02:00.0','2022-06-19 23:03:00.0','2022-06-19 23:04:00.0','2022-06-19 23:05:00.0','2022-06-19 23:06:00.0','2022-06-19 23:07:00.0','2022-06-19 23:08:00.0','2022-06-19 23:09:00.0','2022-06-19 23:10:00.0','2022-06-19 23:11:00.0','2022-06-19 23:12:00.0','2022-06-19 23:13:00.0','2022-06-19 23:14:00.0','2022-06-19 23:15:00.0','2022-06-19 23:16:00.0','2022-06-19 23:17:00.0','2022-06-19 23:18:00.0','2022-06-19 23:19:00.0','2022-06-19 23:20:00.0','2022-06-19 23:21:00.0','2022-06-19 23:22:00.0','2022-06-19 23:23:00.')
I can understand saying there is a issue with the quotations, but I cant find it anywhere. It seems as there is a maximum allowed in a vector, if I compile only the first quarter of the entries it works, else wise on R studio it gives an error, and in the terminal it just expects more code so it gives you a + to add more instead of >.

Decimals (involuntarily) trimmed from values when loaded into environment

I'm working with R 3.6.1 in Rstudio 1.2.1335.
When I assign the following value from a column in my data frame, the values that have decimals in that column in the dataframe, get trimmed in the value I assign:
Dataframe$Column1 has values [368.121 376.436]
Value <-- Dataframe$Column1
And I run my code chunk;
The environment shows the column values as: Value num [1:2] 368 376
My decimals have gone and I need those. Why does this happen and is there a way to fix it?
EDIT:
Set_1.
380.283 332.108 327.405 371.570 325.832 345.583 396.377 367.020 428.980 389.524 379.597 407.483 456.271 312.084 391.198 345.813 406.229 346.450 459.307 392.321 337.638 429.377 353.705 377.512 384.921 346.471 411.855 368.406 386.921 397.797 322.416 412.042 383.240 381.244 440.021 372.444 399.301 345.395 359.865 355.449 314.270 453.173 329.055 299.674 351.675 324.334 425.205 437.013 513.334 436.452 335.658 422.669 300.030 287.893 380.611 297.890 351.203 317.065 350.824 269.149 389.509 467.375 399.065 354.954 465.086 353.615 336.454 372.067 424.167 389.172 357.799 321.663 353.633 388.465 342.489 353.487 398.721 416.194 383.376 355.553 398.667 339.722 316.240 383.894 453.429 351.443 460.038 348.860 304.085 258.921
264.107 241.861 278.548 455.216 393.201 348.211 359.426 427.194 391.599 381.335 340.558 369.617 351.342 318.718 338.960 386.547 388.872 283.943 340.501
Set_2:
380.603 332.100 327.391 371.540 325.826 345.602 396.386 367.029 428.949 389.545 379.584 407.454 456.276 312.093 391.414 345.861 406.235 346.259 459.284 392.334 337.626 429.283 353.539 377.568 384.941 346.491 411.820 368.253 386.816 397.723 322.337 412.020 383.158 381.331 440.066 372.361 399.210 345.438 359.948 355.425 314.271 453.169 328.751 299.701 351.388 324.371 425.219 436.906 513.384 436.475 335.508 422.661 300.036 287.908 380.453 297.306 351.275 317.206 351.165 269.122 389.499 467.402 399.136 354.943 465.057 353.593 336.549 372.079 424.062 389.119 357.753 321.758 353.650 388.599 342.285 353.507 398.682 416.289 383.309 355.456 398.816 339.681 316.273 383.898 453.418 351.395 460.027 348.731 304.111 258.452
264.298 241.829 278.297 455.104 393.228 348.117 359.645 427.096 391.526 381.260 340.474 369.791 351.061 318.780 338.949 386.458 389.030 284.093 340.512
Code:
plot(Set_1,Set_2,col = "red", xlab="Set_1", ylab = "Set_2",
main = "Comparison Set_1 and Set_2", type = 'p')
abline(fit5<-lm(Set_2~Set_1), col="blue")
r5<-round(summary(fit5)$adj.r.square, 4)
text(410,330, paste("R2=",r5))
The decimals aren't gone, they are just not shown in your enviroment. Try accessing the values by Value[1]. This clearly gives you your desired result 368.121.

Import data into R - argument is empty

I am trying to use a R package called GOSemSim, it requires to import a lot of data into variables with a specific format like this:
data1 = c("one", "two", "three")
data2 = c("A", "B", "C")
When the list of data that I try to import into a variable is longer than 293 then I get the following error message:
argument 293 is empty
THere are no error with the "" or comma, I computed it with linux, it does not matter what data it is.
This is really weird basically, I tried on two computers with no luck. I tried to import it as a CSV file but the R package won't allow it.
Anyone knows why you cannot import more than 293 data?
Update:
Here is the code and my data at the same time, it is a one liner in R which has never been a problem for me!
OQ = c("GO:0000003", "GO:0000070", "GO:0000077", "GO:0000079", "GO:0000082", "GO:0000086", "GO:0000122", "GO:0000212", "GO:0000226", "GO:0000278", "GO:0000279", "GO:0000280", "GO:0000724", "GO:0000725", "GO:0000819", "GO:0000910", "GO:0001932", "GO:0002118", "GO:0002121", "GO:0002165", "GO:0003002", "GO:0003006", "GO:0006022", "GO:0006030", "GO:0006040", "GO:0006139", "GO:0006259", "GO:0006260", "GO:0006261", "GO:0006267", "GO:0006270", "GO:0006275", "GO:0006277", "GO:0006281", "GO:0006302", "GO:0006304", "GO:0006305", "GO:0006306", "GO:0006310", "GO:0006323", "GO:0006325", "GO:0006342", "GO:0006351", "GO:0006355", "GO:0006357", "GO:0006366", "GO:0006464", "GO:0006468", "GO:0006479", "GO:0006725", "GO:0006807", "GO:0006928", "GO:0006950", "GO:0006974", "GO:0006996", "GO:0007010", "GO:0007017", "GO:0007018", "GO:0007049", "GO:0007051", "GO:0007059", "GO:0007062", "GO:0007067", "GO:0007076", "GO:0007088", "GO:0007093", "GO:0007095", "GO:0007098", "GO:0007126", "GO:0007127", "GO:0007131", "GO:0007140", "GO:0007141", "GO:0007143", "GO:0007154", "GO:0007155", "GO:0007156", "GO:0007259", "GO:0007266", "GO:0007275", "GO:0007276", "GO:0007281", "GO:0007282", "GO:0007292", "GO:0007304", "GO:0007307", "GO:0007346", "GO:0007350", "GO:0007365", "GO:0007367", "GO:0007379", "GO:0007389", "GO:0007399", "GO:0007400", "GO:0007417", "GO:0007420", "GO:0007423", "GO:0007444", "GO:0007472", "GO:0007476", "GO:0007552", "GO:0007560", "GO:0008104", "GO:0008213", "GO:0008283", "GO:0008284", "GO:0008315", "GO:0008356", "GO:0009059", "GO:0009611", "GO:0009653", "GO:0009790", "GO:0009791", "GO:0009880", "GO:0009886", "GO:0009887", "GO:0009888", "GO:0009889", "GO:0009890", "GO:0009892", "GO:0009893", "GO:0009896", "GO:0009968", "GO:0009987", "GO:0010032", "GO:0010033", "GO:0010092", "GO:0010389", "GO:0010468", "GO:0010498", "GO:0010556", "GO:0010558", "GO:0010564", "GO:0010604", "GO:0010605", "GO:0010608", "GO:0010629", "GO:0010648", "GO:0010948", "GO:0014016", "GO:0014017", "GO:0014070", "GO:0016043", "GO:0016055", "GO:0016070", "GO:0016310", "GO:0016319", "GO:0016321", "GO:0016441", "GO:0016458", "GO:0016568", "GO:0016569", "GO:0016570", "GO:0016571", "GO:0016572", "GO:0017145", "GO:0018130", "GO:0019219", "GO:0019222", "GO:0019438", "GO:0019827", "GO:0019953", "GO:0022402", "GO:0022403", "GO:0022404", "GO:0022412", "GO:0022414", "GO:0022610", "GO:0023052", "GO:0023057", "GO:0030111", "GO:0030154", "GO:0030178", "GO:0030182", "GO:0030261", "GO:0030422", "GO:0030703", "GO:0030727", "GO:0031023", "GO:0031047", "GO:0031050", "GO:0031056", "GO:0031060", "GO:0031123", "GO:0031145", "GO:0031175", "GO:0031323", "GO:0031324", "GO:0031325", "GO:0031326", "GO:0031327", "GO:0031331", "GO:0031398", "GO:0031399", "GO:0031401", "GO:0031570", "GO:0031572", "GO:0031935", "GO:0032268", "GO:0032270", "GO:0032501", "GO:0032502", "GO:0032504", "GO:0032507", "GO:0032774", "GO:0032776", "GO:0032886", "GO:0033043", "GO:0033044", "GO:0033260", "GO:0033301", "GO:0033554", "GO:0034622", "GO:0034641", "GO:0034645", "GO:0034654", "GO:0034754", "GO:0034968", "GO:0035023", "GO:0035107", "GO:0035114", "GO:0035120", "GO:0035186", "GO:0035194", "GO:0035195", "GO:0035220", "GO:0035282", "GO:0035295", "GO:0035825", "GO:0036211", "GO:0036388", "GO:0040029", "GO:0042060", "GO:0042221", "GO:0042445", "GO:0043009", "GO:0043066", "GO:0043069", "GO:0043161", "GO:0043170", "GO:0043331", "GO:0043412", "GO:0043414", "GO:0043549", "GO:0043631", "GO:0043933", "GO:0044237", "GO:0044249", "GO:0044260", "GO:0044271", "GO:0044419", "GO:0044700", "GO:0044702", "GO:0044703", "GO:0044707", "GO:0044728", "GO:0044763", "GO:0044767", "GO:0044770", "GO:0044771", "GO:0044772", "GO:0044773", "GO:0044774", "GO:0044786", "GO:0044818", "GO:0044839", "GO:0044843", "GO:0044848", "GO:0045132", "GO:0045165", "GO:0045168", "GO:0045185", "GO:0045448", "GO:0045455", "GO:0045787", "GO:0045814", "GO:0045859", "GO:0045892", "GO:0045931", "GO:0045934", "GO:0046331", "GO:0046425", "GO:0046483", "GO:0046580", "GO:0046605", "GO:0046777", "GO:0048070", "GO:0048134", "GO:0048135", "GO:0048285", "GO:0048311", "GO:0048468", "GO:0048477", "GO:0048513", "GO:0048518", "GO:0048519", "GO:0048522", "GO:0048523", "GO:0048563", "GO:0048569", "GO:0048583", "GO:0048585", "GO:0048609", "GO:0048646", "GO:0048666", "GO:0048699", "GO:0048704", "GO:0048705", "GO:0048706", "GO:0048707", "GO:0048731", "GO:0048736", "GO:0048737", "GO:0048754", "GO:0048856", "GO:0048863", "GO:0048865", "GO:0048867", "GO:0048869", "GO:0050789", "GO:0050793", "GO:0050794", "GO:0050896", "GO:0051052", "GO:0051058", "GO:0051128", "GO:0051171", "GO:0051172", "GO:0051225", "GO:0051235", "GO:0051246", "GO:0051247", "GO:0051252", "GO:0051253", "GO:0051276", "GO:0051297", "GO:0051299", "GO:0051301", "GO:0051302", "GO:0051321", "GO:0051325", "GO:0051329", "GO:0051338", "GO:0051351", "GO:0051443", "GO:0051445", "GO:0051641", "GO:0051646", "GO:0051651", "GO:0051704", "GO:0051716", "GO:0051726", "GO:0051783", "GO:0051785", "GO:0060255", "GO:0060429", "GO:0060548", "GO:0060688", "GO:0060966", "GO:0060968", "GO:0060993", "GO:0061138", "GO:0065003", "GO:0065004", "GO:0065007", "GO:0070192", "GO:0070507", "GO:0070887", "GO:0070918", "GO:0071103", "GO:0071359", "GO:0071822", "GO:0071824", "GO:0071840", "GO:0071897", "GO:0071900", "GO:0072028", "GO:0072078", "GO:0072079", "GO:0072088", "GO:0080090", "GO:0090068", "GO:0090304", "GO:0090306", "GO:0098609", "GO:1901071", "GO:1901360", "GO:1901362", "GO:1901576", "GO:1901987", "GO:1901988", "GO:1901990", "GO:1901991", "GO:1902275", "GO:1902299", "GO:1902589", "GO:1902679", "GO:1902749", "GO:1903046", "GO:1903047", "GO:1903308", "GO:1903322", "GO:2000026", "GO:2000112", "GO:2000113", "GO:2001141")
The error message in itself is informative. If one tries to make it reproducible, it's best to work with small subsets. It usually helps to have a dead stare at your data before trying to reproduce the behavior. For example,
OQ = c("GO:0000003", "GO:2001141", )
Notice that there are two elements of this character vector. Or are they?
Error in c("GO:0000003", "GO:2001141", ) : argument 3 is empty
Number 3 is the key. R is expecting three elements. Notice the comma after the second element. Once you remove it, you'll be able to create the QQ variable. Scan your real example. I'm sure there's a , , somewhere.
EDIT
I tried copy/pasting your code into a script in Rstudio and it produced the error you describe. If you scroll right, you'll notice that syntax coloring is not working at around position 5000. I have folded the code so that it fits on screen and it runs fine.
This is how I folded the vector and it worked.
OQ = c("GO:0000003", "GO:0000070", "GO:0000077", "GO:0000079", "GO:0000082", "GO:0000086", "GO:0000122",
"GO:0000212", "GO:0000226", "GO:0000278", "GO:0000279", "GO:0000280", "GO:0000724", "GO:0000725",
"GO:0000819", "GO:0000910", "GO:0001932", "GO:0002118", "GO:0002121", "GO:0002165", "GO:0003002",
"GO:0003006", "GO:0006022", "GO:0006030", "GO:0006040", "GO:0006139", "GO:0006259", "GO:0006260",
"GO:0006261", "GO:0006267", "GO:0006270", "GO:0006275", "GO:0006277", "GO:0006281", "GO:0006302",
"GO:0006304", "GO:0006305", "GO:0006306", "GO:0006310", "GO:0006323", "GO:0006325", "GO:0006342",
"GO:0006351", "GO:0006355", "GO:0006357", "GO:0006366", "GO:0006464", "GO:0006468", "GO:0006479",
"GO:0006725", "GO:0006807", "GO:0006928", "GO:0006950", "GO:0006974", "GO:0006996", "GO:0007010",
"GO:0007017", "GO:0007018", "GO:0007049", "GO:0007051", "GO:0007059", "GO:0007062", "GO:0007067",
"GO:0007076", "GO:0007088", "GO:0007093", "GO:0007095", "GO:0007098", "GO:0007126", "GO:0007127",
"GO:0007131", "GO:0007140", "GO:0007141", "GO:0007143", "GO:0007154", "GO:0007155", "GO:0007156",
"GO:0007259", "GO:0007266", "GO:0007275", "GO:0007276", "GO:0007281", "GO:0007282", "GO:0007292",
"GO:0007304", "GO:0007307", "GO:0007346", "GO:0007350", "GO:0007365", "GO:0007367", "GO:0007379",
"GO:0007389", "GO:0007399", "GO:0007400", "GO:0007417", "GO:0007420", "GO:0007423", "GO:0007444",
"GO:0007472", "GO:0007476", "GO:0007552", "GO:0007560", "GO:0008104", "GO:0008213", "GO:0008283",
"GO:0008284", "GO:0008315", "GO:0008356", "GO:0009059", "GO:0009611", "GO:0009653", "GO:0009790",
"GO:0009791", "GO:0009880", "GO:0009886", "GO:0009887", "GO:0009888", "GO:0009889", "GO:0009890",
"GO:0009892", "GO:0009893", "GO:0009896", "GO:0009968", "GO:0009987", "GO:0010032", "GO:0010033",
"GO:0010092", "GO:0010389", "GO:0010468", "GO:0010498", "GO:0010556", "GO:0010558", "GO:0010564",
"GO:0010604", "GO:0010605", "GO:0010608", "GO:0010629", "GO:0010648", "GO:0010948", "GO:0014016",
"GO:0014017", "GO:0014070", "GO:0016043", "GO:0016055", "GO:0016070", "GO:0016310", "GO:0016319",
"GO:0016321", "GO:0016441", "GO:0016458", "GO:0016568", "GO:0016569", "GO:0016570", "GO:0016571",
"GO:0016572", "GO:0017145", "GO:0018130", "GO:0019219", "GO:0019222", "GO:0019438", "GO:0019827",
"GO:0019953", "GO:0022402", "GO:0022403", "GO:0022404", "GO:0022412", "GO:0022414", "GO:0022610",
"GO:0023052", "GO:0023057", "GO:0030111", "GO:0030154", "GO:0030178", "GO:0030182", "GO:0030261",
"GO:0030422", "GO:0030703", "GO:0030727", "GO:0031023", "GO:0031047", "GO:0031050", "GO:0031056",
"GO:0031060", "GO:0031123", "GO:0031145", "GO:0031175", "GO:0031323", "GO:0031324", "GO:0031325",
"GO:0031326", "GO:0031327", "GO:0031331", "GO:0031398", "GO:0031399", "GO:0031401", "GO:0031570",
"GO:0031572", "GO:0031935", "GO:0032268", "GO:0032270", "GO:0032501", "GO:0032502", "GO:0032504",
"GO:0032507", "GO:0032774", "GO:0032776", "GO:0032886", "GO:0033043", "GO:0033044", "GO:0033260",
"GO:0033301", "GO:0033554", "GO:0034622", "GO:0034641", "GO:0034645", "GO:0034654", "GO:0034754",
"GO:0034968", "GO:0035023", "GO:0035107", "GO:0035114", "GO:0035120", "GO:0035186", "GO:0035194",
"GO:0035195", "GO:0035220", "GO:0035282", "GO:0035295", "GO:0035825", "GO:0036211", "GO:0036388",
"GO:0040029", "GO:0042060", "GO:0042221", "GO:0042445", "GO:0043009", "GO:0043066", "GO:0043069",
"GO:0043161", "GO:0043170", "GO:0043331", "GO:0043412", "GO:0043414", "GO:0043549", "GO:0043631",
"GO:0043933", "GO:0044237", "GO:0044249", "GO:0044260", "GO:0044271", "GO:0044419", "GO:0044700",
"GO:0044702", "GO:0044703", "GO:0044707", "GO:0044728", "GO:0044763", "GO:0044767", "GO:0044770",
"GO:0044771", "GO:0044772", "GO:0044773", "GO:0044774", "GO:0044786", "GO:0044818", "GO:0044839",
"GO:0044843", "GO:0044848", "GO:0045132", "GO:0045165", "GO:0045168", "GO:0045185", "GO:0045448",
"GO:0045455", "GO:0045787", "GO:0045814", "GO:0045859", "GO:0045892", "GO:0045931", "GO:0045934",
"GO:0046331", "GO:0046425", "GO:0046483", "GO:0046580", "GO:0046605", "GO:0046777", "GO:0048070",
"GO:0048134", "GO:0048135", "GO:0048285", "GO:0048311", "GO:0048468", "GO:0048477", "GO:0048513",
"GO:0048518", "GO:0048519", "GO:0048522", "GO:0048523", "GO:0048563", "GO:0048569", "GO:0048583",
"GO:0048585", "GO:0048609", "GO:0048646", "GO:0048666", "GO:0048699", "GO:0048704", "GO:0048705",
"GO:0048706", "GO:0048707", "GO:0048731", "GO:0048736", "GO:0048737", "GO:0048754", "GO:0048856",
"GO:0048863", "GO:0048865", "GO:0048867", "GO:0048869", "GO:0050789", "GO:0050793", "GO:0050794",
"GO:0050896", "GO:0051052", "GO:0051058", "GO:0051128", "GO:0051171", "GO:0051172", "GO:0051225",
"GO:0051235", "GO:0051246", "GO:0051247", "GO:0051252", "GO:0051253", "GO:0051276", "GO:0051297",
"GO:0051299", "GO:0051301", "GO:0051302", "GO:0051321", "GO:0051325", "GO:0051329", "GO:0051338",
"GO:0051351", "GO:0051443", "GO:0051445", "GO:0051641", "GO:0051646", "GO:0051651", "GO:0051704",
"GO:0051716", "GO:0051726", "GO:0051783", "GO:0051785", "GO:0060255", "GO:0060429", "GO:0060548",
"GO:0060688", "GO:0060966", "GO:0060968", "GO:0060993", "GO:0061138", "GO:0065003", "GO:0065004",
"GO:0065007", "GO:0070192", "GO:0070507", "GO:0070887", "GO:0070918", "GO:0071103", "GO:0071359",
"GO:0071822", "GO:0071824", "GO:0071840", "GO:0071897", "GO:0071900", "GO:0072028", "GO:0072078",
"GO:0072079", "GO:0072088", "GO:0080090", "GO:0090068", "GO:0090304", "GO:0090306", "GO:0098609",
"GO:1901071", "GO:1901360", "GO:1901362", "GO:1901576", "GO:1901987", "GO:1901988", "GO:1901990",
"GO:1901991", "GO:1902275", "GO:1902299", "GO:1902589", "GO:1902679", "GO:1902749", "GO:1903046",
"GO:1903047", "GO:1903308", "GO:1903322", "GO:2000026", "GO:2000112", "GO:2000113", "GO:2001141")

Is there an 11 digits limit for time series numbers in x12 for R?

I am trying to use the x12 function in the x12 package for R.
My problem is, when using time series object (tso) with monthly data and each observation is a large number (11 or more digits), the function is making a spec file which x12a.exe (binaries) can not read.
x12 binaries does not allow the spec file to be wider then 132 column.
In my example, the spec file have 144 columns, which I believe give me this error message in R:"ERROR: Input record longer than limit : 133".
When I am using smaller numbers (fewer columns) in the spec file, there are no problem so far. When creating the spec file on my own, when using x12-arima for windows, I have never seen the problem before, because I always use the "free" format (one observation per line) for the series in x12-arima.
My question is: How do I make the format for the time series object = "free", or some how just one observation per line, in the "Rout.spc" file, while using x12 function in the x12 package for R?
I am using R version 2.15.2 and R-studio version 0.97.318
Attached is my example code in R-studio, output in R-console, and the spec file
"Rstudio"
library(x12)
alt <- read.csv2("alt.csv",header=T)
tal <- ts(data=alt,start=c(1995,4),freq=12)
x12path <- shortPathName("C:\\Dokumenter\\X_12_Arima_Program\\x12a\\x12a.exe")
x12tal <- x12(tso=tal,automdl=T,x12path=x12path,period=12,trendma=23)
"Console"
C:\Dokumenter\Eksperimentering\x12>md gra
C:\Dokumenter\Eksperimentering\x12>C:\DOKUME~1\X_12_A~2\x12a\x12a.exe Rout -g gra
X-12-ARIMA Seasonal Adjustment Program
Version Number 0.3 Build 192
Execution began Mar 12, 2013 23.46.25
Reading input spec file from Rout.spc
Storing any program output into Rout.out
Storing any program error messages into Rout.err
ERROR: Input record longer than limit : 133
Line 6: start=1995.4
^
ERROR: Expected an real number not "111"
Program error(s) halt execution for Rout.spc
Check error file Rout.err
Error messages generated from processing the X-12-ARIMA spec file
Rout.spc:
Error in readx12Out(file, freq_series = frequency(tso), start_series = start(tso), :
Error! No proper run of x12! Check your parameter settings.
"The spec file: Rout.spc"
series{
title="R Output for X12a"
decimals=2
start=1995.4
period=12
data=(
14056669449 12785389868 12772341230 12342935128 12081332395 12110109950 12367542268 12911930417 12836340370 12214486074 12057940408 11555540809
10002847699 9199284760 8704422249 8492914782 8507816348 8470254675 8665139772 8653204621 9177471163 9676069791 9483990311 9825510541
7613345714 7168896536 7527318694 7721174940 7584049271 7586159794 7411383039 7565724342 7555103032 7148551906 7792379395 7493885451
6636374143 6390731897 6160711917 6003196233 5955867663 5868369296 5858314348 6098506333 6297774946 6074680955 6132163345 5875098456
5198306672 4891946405 4875765641 4834436461 4835096514 4804664875 4684550404 4733459404 5056773308 4912329843 5080643820 4568733581
4286693348 3898776528 3872776341 3842469172 3756957390 3782676505 3924066331 3810475969 3943259720 3665136687 3962811976 3449264257
3120637669 2813261665 2692920289 2652153941 2557247524 2658115616 2777287302 2688976703 2712004412 2596430893 2520548046 2455531008
2429263753 2187017586 2181610529 2139024441 2008850781 2049874584 2110715482 2218937956 2565352715 2635375627 2598584163 2435211675
2433625715 2350144562 2298764466 2242464445 2288528533 2532374821 2696862060 2877128057 3086285374 3309497319 3684989376 3709283880
3483967873 3294407926 3465439983 3546006197 3526166213 3625899404 3774201496 3941610691 4325836434 4466576126 4115121591 4036118609
3824882119 3552896925 3649624960 3570454122 3622089655 3662984491 3601306018 3604389348 3620162022 3401732239 3158217491 2896252892
2800864675 2630474256 2668229303 2631120097 2343131082 2163910930 2108285015 2067601541 2099699134 1803097392 1742652674 1626660618
1560369744 1448264771 1419659828 1547101381 1310783818 1358686467 1300281852 1315247637 1380387680 1286158497 1329769957 1272124521
1185603967 1125238745 1217223861 1265616553 1222054134 1279497332 1499392605 1810208712 2314301847 2908395453 3388479445 3441615991
3432688695 3691000321 3891303059 4111250935 4258776704 4586315450 5050122946 5156728599 5550332779 5769588984 5943764465 6032516246
5765718572 5521116586 5498458566 5374456514 5130561755 5219814632 5542173962 6883624616 7744043244 7913799960 7416210299 7127265644
6790509897 6562709494 6390985216 6126897801 5855125688 6259675447 6439114484 6634617502 6771498442 6674343925 6295709586 5890916431
5545655270 5315444742 5205711894 5115065476 4648229650 4724377012 4816989052 5049928441 5041395923
)
}
transform{
function=auto
}
automdl {
maxorder=(3,2)
maxdiff=(1,1)
balanced=yes
savelog=(adf amd b5m mu)
}
forecast {
}
x11{
sigmalim=(1.5,2.5)
trendma=23
excludefcst=yes
final=(user)
appendfcst=yes
savelog=all
}

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