Error when using neural networks (CARET package) - r

Code:
library(nnet)
library(caret)
#K-folds resampling method for fitting model
ctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 10,
allowParallel = TRUE) #10 separate 10-fold cross-validations
nnetGrid <- expand.grid(decay = seq(0.0002, .0008, length = 4),
size = seq(6, 10, by = 2),
bag = FALSE)
set.seed(100)
nnetFitcv <- train(R ~ .,
data = trainSet,
method = "avNNet",
tuneGrid = nnetGrid,
trControl = ctrl,
preProc = c("center", "scale"),
linout = TRUE,
## Reduce the amount of printed output
trace = FALSE,
## Expand the number of iterations to find
## parameter estimates..
maxit = 2000,
## and the number of parameters used by the model
MaxNWts = 5 * (34 + 1) + 5 + 1)
Error:
Error in train.default(x, y, weights = w, ...) :
final tuning parameters could not be determined
In addition: Warning messages:
1: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
There were missing values in resampled performance measures.
2: In train.default(x, y, weights = w, ...) :
missing values found in aggregated results
data:
dput(head(trainSet))
structure(list(fy = c(317.913756282, 365.006253069, 392.548100067,
305.350697829, 404.999341917, 326.558279739), fu = c(538.962896683,
484.423120589, 607.974981919, 566.461909098, 580.287855801, 454.178316794
), E = c(194617.707566, 181322.455065, 206661.286272, 182492.029532,
189867.929239, 181991.379749), eu = c(0.153782620813, 0.208857408687,
0.29933255604, 0.277013319499, 0.251278125174, 0.20012525805),
imp_local = c(1555.3450957, 1595.41614044, 763.56392418,
1716.78277731, 1045.72429616, 802.742305814), imp_global = c(594.038972858,
1359.48216529, 1018.89209367, 850.887850177, 1381.3557372,
1714.66351462), teta1c = c(0.033375064111, 0.021482368218,
0.020905367537, 0.006956337817, 0.034913536977, 0.03009770223
), k1c = c(4000921.55552, 4499908.41979, 9764999.26902, 9273400.46159,
6163057.88855, 12338543.5703), k2_2L = c(98633499.5682, 53562216.5496,
51597126.6866, 79496746.0098, 54060378.6334, 88854286.5457
), k2_3L = c(53752551.0262, 125020222.794, 124021434.482,
125817803.431, 75021821.6702, 35160224.288), k2_4L = c(56725106.5978,
126865701.893, 145764489.664, 64837586.8755, 49128911.0832,
70088564.0166), bmaxc = c(3481281.32908, 4393584.00639, 2614830.02391,
3128593.72039, 3179348.29527, 4274637.35956), dfactorc = c(2.5474729895,
2.94296926288, 2.79505551368, 2.47882735165, 2.46407943564,
1.41121223341), amaxc = c(73832.9746763, 99150.5068997, 77165.4338508,
128546.996471, 53819.0447533, 54870.9707106), teta1s = c(0.015467320192,
0.013675755546, 0.031668366149, 0.028898297322, 0.019211801086,
0.013349768955), k1s = c(5049506.54552, 11250622.6842, 13852560.5089,
18813117.5726, 18362782.7372, 14720875.0829), k2_ab1s = c(276542468.441,
275768806.723, 211613299.608, 264475187.749, 162043062.526,
252936228.465), k2_ab2s = c(108971516.033, 114017918.32,
248886114.151, 213529935.615, 236891513.077, 142986118.909
), k2_ab3s = c(33306211.9166, 28220338.4744, 40462423.2281,
23450400.4429, 46044346.1128, 23695405.2598), bmaxab1 = c(4763935.86742,
4297372.01966, 3752983.00638, 4861240.46459, 4269771.8481,
4162098.23435), bmaxab2 = c(1864128.647, 1789714.6047, 2838412.50704,
2122535.96812, 2512362.60884, 1176995.61871), ab1 = c(66.4926766666,
42.7771212442, 45.4212664748, 50.3764074404, 35.4792060556,
34.1116517971), ab2 = c(21.0285105309, 23.5869838719, 18.8524808986,
10.1121885612, 10.9695055644, 12.1154127169), dfactors = c(2.47803921947,
0.874644748155, 0.749837099991, 1.96711589185, 2.5407774352,
1.28554379333), teta1f = c(0.037308451805, 0.035718600749,
0.012495093438, 0.000815957999, 0.002155991091, 0.02579104469
), k1f = c(14790480.9871, 17223538.1853, 19930679.8931, 3524230.46974,
15721827.0137, 13599317.0371), k2f = c(55614283.976, 54695745.7762,
86690362.7036, 99857853.7312, 63119072.711, 37510791.5472
), bmaxf = c(2094770.19484, 3633133.51482, 1361188.05421,
2001027.51219, 2534273.6726, 3765850.14143), dfactorf = c(0.745459795314,
2.04869176933, 0.853221909609, 1.76652410119, 0.523675021418,
1.0808768613), k2b = c(1956.92858062, 1400.78738327, 1771.23607857,
1104.05501369, 1756.6767193, 1509.9294956), amaxb = c(38588.0915097,
35158.1672213, 25711.062782, 21103.1603387, 27230.6973685,
43720.3558889999), dfactorb = c(0.822346959126, 2.34421354848,
0.79990635332, 2.99070447299, 1.76373031599, 1.38640223249
), roti = c(16.1560390049, 12.7223971386, 6.43238062144,
15.882552267, 16.0836252663, 18.2734832893), rotmaxbp = c(0.235615453341,
0.343204895932, 0.370304533553, 0.488746319999, 0.176135112774,
0.46921999001), R = c(0.022186087, 0.023768855, 0.023911029,
0.023935705, 0.023655335, 0.022402726)), .Names = c("fy",
"fu", "E", "eu", "imp_local", "imp_global", "teta1c", "k1c",
"k2_2L", "k2_3L", "k2_4L", "bmaxc", "dfactorc", "amaxc", "teta1s",
"k1s", "k2_ab1s", "k2_ab2s", "k2_ab3s", "bmaxab1", "bmaxab2",
"ab1", "ab2", "dfactors", "teta1f", "k1f", "k2f", "bmaxf", "dfactorf",
"k2b", "amaxb", "dfactorb", "roti", "rotmaxbp", "R"), row.names = c(7L,
8L, 20L, 23L, 28L, 29L), class = "data.frame")
data has no equal rows or zero values or NaNs. Any help is appreciated.

I guess the problem is caused by MaxNWts, which is The maximum allowable number of weights. The value you gave is less than the weights for networks with size larger than 5 units. It should be at least:
MaxNWts = max(nnetGrid$size)*(ncol(trainSet) + output_neron)
+ max(nnetGrid$size) + output_neron
So, in your case, it should be at least MaxNWts = 10 * (34 + 1) + 10 + 1

Related

uwot is throwing an error running the Monocle3 R package's "find_gene_module()" function, likely as an issue with how my data is formatted

I am trying to run the Monocle3 function find_gene_modules() on a cell_data_set (cds) but am getting a variety of errors in this. I have not had any other issues before this. I am working with an imported Seurat object. My first error came back stating that the number of rows were not the same between my cds and cds#preprocess_aux$gene_loadings values. I took a look and it seems my gene loadings were a list under cds#preprocess_aux#listData$gene_loadings. I then ran the following code to make a dataframe version of the gene loadings:
test <- seurat#assays$RNA#counts#Dimnames[[1]]
test <- as.data.frame(test)
cds#preprocess_aux$gene_loadings <- test
rownames(cds#preprocess_aux$gene_loadings) <- cds#preprocess_aux$gene_loadings[,1]
Which created a cds#preprocess_aux$gene_loadings dataframe with the same number of rows and row names as my cds. This resolved my original error but now led to a new error being thrown from uwot as:
15:34:02 UMAP embedding parameters a = 1.577 b = 0.8951
Error in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components, :
No numeric columns found
Running traceback() produces the following information.
> traceback()
4: stop("No numeric columns found")
3: uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
metric = metric, n_epochs = n_epochs, alpha = learning_rate,
scale = scale, init = init, init_sdev = init_sdev, spread = spread,
min_dist = min_dist, set_op_mix_ratio = set_op_mix_ratio,
local_connectivity = local_connectivity, bandwidth = bandwidth,
gamma = repulsion_strength, negative_sample_rate = negative_sample_rate,
a = a, b = b, nn_method = nn_method, n_trees = n_trees, search_k = search_k,
method = "umap", approx_pow = approx_pow, n_threads = n_threads,
n_sgd_threads = n_sgd_threads, grain_size = grain_size, y = y,
target_n_neighbors = target_n_neighbors, target_weight = target_weight,
target_metric = target_metric, pca = pca, pca_center = pca_center,
pca_method = pca_method, pcg_rand = pcg_rand, fast_sgd = fast_sgd,
ret_model = ret_model || "model" %in% ret_extra, ret_nn = ret_nn ||
"nn" %in% ret_extra, ret_fgraph = "fgraph" %in% ret_extra,
batch = batch, opt_args = opt_args, epoch_callback = epoch_callback,
tmpdir = tempdir(), verbose = verbose)
2: uwot::umap(as.matrix(preprocess_mat), n_components = max_components,
metric = umap.metric, min_dist = umap.min_dist, n_neighbors = umap.n_neighbors,
fast_sgd = umap.fast_sgd, n_threads = cores, verbose = verbose,
nn_method = umap.nn_method, ...)
1: find_gene_modules(cds[pr_deg_ids, ], reduction_method = "UMAP",
max_components = 2, umap.metric = "cosine", umap.min_dist = 0.1,
umap.n_neighbors = 15L, umap.fast_sgd = FALSE, umap.nn_method = "annoy",
k = 20, leiden_iter = 1, partition_qval = 0.05, weight = FALSE,
resolution = 0.001, random_seed = 0L, cores = 1, verbose = T)
I really have no idea what I am doing wrong or how to proceed from here. Does anyone with experience with uwot know where my error is coming from? Really appreciate the help!

ERROR: unused argument (output.results = TRUE)

This is how my data looks like:
> dput(head(GDP_NUTS2,5))
structure(list(Regiao = c("T", "N", "Ag", "C", "AML"), t2000 = c(12529.42964,
10054.60679, 13045.59069, 10621.51789, 18104.36306), t2001 = c(13142.7713,
10652.46712, 13920.41552, 11101.08412, 18865.55149), t2002 = c(13714.17406,
11001.34917, 14612.37052, 11507.36163, 19812.29293), t2003 = c(13985.02689,
11031.7278, 15137.89461, 11884.96687, 20165.68892), t2004 = c(14537.15966,
11354.02317, 15479.68985, 12364.05053, 21068.05117), t2005 = c(15107.92333,
11875.44359, 16237.49791, 12754.40299, 21829.31373), t2006 = c(15816.27567,
12439.6426, 17046.29326, 13378.47797, 22714.25829), t2007 = c(16660.99538,
13229.02402, 17981.40383, 14044.39707, 23847.44923), t2008 = c(16971.19746,
13579.51144, 18226.74178, 14091.85326, 24347.83971), t2009 = c(16606.6617,
13243.19054, 17038.45595, 13974.46502, 23794.44899), t2010 = c(16986.91604,
13677.38358, 16976.83391, 14284.14565, 24119.66719), t2011 = c(16655.71238,
13491.68626, 16347.69468, 14011.54637, 23503.1765), t2012 = c(15963.69251,
13111.6173, 16059.51047, 13623.68635, 22118.01701), t2013 = c(16257.04222,
13473.68717, 16301.87448, 13919.18355, 22337.24739), t2014 = c(16596.21219,
13935.07757, 16974.57715, 14220.1043, 22491.62875), t2015 = c(17322.0514,
14570.33755, 17851.78088, 14983.95312, 23101.89351), t2016 = c(18033.44444,
15283.33044, 19251.57661, 15620.77307, 23800.20038), t2017 = c(19006.33518,
16083.53849, 20893.19975, 16410.11278, 24938.22636), t2018 = c(19938.15583,
17031.94867, 22131.96942, 17242.70015, 25974.24055), t2019 = c(20755.955,
17712.44223, 23145.30242, 18045.54697, 26970.71178)), row.names = c(NA,
-5L), class = c("tbl_df", "tbl", "data.frame"))
I'm using the "REAT" package to test the absolute beta convergence comparing years 2000 (t2000) and 2019 (t2019) with OLS (Ordinary Least Squares) estimation using function betaconv.ols().
I've used this code: betaconv.ols(GDP_NUTS2$t2000, 2000, GDP_NUTS2$t2019, 2019, output.results = TRUE) I tried other version of the code but my major problem is the output.results=TRUE because I get always this error: Error in betaconv.ols(GDP_NUTS2$t2000, 2000, GDP_NUTS2$t2019, 2019, output.results = TRUE) : unused argument (output.results = TRUE)
I've been searching for alternatives of output.results but no success.
Any help will be much appreciated.
The argument is print.results based on the args of the function
> args(betaconv.ols)
function (gdp1, time1, gdp2, time2, conditions = NULL, beta.plot = FALSE,
beta.plotPSize = 1, beta.plotPCol = "black", beta.plotLine = FALSE,
beta.plotLineCol = "red", beta.plotX = "Ln (initial)", beta.plotY = "Ln (growth)",
beta.plotTitle = "Beta convergence", beta.bgCol = "gray95",
beta.bgrid = TRUE, beta.bgridCol = "white", beta.bgridSize = 2,
beta.bgridType = "solid", print.results = FALSE)
NULL
betaconv.ols(GDP_NUTS2$t2000, 2000, GDP_NUTS2$t2019, 2019, print.results = TRUE)
-output
Absolute Beta Convergence
Model coefficients (Estimation method: OLS)
Estimate Std. Error t value Pr (>|t|)
Alpha 1.537689e-01 0.048509886 3.169847 0.05048663
Beta -1.341938e-02 0.005137275 -2.612158 0.07953682
Lambda 7.110647e-04 NA NA NA
Halflife 9.748018e+02 NA NA NA
Model summary
Estimate F value df 1 df 2 Pr (>F)
R-Squared 0.6946059 6.823372 1 3 0.07953682

specify `makeNumericVectorParam` for `hidden_dropout_ratios` hyper parameter which would depend on the number of hidden layers

I would like to tune "classif.h2o.deeplearning" learner via mlr. During the tuning I have several architectures I would like explored. For each of these architectures I would like to specify a dropout space. However I am struggling with this.
Example:
library(mlr)
library(h2o)
ctrl <- makeTuneControlRandom(maxit = 10)
lrn <- makeLearner("classif.h2o.deeplearning", predict.type = "prob")
I define two architectures "a" and "b" via the "hidden" DiscreteParam, for each of them I would like to create a NumericVectorParam of "hidden_dropout_ratios"
par_set <- makeParamSet(
makeDiscreteParam("hidden", values = list(a = c(16L, 16L),
b = c(16L, 16L, 16L))),
makeDiscreteParam("activation", values = "RectifierWithDropout", tunable = FALSE),
makeNumericParam("input_dropout_ratio", lower = 0, upper = 0.4, default = 0.1),
makeNumericVectorParam("hidden_dropout_ratios", len = 2, lower = 0, upper = 0.6, default = rep(0.3, 2),
requires = quote(length(hidden) == 2)),
makeNumericVectorParam("hidden_dropout_ratios", len = 3, lower = 0, upper = 0.6, default = rep(0.3, 3),
requires = quote(length(hidden) == 3)))
this produces an error:
Error in makeParamSet(makeDiscreteParam("hidden", values = list(a = c(16L, :
All parameters must have unique names!
Setting just one of them results in dropout being applied only on architectures of appropriate number of hidden layers.
When I attempt to use the same dropout for all hidden layers:
par_set <- makeParamSet(
makeDiscreteParam("hidden", values = list(a = c(16L, 16L),
b = c(16L, 16L, 16L))),
makeDiscreteParam("activation", values = "RectifierWithDropout", tunable = FALSE),
makeNumericParam("input_dropout_ratio", lower = 0, upper = 0.4, default = 0.1),
makeNumericParam("hidden_dropout_ratios", lower = 0, upper = 0.6, default = 0.3))
tw <- makeTuneWrapper(lrn,
resampling = cv3,
control = ctrl,
par.set = par_set,
show.info = TRUE,
measures = list(auc,
bac))
perf_tw <- resample(tw,
task = sonar.task,
resampling = cv5,
extract = getTuneResult,
models = TRUE,
show.info = TRUE,
measures = list(auc,
bac))
I get the error:
Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = page, :
ERROR MESSAGE:
Illegal argument(s) for DeepLearning model: DeepLearning_model_R_1566289564965_2. Details: ERRR on field: _hidden_dropout_ratios: Must have 3 hidden layer dropout ratios.
Perhaps I could overcome this by creating a separate learner for each architecture and then combining with makeModelMultiplexer?
I would like your help in overcoming this. Thanks.
EDIT: I was able to overcome this using makeModelMultiplexer and by creating a learner for each architecture (number of hidden layers).
base_lrn <- list(
makeLearner("classif.h2o.deeplearning",
id = "h20_2",
predict.type = "prob"),
makeLearner("classif.h2o.deeplearning",
id = "h20_3",
predict.type = "prob"))
mm_lrn <- makeModelMultiplexer(base_lrn)
par_set <- makeParamSet(
makeDiscreteParam("selected.learner", values = extractSubList(base_lrn, "id")),
makeDiscreteParam("h20_2.hidden", values = list(a = c(16L, 16L),
b = c(32L, 32L)),
requires = quote(selected.learner == "h20_2")),
makeDiscreteParam("h20_3.hidden", values = list(a = c(16L, 16L, 16L),
b = c(32L, 32L, 32L)),
requires = quote(selected.learner == "h20_3")),
makeDiscreteParam("h20_2.activation", values = "RectifierWithDropout", tunable = FALSE,
requires = quote(selected.learner == "h20_2")),
makeDiscreteParam("h20_3.activation", values = "RectifierWithDropout", tunable = FALSE,
requires = quote(selected.learner == "h20_3")),
makeNumericParam("h20_2.input_dropout_ratio", lower = 0, upper = 0.4, default = 0.1,
requires = quote(selected.learner == "h20_2")),
makeNumericParam("h20_3.input_dropout_ratio", lower = 0, upper = 0.4, default = 0.1,
requires = quote(selected.learner == "h20_3")),
makeNumericVectorParam("h20_2.hidden_dropout_ratios", len = 2, lower = 0, upper = 0.6, default = rep(0.3, 2),
requires = quote(selected.learner == "h20_2")),
makeNumericVectorParam("h20_3.hidden_dropout_ratios", len = 3, lower = 0, upper = 0.6, default = rep(0.3, 3),
requires = quote(selected.learner == "h20_3")))
tw <- makeTuneWrapper(mm_lrn,
resampling = cv3,
control = ctrl,
par.set = par_set,
show.info = TRUE,
measures = list(auc,
bac))
perf_tw <- resample(tw,
task = sonar.task,
resampling = cv5,
extract = getTuneResult,
models = TRUE,
show.info = TRUE,
measures = list(auc,
bac))
Is there a more elegant solution?
I've no experience with h2o learners or their deep learning approach.
However, specifying the same parameter twice in a single ParamSet (as your first try) won't work. So you will always need to use two ParamSets anyways.
I cannot say anything about the second error you are getting. This looks like a h2o related problem.
Using makeModelMultiplexer() is one option. You can also use single benchmark() calls and aggregate them afterwards.

Error when using adaptive resampling (CARET package)

Code:
library(caret)
#adaptative control resampling method for fitting svr
ctrlada <- trainControl(method = "adaptive_cv", number = 10, returnResamp = "final",
adaptive = list(min = 5,
alpha = 0.05,
method = "gls",
complete = TRUE),
allowParallel = TRUE) #10 separate 10-fold cross-validations are used as the resampling scheme
set.seed(100)
marsFitacv <- train(R ~ ., data = trainSet,
method = "earth",
tuneGrid = expand.grid(degree = 2, nprune = 40:80),
trControl = ctrlada)
error:
x parameter filtering failed
Error in `$<-.data.frame`(`*tmp*`, "nprune", value = NA) :
replacement has 1 row, data has 0
data:
dput(head(trainSet))
structure(list(fy = c(317.913756282, 365.006253069, 392.548100067,
305.350697829, 404.999341917, 326.558279739), fu = c(538.962896683,
484.423120589, 607.974981919, 566.461909098, 580.287855801, 454.178316794
), E = c(194617.707566, 181322.455065, 206661.286272, 182492.029532,
189867.929239, 181991.379749), eu = c(0.153782620813, 0.208857408687,
0.29933255604, 0.277013319499, 0.251278125174, 0.20012525805),
imp_local = c(1555.3450957, 1595.41614044, 763.56392418,
1716.78277731, 1045.72429616, 802.742305814), imp_global = c(594.038972858,
1359.48216529, 1018.89209367, 850.887850177, 1381.3557372,
1714.66351462), teta1c = c(0.033375064111, 0.021482368218,
0.020905367537, 0.006956337817, 0.034913536977, 0.03009770223
), k1c = c(4000921.55552, 4499908.41979, 9764999.26902, 9273400.46159,
6163057.88855, 12338543.5703), k2_2L = c(98633499.5682, 53562216.5496,
51597126.6866, 79496746.0098, 54060378.6334, 88854286.5457
), k2_3L = c(53752551.0262, 125020222.794, 124021434.482,
125817803.431, 75021821.6702, 35160224.288), k2_4L = c(56725106.5978,
126865701.893, 145764489.664, 64837586.8755, 49128911.0832,
70088564.0166), bmaxc = c(3481281.32908, 4393584.00639, 2614830.02391,
3128593.72039, 3179348.29527, 4274637.35956), dfactorc = c(2.5474729895,
2.94296926288, 2.79505551368, 2.47882735165, 2.46407943564,
1.41121223341), amaxc = c(73832.9746763, 99150.5068997, 77165.4338508,
128546.996471, 53819.0447533, 54870.9707106), teta1s = c(0.015467320192,
0.013675755546, 0.031668366149, 0.028898297322, 0.019211801086,
0.013349768955), k1s = c(5049506.54552, 11250622.6842, 13852560.5089,
18813117.5726, 18362782.7372, 14720875.0829), k2_ab1s = c(276542468.441,
275768806.723, 211613299.608, 264475187.749, 162043062.526,
252936228.465), k2_ab2s = c(108971516.033, 114017918.32,
248886114.151, 213529935.615, 236891513.077, 142986118.909
), k2_ab3s = c(33306211.9166, 28220338.4744, 40462423.2281,
23450400.4429, 46044346.1128, 23695405.2598), bmaxab1 = c(4763935.86742,
4297372.01966, 3752983.00638, 4861240.46459, 4269771.8481,
4162098.23435), bmaxab2 = c(1864128.647, 1789714.6047, 2838412.50704,
2122535.96812, 2512362.60884, 1176995.61871), ab1 = c(66.4926766666,
42.7771212442, 45.4212664748, 50.3764074404, 35.4792060556,
34.1116517971), ab2 = c(21.0285105309, 23.5869838719, 18.8524808986,
10.1121885612, 10.9695055644, 12.1154127169), dfactors = c(2.47803921947,
0.874644748155, 0.749837099991, 1.96711589185, 2.5407774352,
1.28554379333), teta1f = c(0.037308451805, 0.035718600749,
0.012495093438, 0.000815957999, 0.002155991091, 0.02579104469
), k1f = c(14790480.9871, 17223538.1853, 19930679.8931, 3524230.46974,
15721827.0137, 13599317.0371), k2f = c(55614283.976, 54695745.7762,
86690362.7036, 99857853.7312, 63119072.711, 37510791.5472
), bmaxf = c(2094770.19484, 3633133.51482, 1361188.05421,
2001027.51219, 2534273.6726, 3765850.14143), dfactorf = c(0.745459795314,
2.04869176933, 0.853221909609, 1.76652410119, 0.523675021418,
1.0808768613), k2b = c(1956.92858062, 1400.78738327, 1771.23607857,
1104.05501369, 1756.6767193, 1509.9294956), amaxb = c(38588.0915097,
35158.1672213, 25711.062782, 21103.1603387, 27230.6973685,
43720.3558889999), dfactorb = c(0.822346959126, 2.34421354848,
0.79990635332, 2.99070447299, 1.76373031599, 1.38640223249
), roti = c(16.1560390049, 12.7223971386, 6.43238062144,
15.882552267, 16.0836252663, 18.2734832893), rotmaxbp = c(0.235615453341,
0.343204895932, 0.370304533553, 0.488746319999, 0.176135112774,
0.46921999001), R = c(0.022186087, 0.023768855, 0.023911029,
0.023935705, 0.023655335, 0.022402726)), .Names = c("fy",
"fu", "E", "eu", "imp_local", "imp_global", "teta1c", "k1c",
"k2_2L", "k2_3L", "k2_4L", "bmaxc", "dfactorc", "amaxc", "teta1s",
"k1s", "k2_ab1s", "k2_ab2s", "k2_ab3s", "bmaxab1", "bmaxab2",
"ab1", "ab2", "dfactors", "teta1f", "k1f", "k2f", "bmaxf", "dfactorf",
"k2b", "amaxb", "dfactorb", "roti", "rotmaxbp", "R"), row.names = c(7L,
8L, 20L, 23L, 28L, 29L), class = "data.frame")
Data has no equal rows or NaNs

Putting series summary of ugarchboot into a dataframe

I am looking at the ugarchboot function in rugarch but I am having trouble getting the Series (summary) into a dataframe.
library(rugarch)
data(dji30ret)
spec = ugarchspec(variance.model=list(model="gjrGARCH", garchOrder=c(1,1)),
mean.model=list(armaOrder=c(1,1), arfima=FALSE, include.mean=TRUE,
archm = FALSE, archpow = 1), distribution.model="std")
ctrl = list(tol = 1e-7, delta = 1e-9)
fit = ugarchfit(data=dji30ret[, "BA", drop = FALSE], out.sample = 0,
spec = spec, solver = "solnp", solver.control = ctrl,
fit.control = list(scale = 1))
bootpred = ugarchboot(fit, method = "Partial", n.ahead = 120, n.bootpred = 2000)
bootpred
as.data.frame(bootpred, which = "sigma", type = "q", qtile = c(0.01, 0.05))
##I am tring to get this into a dataframe:
Series (summary):
min q.25 mean q.75 max forecast
t+1 -0.24531 -0.016272 0.000143 0.018591 0.16263 0.000743
t+2 -0.24608 -0.018006 -0.000290 0.017816 0.16160 0.000232
t+3 -0.24333 -0.017131 0.001007 0.017884 0.31861 0.000413
t+4 -0.26126 -0.018643 -0.000618 0.017320 0.34078 0.000349
t+5 -0.19406 -0.018545 -0.000453 0.016690 0.33356 0.000372
t+6 -0.23864 -0.017268 -0.000113 0.016001 0.18233 0.000364
t+7 -0.27024 -0.018031 -0.000514 0.017852 0.18436 0.000367
t+8 -0.13926 -0.016676 0.000539 0.017904 0.16271 0.000366
t+9 -0.32941 -0.017221 -0.000194 0.016718 0.13894 0.000366
t+10 -0.19013 -0.015845 0.001095 0.017064 0.14498 0.000366
Thank you for your help.

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