CDF is not starting from zero - r

I have a data without any infinite value like below:
data<-c(6.87, 0.11, 0, 0.03, 0.08, 0, 0.01, 0, 0, 0.13, 0.17, 0, 0.53,
0.01, 2.69, 0, 0, 0, 0, 0, 0, 5.44, 4.71, 3.57, 0.38, 0.31, 0.45,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0.09, 0.43, 0, 0.23, 2.31, 0,
0.01, 0.96, 1.28, 0.07, 0, 0, 0, 0, 0.07, 0, 0, 0.03, 5.99, 0.08,
6.23, 2.95, 0.04, 0, 0.98, 0.03, 17.2, 0, 0, 0.25, 1.99, 0, 0,
0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 1.94, 1.06, 0.05, 0, 0, 0, 0,
0, 0.02, 0, 0.02, 0.05, 0.66, 0.57, 0.54, 0, 0.11, 0.75, 0.65,
0, 7.35, 0.22, 0, 1.97, 0, 0, 0, 0, 0, 0, 0, 3.78, 0.28, 0.06,
0.09, 0.03, 0.01, 0.14, 0.02, 0, 1.82, 0.04, 0, 0.01, 0, 0, 0,
9.52, 10.38, 29.09, 0.1, 0.4, 0, 0.97, 0.57, 0.33, 0.16, 40.19,
1.06, 5.02, 0.01, 0.79, 4.78, 0.44, 15.29, 7.26, 0.05, 0.01,
6.47, 37.66, 0, 0.4, 0.15, 1.42, 0, 0.07, 0.01, 0, 0, 0.01, 7.43,
0, 0.03, 0.02, 0.15, 0.09, 0.44, 1.45, 0.03, 0, 0.01, 0.01, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09, 0, 0.01, 0, 0, 0, 0, 0, 0, 0.04,
1.52, 0.01, 0, 0.27, 0.01, 1.11, 2.41, 0, 0, 0.15, 0, 0, 1.28,
0.02, 0.16, 0.17, 1.74, 3.75, 0.05, 0.18, 0.07, 0.39, 0, 0, 0,
0.45, 0.78, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0.05, 0, 0, 0, 0, 0.09,
0.17, 0, 0.27, 0.04, 0.02, 0.88, 1.23, 0.01, 0.01, 0.13, 0.6,
0.02, 0.4, 0, 0, 0.09, 0, 0, 0, 0, 0, 0, 0, 0.12, 0, 0.02, 0.02,
0, 0.01, 0, 0, 0, 0, 0.05, 0, 0, 0, 2.71, 0.49, 0, 0, 0, 0.84,
0.04, 0, 0, 0.02, 0.06, 0, 0, 0.02, 0.15, 0, 0.48, 5.34, 5.23,
0.22, 2.37, 1.55, 3.29, 0, 0, 0.08, 0.03, 0.05, 0.06, 7.62, 0.84,
0.48, 0.04, 0, 0, 0, 0, 0.6, 0, 0.02, 0.08, 0, 0, 0, 0, 0, 0,
0, 0, 0.11, 0.1, 0, 0.01, 0, 0.24, 0, 2.4, 1.57, 0.12, 0.03,
0, 0, 0, 0, 0.08, 0.56, 0, 0.09, 0.01, 0.09, 0.01, 0, 0, 0, 0,
0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0.03, 0, 0, 0, 0.38, 0.01, 0.03,
0.14, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0.08, 0, 6.1, 0, 0, 0, 0.31,
0.01, 0.03, 0.2, 0.03, 0.02, 0.08, 0.12, 0, 5.79, 0.91, 0.95,
0, 0.39, 1.3, 0, 0, 0, 0, 0.01, 0, 0.64, 0.02, 0, 1.14, 0.24,
0, 0, 0.04, 0, 0, 0, 1.28, 0, 0.01, 0.02, 2.18, 0.05, 0, 0, 0,
0, 0, 0.01, 0.02, 0.19, 3.9, 0.01, 0.03, 0, 5.93, 0.88, 0, 0,
0, 0, 0.07, 0, 4.06, 0.41, 0.04, 0, 0, 0.08, 2.21, 0.03, 0, 8.07,
0, 0, 0.66, 1.73, 0.17, 0.02, 1.37, 4.68, 0.26, 0.09, 0.2, 0.03,
0, 0, 0, 0, 2.74, 0.1, 0.42, 0.65, 0.53, 0, 0, 0, 0, 0, 0.01,
0.16, 0.17, 0, 0, 0.01, 0, 0, 0, 0.6, 0, 0, 0.09, 0.01, 0, 0,
0.69, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0.13, 0.54, 0, 0.16, 1.57,
0.12, 0, 0, 0, 0, 0.15, 0.18, 0, 0.86, 0.01, 0.01, 0, 0, 0, 0.01,
0, 0, 0, 0, 0, 0, 0, 0.03, 0.37, 0.18, 0, 0, 0, 0.08, 5.25, 1.1,
0.68, 0.14, 0.01, 0, 0, 0, 0, 0, 0, 0.02, 0, 0.04, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 0.28, 0, 0, 0, 0, 0, 5.78,
3.05, 0.39, 1.65, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.25, 0.78, 0, 0, 0, 0.85, 0, 0, 0, 0, 0, 0, 0, 0.34, 0.45, 0,
0, 0.02, 0.02, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7,
0.69, 0, 0, 0, 0, 0, 0.02, 2.01, 0.05, 0, 0.77, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.01, 0.01, 0.02, 0.01, 0.79, 0.01, 0, 0.97, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.69, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.47, 0.02, 0, 0, 0, 0.13, 0.01, 0, 0, 0, 0, 0.33,
0.01, 0, 0, 0, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.01, 0, 0, 0.02, 0, 0.01, 12.31, 7.54, 0, 0,
0, 0.05, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 1.02, 0,
0, 0.54, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0.01, 0.08, 0.03,
0, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.11, 0.25, 0.03, 0, 0, 0.09, 0, 0, 0.01,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.15, 1.41, 0, 0, 0, 0, 0)
When I try to plot CDF, curve is not starting from zero. It starts with 0.57. I read that infinite and zero values can cause this problem. To overcome this I replaced zeros with very small values.
data[data==0]<-0.000001
plot(ecdf(data), xlim = c(min(data),max(data)))
But still I am getting same result. Why is that happening?


I'm not sure it makes much sense to show empirical cdf for values outside of observed data. All values that are lower than the minimum of data will have ecdf of zero. You could try something like:
f = ecdf(data)
curve(f, min(data) - 0.01, max(data))


Like it is said in comments to the question 1 and 2, there are many zeros in your data, representing 57% of the total data points.
mean(data == 0)
#[1] 0.5794045
If you plot the ECDF and an horizontal line at that ordinate, you will see that the ECDF starts there.
plot(ecdf(data))
abline(h = mean(data == 0))

Related

Predictive models with different sized time series

I'm trying to estimate an ARIMA, VAR and Neural Network models in R, to compare the quality of their predictions regarding unemployment, using the data regarding several economic indicators and COVID-19 related information which i would like to use as exogenous information. I would also like these models to use the rolling window approach, if possible.
My economic information extends from 2007 to 2021 (monthly) while the COVID data only comes from 2020, which obviously poses problems regarding the size of the time series.
Since i am trying to use rolling windows, every time I try to include COVID information i get the following error.
Error in solve.default(Sigma) :
system is computationally singular: reciprocal condition number = 2.6396e-19
Which I assume is because the covid information is all 0's (since we are using data from 2007) and therefore the model assumes a perfect correlation for that information.
Is there any way I can combine these 2 time series (the economic one from 2007 and the covid one) in the models mentioned, while using the rolling window approach?
dput version
structure(list(date = c("02/2007", "03/2007", "04/2007", "05/2007",
"06/2007", "07/2007", "08/2007", "09/2007", "10/2007", "11/2007",
"12/2007", "01/2008", "02/2008", "03/2008", "04/2008", "05/2008",
"06/2008", "07/2008", "08/2008", "09/2008", "10/2008", "11/2008",
"12/2008", "01/2009", "02/2009", "03/2009", "04/2009", "05/2009",
"06/2009", "07/2009", "08/2009", "09/2009", "10/2009", "11/2009",
"12/2009", "01/2010", "02/2010", "03/2010", "04/2010", "05/2010",
"06/2010", "07/2010", "08/2010", "09/2010", "10/2010", "11/2010",
"12/2010", "01/2011", "02/2011", "03/2011", "04/2011", "05/2011",
"06/2011", "07/2011", "08/2011", "09/2011", "10/2011", "11/2011",
"12/2011", "01/2012", "02/2012", "03/2012", "04/2012", "05/2012",
"06/2012", "07/2012", "08/2012", "09/2012", "10/2012", "11/2012",
"12/2012", "01/2013", "02/2013", "03/2013", "04/2013", "05/2013",
"06/2013", "07/2013", "08/2013", "09/2013", "10/2013", "11/2013",
"12/2013", "01/2014", "02/2014", "03/2014", "04/2014", "05/2014",
"06/2014", "07/2014", "08/2014", "09/2014", "10/2014", "11/2014",
"12/2014", "01/2015", "02/2015", "03/2015", "04/2015", "05/2015",
"06/2015", "07/2015", "08/2015", "09/2015", "10/2015", "11/2015",
"12/2015", "01/2016", "02/2016", "03/2016", "04/2016", "05/2016",
"06/2016", "07/2016", "08/2016", "09/2016", "10/2016", "11/2016",
"12/2016", "01/2017", "02/2017", "03/2017", "04/2017", "05/2017",
"06/2017", "07/2017", "08/2017", "09/2017", "10/2017", "11/2017",
"12/2017", "01/2018", "02/2018", "03/2018", "04/2018", "05/2018",
"06/2018", "07/2018", "08/2018", "09/2018", "10/2018", "11/2018",
"12/2018", "01/2019", "02/2019", "03/2019", "04/2019", "05/2019",
"06/2019", "07/2019", "08/2019", "09/2019", "10/2019", "11/2019",
"12/2019", "01/2020", "02/2020", "03/2020", "04/2020", "05/2020",
"06/2020", "07/2020", "08/2020", "09/2020", "10/2020", "11/2020",
"12/2020", "01/2021", "02/2021", "03/2021", "04/2021", "05/2021",
"06/2021", "07/2021", "08/2021", "09/2021", "10/2021", "11/2021"
), unemp = c(2.25752508, -1.0989011, 2.1978022, -2.1978022, -1.0989011,
-0.05972289, -1.08695652, -6.43115942, -1.00945017, 0, -1.07388316,
-3.04982818, -1.96717862, 1.96717862, -1.87441425, 2.90534208,
3.06056701, -2.02963918, 3.07130584, 2.08333333, 0, 0, 5.43478261,
5.6763285, 3.49563046, 2.05992509, 3.33333333, 0.87912088, 0.62625546,
4.58158018, -0.55504163, 1.06382979, 0, -1.06382979, -2.85150252,
-4.68041237, 0.62745098, -3.38935574, 0.5026455, -0.28619529,
-1.29525342, -2.95239548, -1.03740503, -1.76613018, -2.5210084,
2.59222333, -1.7518872, 1.7518872, -2.6894523, 1.65289256, -3.43801653,
-0.8503937, -2.3744125, 0.7751938, 2.29576625, 3.90578414, 4.94647471,
1.3640873, 4.04773622, -0.92273622, 3.125, 0.45673077, 3.84615385,
-0.95126248, 1.72049325, 0.56958309, 0.71246819, -3.64508393,
0.92207924, -2.02585375, -1.25114155, 0.66666667, -3.8008658,
-2.882786, -4.77313832, 0, -2.51458002, -1.30135713, -7.81499203,
-1.75438596, -3.87759171, -2.16419816, -3.70910671, -2.12494688,
0.46748831, -0.63842848, 0.38701623, -1.12359551, 1.43499197,
-1.14285714, -0.07142857, 0.31385281, 1.64714631, 0.44583317,
2.8365708, 2.38646704, -1.2987013, -1.44395079, -1.39857093,
-1.51117827, -0.67114094, 1.15965849, 3.67636416, 2.73483341,
-1.37718397, -0.78108941, -1.36176773, -2.05167173, 1.97459054,
1.16173728, -0.21885522, 0.47348485, -0.86122047, -0.78740157,
-0.78740157, -1.36727095, -1.66545543, 0.55803571, -3.31428571,
1.3398374, -3.09467611, 0.50237969, 0.36506406, 0.38394668, 0.55958863,
-1.04021115, -1.05878635, -1.78571429, 0.327654, -2.97315664,
2.71485077, -0.97087379, -0.66990291, -0.23232323, -1.50994481,
0.53174173, -1.32138858, 1.61887142, 2.80193237, -1.61616162,
1.79704017, 3.85706183, -0.21077989, 1.03395062, 1.96428571,
1.12781955, 4.93871665, 1.27473364, 2.65873016, -1.42857143,
-2.73641851, 6.98011599, -1.47058824, 2.94117647, 1.49253731,
1.49253731, -2.98507463, -4.54545455, 0.06784261, -5.97014925,
22.3880597, 10.78697422, 3.42657343, -6.75086108, -4.25146992,
-9.26916221, -4.41176471, -0.02131287, 3.09617918, 0.21645022,
4.61309524, 7.29166667, -26, -9.18518519, -4.65221319, 3.17073171,
4.21052632, 3.28947368), regist_un = c(-7.6, -8.1, -10.4, -13,
-12.2, -10.8, -10.2, -11.3, -12, -13.2, -13.8, -12.7, -11.6,
-11.4, -8.2, -3.6, -1.6, -2, -0.5, -0.7, 0.5, 2.9, 6.6, 12.1,
17.7, 23.8, 27.3, 27.6, 28.1, 30.1, 28.7, 29.1, 29.1, 28.2, 26.1,
25.1, 19.6, 18.1, 16.1, 14.6, 12.7, 10.3, 9.6, 8.9, 6.4, 4.4,
3.3, -0.5, -1, -3.5, -5, -5.4, -6, -4.4, -3, -0.3, 3, 6.7, 11.7,
14.4, 16.6, 19.8, 21, 20.8, 24.5, 25, 26.3, 23.4, 22.5, 19.6,
17.4, 16.05866431, 14.1343296, 11.04394749, 11.07092871, 9.66638699,
6.80821419, 4.99845882, 3.21403698, 2.00992748, -0.01381295,
-0.82689753, -2.83078075, -4.69352568, -5.22666645, -6.07571945,
-8.30308904, -9.4986526, -10.86351863, -11.10348947, -10.1911332,
-11.56954866, -12.86335954, -13.57419377, -13.31634168, -12.71367749,
-13.78692468, -14.3833581, -14.16732657, -12.93820022, -12.73630773,
-12.91458502, -14.04113868, -12.63480706, -10.48461147, -7.99771938,
-7.25281958, -7.35380587, -4.68547808, -2.62950703, -1.82217091,
-3.44938365, -4.66108643, -6.57689723, -7.04795734, -8.83698741,
-9.49043411, -11.59763744, -13.07912754, -13.26308777, -15.34204052,
-18.01521541, -19.89096413, -19.19477791, -18.26531051, -16.35403878,
-16.14554408, -16.34837215, -17.53504461, -16.81810893, -16.32660251,
-16.00691286, -17.02626382, -16.57334233, -16.61939724, -18.99258341,
-20.51560419, -20.5844694, -19.149043, -17.49773014, -17.38241663,
-17.23274637, -16.0328503, -15.58626266, -15.29940386, -15.14205448,
-14.5670108, -12.85161091, -10.29016682, -10.0720839, -10.00068018,
-11.10920973, -10.23871997, -8.6402685, -8.42184435, -8.61357235,
-7.91941687, 2.99152725, 22.12769269, 34.00159255, 36.37735545,
37.00494467, 34.50234942, 36.14288275, 34.50948107, 30.17574135,
29.55791318, 32.38134753, 36.84886013, 25.91626159, 8.04566645,
-1.65087765, -7.08027492, -9.47650638, -9.99850976, -12.4400864,
-12.85751101, -13.15709526), electr_cons = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1.52573055, -13.77356375, -2.91314706, -8.07503923,
5.64741668, 2.14652942, 8.96942648, -12.90473041, 6.7462353,
-1.99320589, 3.5519951, 3.96085785, 2.90812218, -3.01852589,
-0.72990857, 6.62024931, -4.03278316, 15.15581887, -5.80240675,
6.15355971, -8.9434497, 7.09174981, -3.03231017, 5.99309871,
0.12505412, 7.98920263, 4.97957359, -13.41437565, 3.1251824,
-19.61649962, 8.42404224, 3.9274678, -0.69310101, -6.20882422,
2.92246113, 0.84784002, -5.54698038, -0.34784001, -2.23860664,
2.52721219, 1.51556136, 0.11030442, -6.11617537, -0.35384086,
3.73968268, 1.36305933, -4.2866283, -3.01881185, 8.07963944,
5.51292808, -4.35131302, 3.6602547, -6.44713838, 1.39859892,
1.30415104, -1.60930464, -0.40894902, -0.56603623, 3.40891108,
-1.80100326, -5.44803444, -6.44187786, 38.45132631, -4.07719031,
0.61965483, -1.49863173, 3.30109363, 1.19224911, -2.92700391,
16.92568044, -15.15157455, 3.48316945, -1.00483719, -40.57315028,
9.7659042, 4.78461676, 0.28264713, -0.87145969, -3.86558313,
-0.0679219, 2.45947602, -9.41849532, 8.07657479, 3.93419348,
1.94861355, 52.56477551, -54.55955271, -3.99890956, 4.60669488,
1.67468875, 0.32376412, -0.80237405, -4.03956352, 1.8608071,
-0.74354869, -6.79369666, -2.74137491, 5.6219074, 5.13307485,
2.09278167, -8.97792005, 0.23895157, 2.34350504, 2.01565156,
0.34461909, -5.39315134, 3.70375643, 1.35911336, 0.14440375,
-10.59505494, 2.44997231, -3.43575023, 8.45149168, 1.82580942,
-5.09746181, 0.54849722, -0.40588822, 2.90913923, -2.59092777,
2.87179578, 3.5472478, 5.50635214, 3.45884957, 0.5574311, -9.17310051,
-2.3069345, 1.01792404, 3.39829408, 2.18975374, -3.29888986,
3.36520805, -6.06857013, 3.69912561, -5.88831257, -5.59447201,
6.93463825, 5.18518235, -4.45047426, 3.48886956, -4.22410882,
0.36229398, 4.38850288, 2.44283293, -4.13337598, 0.07853854,
0.33111425, -1.50291612, -10.73449339, -2.31367763, 6.06925576,
7.25078064, -1.2508886, 0.69265168, -1.6952393, -5.45510355,
10.36136532, 0.81292421, -5.78213732, 0.29608406, 8.58995306,
3.33976066, -16.05532167, 4.59091993, 2.96362702, -1.21232785,
0.44224015, 4.78743418), inflation = c(-0.3, 0.1, 0.4, -0.4,
0, -0.1, -0.4, 0.1, 0.5, 0.3, -0.1, 0.2, 0, 0.3, -0.7, 0.3, 0.6,
-0.3, 0, 0.1, -0.7, -1.1, -0.6, -0.7, 0, -0.7, 0.1, -0.8, -0.3,
0.1, 0.3, -0.6, 0.2, 0.8, 0.7, 0.2, 0.1, 0.4, 0.1, 0.4, 0, 0.8,
0.1, 0, 0.3, -0.1, 0.2, 1.2, -0.1, 0.4, 0.1, -0.3, -0.4, -0.3,
-0.2, 0.7, 0.5, -0.2, -0.3, -0.2, 0.2, -0.4, -0.2, -0.2, 0, 0.1,
0.3, -0.2, -0.8, -0.2, 0.2, -1.7, -0.2, 0.5, -0.3, 0.5, 0.3,
-0.4, -0.6, 0.1, -0.3, 0.1, 0.1, -0.1, -0.2, -0.3, 0.3, -0.2,
0.1, -0.5, 0.6, 0.1, 0.1, 0, -0.4, -0.1, 0.3, 0.5, 0.1, 0.5,
-0.2, -0.1, 0, 0.2, -0.2, -0.1, -0.3, 0.4, -0.5, 0.3, 0, -0.1,
0.3, 0, 0.1, -0.1, 0.4, -0.6, 0.4, 0.4, 0.3, -0.2, 1, -0.7, -0.7,
0, 0.3, 0.3, 0.3, -0.1, -0.2, -0.5, -0.4, 0.1, -0.5, 1.1, 0.6,
0.2, -0.9, 0.5, -1, 0.1, -0.3, 0, 0.3, -0.1, 0.1, -0.6, 0.4,
-1.4, 0.6, -0.2, 0.2, 0.3, 0.2, 0.4, -0.3, -0.4, -0.2, -0.5,
0.8, -0.3, -0.1, -0.6, 0.2, 0.2, 0.1, 0.5, 0.1, -0.2, -0.2, 0.6,
-1.1, 1.7, 0.2, 0, 0.5, 0.8), indust_prod = c(-2.572274344, -2.787547763,
2.430571433, -3.345301558, -0.463876872, -4.282068938, -1.120792803,
1.572826809, 5.587675149, -4.689662112, -4.039000653, 6.048760143,
3.054565546, -8.324475243, 8.938629302, -8.359749373, 1.285898904,
2.787686994, -1.18689554, -1.044345864, -5.377152698, 0.417765217,
-2.016437912, -6.076899004, 0.004832982, 6.553009253, -3.861674081,
2.856277, -0.305740286, 3.884000992, 0.228026533, 3.160838283,
0.718539054, -0.177278754, 5.861419366, 1.635627332, -0.233559319,
1.656666214, -3.416861953, 3.08593964, 0.219887065, -5.252665393,
2.67414747, -3.078208264, -1.786603334, 5.055713134, -0.487827087,
-1.085759889, 1.558281917, 1.522276844, -0.649720104, -2.25624012,
-3.000400858, 1.287503915, 2.146782202, -2.414634004, -0.710817622,
-2.037943864, -3.422580841, 2.574492816, -0.989720019, -3.140953611,
-2.00903906, 5.044475953, 2.421105335, 1.312884435, -0.423167539,
-1.618876184, 0.749480391, -0.067574147, 0.665361763, 2.736011629,
-0.984538393, 2.734504118, 3.689697632, -4.285546756, -1.453623444,
-3.036406761, 0.371745852, 5.268465577, 2.014992273, 0.230769423,
1.479245498, -0.601620521, 0.435743581, -6.919825179, 3.973589167,
-1.18054296, 1.009209474, 3.4645508, -4.268359859, -1.38055936,
1.398352479, -1.380441077, -0.945043656, -3.070867046, 2.286824653,
4.15932548, -0.885666142, 1.66749454, 1.079797346, 1.268988373,
-3.757792572, 3.297688911, 0.757515843, -3.344176468, 0.937671829,
1.942358369, -0.363516768, -2.258151209, 1.602633167, -3.43640148,
1.319433782, -3.471816444, 6.680682523, -3.215319966, -4.672113236,
6.216354428, 0.969772263, -0.213391145, -3.34142316, 5.648021789,
-8.289444236, 8.300362011, -2.663437754, 3.039228136, 3.239437295,
-6.66030595, 1.185659472, -1.499248124, -3.292614657, 2.681309878,
-0.579981109, 0.867209022, 2.022041722, -6.479958297, 1.680604654,
-0.873272771, -2.875794511, 3.56198993, 0.706252639, -3.546626564,
1.706203079, -1.238940266, 0.694831062, -4.576557295, 5.589573409,
1.887484409, -6.283451003, 3.968287705, -4.024275523, -0.142379113,
3.389769739, 5.34662785, -1.707063421, -1.962359729, 1.648794981,
-5.778920692, -8.801594747, 6.391085381, 3.735161238, 0.512586559,
1.347387952, 0.083401305, -0.958210945, 0.28448621, -0.398402291,
0.174892606, -2.923332835, 9.40208252, 8.440250585, -5.118029161,
-4.652533609, -1.031496063, 0.253718285, 0.116959064, -0.767472053,
-2.656293514), construction = c(-5.97969161, -4.79269684, -5.74581431,
-5.80595875, -3.91928599, -4.68384075, 2.11180124, -2.08333333,
-2.12510024, -2.80786201, -2.94938222, -2.5189924, -3.04, -3.39592489,
-2.50302786, -4.54176805, -5.77544426, -5.52825553, 2.83860503,
-6.58756137, -7.37402704, -6.9748246, -9.32238193, -11.64889253,
-10.35478548, -10.21505376, -11.09730849, -8.41121495, -8.65837977,
-11.66016472, -15.14195584, -9.46123522, -8.97832817, -9.40550133,
-10.00905797, -8.58867224, -10.86056144, -11.60755412, -10.75919888,
-11.03896104, -12.57625528, -8.83218842, -15.24163569, -12.48185776,
-12.97376093, -13.36924584, -11.67589331, -11.93499238, -10.22199277,
-11.04742053, -10.96033403, -13.3472367, -11.27214171, -12.86329386,
-14.03508772, -13.0458817, -14.40536013, -15.65856416, -14.52991453,
-13.49480969, -15.12363427, -15.58289397, -18.22977726, -17.74969916,
-16.57592257, -16.86226065, -14.66836735, -16.52892562, -16.1121983,
-15.8847185, -16.46666667, -19.2, -19.85094851, -17.4184594,
-16.27240143, -14.55742502, -15.88107324, -15.15601783, -15.8445441,
-16.22239147, -15.08553655, -13.14741036, -14.44533121, -14.68646865,
-11.3271344, -14.45378151, -9.93150685, -11.81506849, -10.94827586,
-10.07005254, -8.25932504, -6.18181818, -6.04395604, -6.05504587,
-3.91791045, -0.87040619, -4.09914204, 0.09823183, -3.8973384,
-2.23300971, -2.80735721, -2.72638754, -1.839303, -3.87596899,
-3.89863548, -4.4921875, -6.69902913, -5.75609756, -3.77733598,
-5.69185476, -3.85756677, -4.27010924, -5.37848606, -3.5035035,
-6.01577909, -3.83064516, -2.63691684, -2.04498978, 0.52029136,
3.10559006, 2.58264463, 1.144641, -0.30864198, 0.20746888, 2.31578947,
1.34854772, 2.51836306, 2.72536688, 1.66666667, 1.67014614, 3.51966874,
2.10843373, 1.91339376, 3.08641975, 3.81836945, 4.24430642, 3.90946502,
3.48004094, 4.81064483, 4.18367347, 4.4057377, 3.28542094, 1.7,
1.08161259, 1.97628458, 3.79241517, 5.96421471, 3.17775571, 2.27722772,
3.26409496, 2.44140625, 1.76297747, 2.15897939, 8.41012196, -0.16302847,
0.76178712, -4.69372663, -17.51199295, -45.16320079, -36.28914654,
-16.06074568, -4.87295907, 7.5668007, 3.72748757, 7.02862225,
3.38149083, 1.75109309, 4.74865906, 14.84745984, 34.803423, 100.52114863,
67.60757235, 30.44713435, 18.17227737, 11.63694169, 9.69843425,
4.74314833, -4.52314658), exports = c(24.96318942, 23.36969334,
22.92995604, 10.65726657, 16.4160009, 15.06347523, 7.05135338,
15.02299212, 19.0326442, 18.98558329, 3.33296617, 23.07105097,
24.1601689, 19.72166595, 27.16602809, 24.3702412, 14.56065717,
23.81003502, 12.51014556, 1.73165902, -13.84780944, -22.58391171,
-20.80753018, -33.06565254, -36.85899284, -37.79190796, -39.34913459,
-33.38661304, -27.85334908, -28.37492746, -23.27801369, -13.6431818,
6.74411365, 11.84361899, 13.51797098, 20.8751474, 22.95402278,
28.37464897, 21.54264152, 8.61516289, 3.13570326, 4.20220684,
8.75273789, 5.92066231, 6.95366577, 4.26020533, 11.40501619,
9.87457947, 21.13174127, 16.42066897, 31.89439842, 33.06799105,
34.07732157, 29.29939609, 27.02846164, 20.81549686, 14.50583432,
16.11199422, 5.25642553, 5.67979421, 5.03884106, 5.91383786,
-6.92432553, -4.54670446, -5.43453018, -11.06914116, -4.30401515,
-5.89475834, -6.20354881, -5.22134237, -1.09985899, 9.43369798,
1.66688812, 1.20138491, 6.90689277, 7.75020233, 10.13668917,
11.30827244, 9.29930475, 10.9321974, 9.07936123, 15.05633842,
13.19202501, 5.4705525, 6.82591085, 2.44716859, 4.11748271, 5.06863218,
6.22577349, 6.12185947, 0.58671313, -3.10270985, 1.18732102,
-8.2784308, -6.9219884, -15.34433626, -14.14267264, -15.499663,
-17.52534249, -13.08561877, -13.30851885, -14.85330263, -14.60224224,
-11.87824448, -10.99332931, -13.12023756, -13.1469306, -6.67165565,
-4.86353483, -1.18866693, 5.19617018, -5.56720735, -1.31601522,
2.27149134, -0.32052319, 6.34015708, -2.21439899, 8.54617508,
7.90296929, 8.97464479, 8.46001663, 14.8780712, 0.80639894, 12.94240637,
3.50598117, 9.14630966, 19.69171985, 15.802644, 16.51140186,
21.66597738, 20.28010486, 25.27245738, 22.10541531, 15.94575338,
26.38566548, 13.20690115, 17.00548484, 12.09162042, 0.06482127,
2.03901271, 0.44242727, -9.82586651, 0.27668163, -2.78174059,
-2.82866966, -3.82970378, -5.71082564, 2.43947926, -5.13803015,
-5.57470758, -5.42985341, -2.92568853, 3.29088281, 0.70260049,
-5.31805275, -3.60733326, -1.34953732, -13.82572968, -43.79388438,
-37.96950775, -25.84963925, -17.45358624, 1.37712882, -6.52424698,
-1.90571756, -7.56995756, 4.05542391, -3.88890971, 0.22947427,
18.0580403, 83.71922617, 69.97805207, 37.59500165, 29.96692408,
15.45334076, 19.1358433, 18.69987106, 1.361784), new_licences = c(-8.68185938,
-0.02872738, -4.63917526, -1.28723796, 6.61294804, 22.56276492,
0.7781103, 4.79877409, 10.14401124, 8.24945593, 12.81146637,
9.55541727, 22.9280397, 5.57950192, 13.32309582, 5.15754737,
-8.87412041, -4.33625964, 4.81079247, 6.37217177, -6.40984757,
-3.33605579, 37.89418817, -43.23130325, -42.15352673, -42.12746655,
-33.9205377, -33.62858734, -23.67129034, -20.50889878, -14.90326237,
-12.50180871, 3.46190541, 1.31288184, -17.85106785, 61.84546971,
53.12063809, 86.83179182, 33.04076778, 46.80088462, 62.54917879,
18.35461489, 13.50837139, 15.08185877, 1.04729285, 19.36104696,
61.72782699, -9.18395384, -12.57976299, -20.58231247, -11.10426044,
-23.56363636, -34.11195205, -29.70589684, -31.85551458, -33.86262394,
-40.44064924, -48.64559819, -59.99715485, -47.36404205, -48.59228363,
-49.05969361, -41.7394923, -27.49762131, -37.00874636, -35.13210456,
-33.07096298, -30.93970668, -19.15289482, -25.34798535, -43.46550498,
0.89093261, 8.91046074, -0.19703412, -4.26190476, -5.38995126,
17.88392113, 17.14563526, 12.84454245, 15.91945886, 23.0810884,
23.51044441, 35.77606542, 31.90428714, 40.30863376, 47.04904406,
53.12111415, 36.55008422, 23.55712603, 30.67416767, 26.05438854,
31.72750712, 29.72943247, 33.01929625, 37.87352328, 27.84796458,
35.64994785, 41.8951385, 21.94250447, 33.10840226, 33.91801716,
9.71134166, 21.90931404, 30.04017719, 16.31199661, 14.13943169,
10.39986559, 17.71959459, 25.98727895, 31.75638663, 6.44645711,
13.57863178, 10.93394077, 0.59826311, 12.84306453, 10.63138715,
8.87090896, 23.54216507, 29.26495206, 7.82034725, 4.63800277,
-1.80663693, 17.80530531, 13.51986946, 6.23716632, 12.3673104,
12.0950324, 6.38739706, 6.44081414, 6.66263238, 0.41205557, -2.8280543,
10.1373204, 7.07467283, 13.80775358, -0.3255401, 5.34750745,
13.59549283, 28.0137388, -14.04051962, -12.215373, -12.25462385,
-5.28197913, 7.40258851, -9.21869735, -10.48961104, -1.44190387,
-3.61384459, -3.27574344, -7.6398978, -18.62443557, 13.99263957,
12.13098309, 6.03905341, 9.71096119, -8.04004081, 7.45041892,
-57.44578313, -86.98451778, -74.73596198, -56.22999407, -17.50379692,
-0.14475271, -9.42437148, -12.58866381, -27.8902439, -19.59833014,
-30.46522915, -58.98435572, 19.84711212, 438.70498363, 190.21076468,
70.96424702, -18.97560655, -35.80575018, -18.2011224, -22.68440675,
-7.59343819), investm = c(9.59206261, 10.09104451, 8.49259555,
11.05912367, 11.33035356, 10.69893187, 9.27221449, 6.36319052,
5.1145808, 3.14675108, -2.0583252, -5.9801462, -8.54458193, -11.85690298,
-12.73704068, -15.30334665, -16.51488054, -18.55445193, -18.78407228,
-20.33141371, -23.20479413, -23.70413199, -25.82349247, -23.49151634,
-23.20331483, -21.76656318, -19.69767216, -16.1163604, -15.56801204,
-10.63893647, -6.90914634, -1.33860382, 5.00317034, 9.14637266,
13.66111398, 14.90618555, 16.27492141, 18.9726911, 17.81129998,
11.93953654, 9.97602683, 6.72450506, 3.35538125, 1.22492917,
-0.25529345, -3.58030117, -5.66383325, -5.99451638, -5.59778107,
-8.22207902, -8.75620416, -6.82634766, -7.76850362, -8.81317285,
-10.30877298, -12.31016477, -12.44698993, -11.49249967, -9.6953666,
-10.58305192, -9.65103885, -8.93856304, -8.31371275, -8.81656192,
-7.08877565, -5.92517826, -3.13301918, -0.90715848, 0.17047088,
0.71130641, 3.09817756, 6.3772002, 6.05805695, 4.99307084, 4.93907049,
7.50839619, 8.21051614, 7.46196337, 6.59668824, 6.44846351, 4.4735673,
3.33064046, -0.20557392, -0.17865147, -0.07705874, 1.55447287,
0.59002986, -0.47146863, 0.62804213, -2.08346753, -2.73661788,
-3.90829617, -3.78220665, -4.01170424, -5.17638734, -6.16018899,
-5.69641901, -6.49065187, -5.75373139, -6.16938142, -10.39770764,
-7.95466132, -7.83823783, -8.7925605, -7.90867493, -5.97676105,
-5.228471, -6.84892555, -8.8243345, -9.50405058, -10.14943257,
-10.02532808, -8.25385595, -7.41211911, -6.72331539, -4.65132561,
-3.88085377, -5.35115101, -3.6992884, 3.41808435, 5.01032011,
6.4338955, 7.44842984, 7.96282188, 10.42036183, 9.85846999, 10.3384518,
12.3886042, 13.55244814, 14.4281047, 12.93327424, 7.14080025,
6.37943542, 4.68820186, 5.38710032, 4.61464096, 3.14205599, 2.9423131,
2.15746296, 0.09387282, -2.38577599, -2.97124266, -4.32001028,
-4.33596506, -3.36175388, -1.65514585, -1.1147413, -1.1116245,
0.03453284, 1.51801102, 1.97354903, 3.29586522, 4.83238917, 5.70220137,
7.46051222, 7.48040345, 6.30281751, 0.14703508, -0.15754408,
0.73361141, 0.474155, 0.46358707, 1.64385862, 1.15369522, 0.67754708,
4.63841367, 6.78461903, 8.58994132, 10.23754398, 17.32260776,
19.2077649, 20.72874275, 21.76681016, 22.19165981, 23.17276241,
23.11683188, 23.85460849, 20.34415852), household_consupt = c(0,
0, 1.359168191, 0, 0, -0.342789739, 0, 0, 1.790564565, 0, 0,
-0.482967554, 0, 0, -1.762057837, 0, 0, -0.536837331, 0, 0, -4.302408991,
0, 0, -4.488751839, 0, 0, -1.381433473, 0, 0, 0.30585643, 0,
0, 3.227950235, 0, 0, 5.377633558, 0, 0, 1.854863297, 0, 0, -0.352069414,
0, 0, -0.701775487, 0, 0, -3.526502117, 0, 0, -2.191789397, 0,
0, -0.956817085, 0, 0, -2.394047662, 0, 0, 1.250192652, 0, 0,
-0.572344375, 0, 0, 0.753471833, 0, 0, 0.070143601, 0, 0, -0.298510806,
0, 0, 3.154164092, 0, 0, 0.883890768, 0, 0, 2.625083331, 0, 0,
0.256046586, 0, 0, -0.856863549, 0, 0, 0.590803335, 0, 0, -0.813925723,
0, 0, 0.125528578, 0, 0, 1.468506379, 0, 0, -0.670352866, 0,
0, -0.362744845, 0, 0, 1.32210668, 0, 0, -1.215018014, 0, 0,
0.331687133, 0, 0, 1.532560625, 0, 0, -0.533138522, 0, 0, -0.422657587,
0, 0, 0.037693685, 0, 0, -0.657257657, 0, 0, 0.489966584, 0,
0, 0.938809848, 0, 0, -0.343317849, 0, 0, 0.626641626, 0, 0,
0.067372918, 0, 0, -0.634514601, 0, 0, 0.004316699, 0, 0, -0.456951802,
0, 0, -3.945597519, 0, 0, -16.828812208, 0, 0, 12.869510817,
0, 0, -1.316414045, 0, 0, -1.582469509, 0, 0, 26.887239149, 0,
0, -14.346872459, 0, 0, 2.145073044, 0), covid_total_cases = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 236.490662, 29.76642, 29.664615,
21.193137, 13.588659, 30.217886, 87.020465, 110.973322, 38.789711,
74.173149, 11.664696, 2.132837, 1.797567, 1.506289, 3.587828,
10.127144, 7.154014, 3.020636, 1.99873, 5.189378), covid_total_vaccinations = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 964.859996,
163.568194, 94.43678, 87.634082, 73.551864, 47.399777, 42.470628,
20.985756, 8.109421, 1.48542, 7.961336)), class = "data.frame", row.names = c(NA,
-178L))
Code:
VAR_data_Total = data
f = function(data){
m1 = vars::VAR(data[,1:10], p=3, exogen = data[,11:12])
s1 = summary(m1)
return(s1$varresult$unemp$coefficients)
}
zoo::rollapply(VAR_data_Total, width = 41, FUN = f, by.column = FALSE)

Calculate the S1 sum for variance of Moran's I

I would like to calculate the S1 term for the variance of Moran's I. The following is the formula to calculate the S1
where w_{ij} is an element in the spatial weights matrix.
My spatial weights matrix is as following:-
structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.111111111111111, 0.1, 0.1, 0.166666666666667,
0, 0.1, 0.111111111111111, 0.125, 0.166666666666667, 0, 0, 0.1,
0.111111111111111, 0, 0, 0.166666666666667, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0.0909090909090909,
0, 0, 0.1, 0.1, 0.166666666666667, 0, 0.1, 0.111111111111111,
0.125, 0.166666666666667, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0.0909090909090909,
0, 0.111111111111111, 0, 0.1, 0, 0, 0.1, 0.111111111111111, 0.125,
0.166666666666667, 0, 0, 0.1, 0.111111111111111, 0, 0, 0.166666666666667,
0, 0, 0.0909090909090909, 0, 0.111111111111111, 0.1, 0, 0, 0.333333333333333,
0.1, 0.111111111111111, 0.125, 0, 0, 0, 0.1, 0.111111111111111,
0, 0, 0.166666666666667, 0, 0, 0.0909090909090909, 0, 0.111111111111111,
0, 0, 0, 0, 0.1, 0.111111111111111, 0, 0.166666666666667, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.111111111111111, 0, 0, 0.166666666666667, 0, 0, 0.0909090909090909,
0, 0.111111111111111, 0.1, 0.1, 0.166666666666667, 0, 0, 0.111111111111111,
0.125, 0.166666666666667, 0, 0, 0.1, 0.111111111111111, 0, 0,
0, 0, 0, 0.0909090909090909, 0, 0.111111111111111, 0.1, 0.1,
0.166666666666667, 0, 0.1, 0, 0.125, 0, 0, 0, 0.1, 0.111111111111111,
0, 0, 0, 0, 0, 0.0909090909090909, 0, 0.111111111111111, 0.1,
0.1, 0, 0, 0.1, 0.111111111111111, 0, 0, 0, 0, 0.1, 0.111111111111111,
0, 0, 0, 0, 0, 0.0909090909090909, 0, 0.111111111111111, 0.1,
0, 0.166666666666667, 0, 0.1, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0.0909090909090909, 0, 0.111111111111111, 0.1, 0.1, 0, 0, 0.1,
0.111111111111111, 0.125, 0.166666666666667, 0, 0, 0, 0.111111111111111,
0, 0, 0.166666666666667, 0, 0, 0.0909090909090909, 0, 0, 0.1,
0.1, 0, 0.333333333333333, 0.1, 0.111111111111111, 0.125, 0,
0, 0, 0.1, 0, 0, 0, 0.166666666666667, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.166666666666667,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0909090909090909,
0, 0, 0.1, 0.1, 0, 0.333333333333333, 0, 0, 0, 0, 0, 0, 0.1,
0.111111111111111, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(20L, 20L), .Dimnames = list(
c("AUS", "BEL", "CAN", "CHE", "DEU", "DNK", "ESP", "FIN",
"FRA", "GBR", "IRL", "ITA", "JPN", "KOR", "NLD", "NOR", "NZL",
"PRT", "SWE", "USA"), c("AUS", "BEL", "CAN", "CHE", "DEU",
"DNK", "ESP", "FIN", "FRA", "GBR", "IRL", "ITA", "JPN", "KOR",
"NLD", "NOR", "NZL", "PRT", "SWE", "USA")))
I can do this by using a for loop within a for loop, but I am thinking that there might be a more efficient way to do this. Maybe using lapply or something else.
Thanks in advance.

Define a squared sum function and use outer.
squared_sum <- function(x, y) (x + y)^2
sum(outer(W, W, squared_sum))/2
#[1] 3037.03

for me, the solution looks like this
set.seed(1)
m <- matrix(sample(c(0, 1), size = 16, replace = TRUE), nrow = 4)
diag(m) <- 0
m
#> [,1] [,2] [,3] [,4]
#> [1,] 0 1 1 0
#> [2,] 1 0 1 0
#> [3,] 0 0 0 0
#> [4,] 0 0 0 0
S1 <- 0.5 * sum((m + t(m))^2)
S1
#> [1] 6
Created on 2021-10-07 by the reprex package (v2.0.1)

Using expand.grid
squared_sum <- function(x, y) (x + y)^2
with(expand.grid(W, W) , sum(squared_sum(Var1, Var2)))/2
[1] 3037.03

Density Plot based on specific tolerance bands (normalized)

I'm having a huge data table with many different columns. Now I want to plot for some columns (EV, EH, ...) the densities based on a specific tolerance band (column pBu). In my sample below you can see that I'm having five different tolerance bands (0.00, 0.05, 0.10, 0.15 and 0.20), so for my density plot EV I would have five different densities in one plot. In addition, the y-axis should be normalized (range from 0 to 1).
How can I handle this with plotly?
Here my data structure:
structure(list(Year = c(2018L, 2018L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L), GPNRPlan = c(100236L, 101554L,
111328L, 124213L, 127434L, 128509L, 130058L, 130192L, 130224L,
130309L, 130342L, 130351L, 130392L, 130402L, 130843L, 132067L,
132597L, 133874L, 133943L, 133950L), TB.Info = c("Below TB",
"Over TB", "In TB", "In TB", "In TB", "Below TB", "Over TB",
"Below TB", "Below TB", "Below TB", "Over TB", "Over TB", "In TB",
"In TB", "In TB", "In TB", "In TB", "Over TB", "In TB", "Over TB"
), EV = c(-33498.61, 0, -31117.17, 70950.43, -37984.99, 0, 0,
-93820830.01, -7852622.13, -245712.8, 1800050, 320263371.77,
-803069.3, -731544.77, -45841.06, -19931.59, 62286.48, 14652139.32,
296249.92, 173414.12), EK = c(311746.83, 0, 408503.01, 965723.51,
447176.86, 0, 0, 237703401.51, 11650300.16, 761470.17, 15514898.49,
791067269.75, 35591131, 10754272.33, 9496742.11, 512370.9, 1134032.95,
35318984.4, 5630139.9, 1111511.07), EH = c(345245.44, 0, 439620.18,
894773.08, 485161.85, 0, 0, 331524231.52, 19502922.3, 1007182.97,
13714848.49, 470803897.97, 36394200.3, 11485817.1, 9542583.17,
532302.49, 1071746.46, 20666845.08, 5333889.99, 938096.94), Peff = c(104.78,
0, 91.52, 112.18, 113.39, 0, 0, 86.18, 101.04, 104.39, 106.23,
86.4, 96.19, 86.38, 113.5, 115.88, 104.61, 96.31, 95.6, 101.71
), Qeff = c(-0.01, 0, 0, 0, 0, 0, 0, 0, -0.01, -0.01, 0, 0, 0,
0, 0.01, 0, 0, 0, 0, 0), Meff = c(-7.35, 0, -2.16, -2.35, 1.9,
0, 0, 0.94, -8.28, -6.9, -0.57, 0, -0.63, -0.21, 10.89, -3.73,
-1.63, 0.69, 0.88, 3.29), G = c(104.7, 0, 91.51, 112.14, 113.46,
0, 0, 86.19, 100.99, 104.37, 106.2, 86.4, 96.19, 86.38, 113.67,
115.83, 104.61, 96.29, 95.61, 101.76), D = c(3262.07, 0, 4770.44,
7947.26, 4254.06, 0, 0, 3818668.12, 191836.45, 9612.18, 127995.67,
5426568.38, 377446.8, 132384.92, 83629.36, 4575.32, 10225.66,
212630.83, 55721.94, 9204.12), pP = c(24.86, 0, 28.34, 37.62,
30.53, 0, 0, 27.95, 28.54, 37.74, 26.96, 32.78, 37.09, 32.87,
29.64, 36.81, 39.14, 24.61, 40.53, 38.33), rP = c(1.05, 0, 0.64,
0.4, 0.65, Inf, Inf, 0.64, 0.63, 0.39, 0.92, 0.36, 0.23, 0.38,
0.6, 0.46, 0.2, 0.89, 0.12, 0.21), rQ = c(-0.11, Inf, -0.08,
0.07, -0.09, -0.04, 0.05, -0.29, -0.41, -0.25, 0.12, 0.66, -0.03,
-0.08, -0.02, -0.05, 0.05, 0.69, 0.04, 0.17), rE = c(0.91, 0,
0.6, 0.22, 0.52, Inf, Inf, 0.61, 0.59, 0.22, 0.76, 0.36, 0.21,
0.36, 0.54, 0.26, 0.15, 0.85, 0.1, 0.17), dEHdD = c(105.84, 0,
92.15, 112.59, 114.05, 0, 0, 86.82, 101.66, 104.78, 107.15, 86.76,
96.42, 86.76, 114.11, 116.34, 104.81, 97.2, 95.72, 101.92), dEHdzH = c(149.99,
0, 174.85, 250, 157.6, 0, 0, 140353.13, 6999.39, 299.99, 5499.95,
165545.1, 10399.12, 4079.6, 2999.81, 150, 274.98, 8998.95, 1399.89,
249.98), dEHdrP = c(3262.07, 0, 4770.44, 7947.26, 4254.06, 0,
0, 3818668.12, 191836.45, 9612.18, 127995.67, 5426568.38, 377446.8,
132384.92, 83629.36, 4575.32, 10225.66, 212630.83, 55721.94,
9204.12), dEKdzV = c(135, 0, 160.54, 267.07, 143.61, 0, 0, 133335.47,
6649.42, 284.99, 6049.94, 198654.12, 10058.17, 3772.52, 2951.88,
142.86, 287.99, 9448.9, 1461.51, 249.98), dEKdzTB = c(-1.13,
0, 0, 0, 0, 0, 0, -34015.93, -2514.12, -60.93, 109.72, 76326.42,
0, 0, 0, 0, 0, 5769.55, 0, 42.97), dEKdzH = c(135, 0, 160.54,
267.07, 143.61, 0, 0, 133335.47, 6649.42, 284.99, 6049.94, 198654.12,
10058.17, 3772.52, 2951.88, 142.86, 287.99, 9448.9, 1461.51,
249.98), dEKdrQ = c(348451.86, 0, 441620.17, 900981.67, 487701.61,
0, 0, 335596984.05, 19696287.61, 1017097.6, 13847581.34, 476478784,
36707738.02, 11575841.25, 9599815.68, 535717.91, 1080845.93,
20888792.33, 5386259.63, 948299.85), dEKdrP = c(3262.07, 0, 4770.44,
7947.26, 4254.06, 0, 0, 3818668.12, 191836.45, 9612.18, 127995.67,
5426568.38, 377446.8, 132384.92, 83629.36, 4575.32, 10225.66,
212630.83, 55721.94, 9204.12), dEKdrE = c(-24.56, 0, 0, 0, 0,
0, 0, -925490.96, -68906.02, -1952.36, 2553.36, 2501979.97, 0,
0, 0, 0, 0, 136325.23, 0, 1582.17), dEKdpP = c(133.87, 0, 160.54,
267.07, 143.61, 0, 0, 99319.54, 4135.3, 224.06, 6159.66, 274980.54,
10058.17, 3772.52, 2951.88, 142.86, 287.99, 15218.45, 1461.51,
292.95), dEKdD = c(95.57, 0, 85.63, 121.52, 105.12, 0, 0, 62.25,
60.73, 79.22, 121.21, 145.78, 94.29, 81.23, 113.56, 111.99, 110.9,
166.1, 101.04, 120.76), dEKdf = c(3327.91, 0, 4549.69, 10047.22,
4384.35, 0, 0, 2775981.18, 118021.52, 8456.02, 166064.35, 9013862.02,
373057.45, 124002.65, 87493.72, 5258.83, 11271.93, 374526.13,
59234.84, 11228.85), dEKdavgPh = c(-162.25, 0, -171.47, -328.66,
-170.41, 0, 0, -107505.38, -4532.49, -274.4, -7350.79, -287563.54,
-10693.86, -3995.68, -3297.26, -179.43, -314.73, -16536.87, -1543.22,
-317.73), dEVdzV = c(135, 0, 160.54, 267.07, 143.61, 0, 0, 133335.47,
6649.42, 284.99, 6049.94, 198654.12, 10058.17, 3772.52, 2951.88,
142.86, 287.99, 9448.9, 1461.51, 249.98), dEVdzTB = c(-1.13,
0, 0, 0, 0, 0, 0, -34015.93, -2514.12, -60.93, 109.72, 76326.42,
0, 0, 0, 0, 0, 5769.55, 0, 42.97), dEVdzH = c(-15, 0, -14.32,
17.07, -14, 0, 0, -7017.66, -349.97, -15, 549.99, 33109.02, -340.95,
-307.09, -47.93, -7.13, 13.01, 449.95, 61.62, 0), dEVdrQ = c(348451.86,
0, 441620.17, 900981.67, 487701.61, 0, 0, 335596984.05, 19696287.61,
1017097.6, 13847581.34, 476478784, 36707738.02, 11575841.25,
9599815.68, 535717.91, 1080845.93, 20888792.33, 5386259.63, 948299.85
), dEVdrE = c(-24.56, 0, 0, 0, 0, 0, 0, -925490.96, -68906.02,
-1952.36, 2553.36, 2501979.97, 0, 0, 0, 0, 0, 136325.23, 0, 1582.17
), dEVdpP = c(133.87, 0, 160.54, 267.07, 143.61, 0, 0, 99319.54,
4135.3, 224.06, 6159.66, 274980.54, 10058.17, 3772.52, 2951.88,
142.86, 287.99, 15218.45, 1461.51, 292.95), dEVdD = c(-10.27,
0, -6.52, 8.93, -8.93, 0, 0, -24.57, -40.93, -25.56, 14.06, 59.02,
-2.13, -5.53, -0.55, -4.36, 6.09, 68.91, 5.32, 18.84), dEVdf = c(3327.91,
0, 4549.69, 10047.22, 4384.35, 0, 0, 2775981.18, 118021.52, 8456.02,
166064.35, 9013862.02, 373057.45, 124002.65, 87493.72, 5258.83,
11271.93, 374526.13, 59234.84, 11228.85), dEVdavgPh = c(-162.25,
0, -171.47, -328.66, -170.41, 0, 0, -107505.38, -4532.49, -274.4,
-7350.79, -287563.54, -10693.86, -3995.68, -3297.26, -179.43,
-314.73, -16536.87, -1543.22, -317.73), dPeffdrP = c(-1.04, 0,
-0.92, -1.08, -1.11, -0.94, -1.13, -0.88, -1.03, -1.02, -1.04,
-0.88, -0.98, -0.88, -1.16, -1.11, -1.06, -0.96, -0.97, -1.04
), dMeffdrP = c(0.04, 0.82, 0.01, 0.03, -0.04, -0.05, -0.04,
-0.01, 0.03, 0.01, 0.01, 0, 0, 0, -0.11, 0.03, 0, 0.01, -0.01,
-0.04), type = c("J/B", "SPOT", "J/B", "J/B", "J/B", "J/B", "J*/B",
"KEINE", "J*/B", "J/B", "J*/B", "KEINE", "J*/B", "J/B", "J*/B",
"J/B", "J/B", "J*/B", "J*/B", "J/B"), pBu = c(0.1, 0, 0.05, 0.1,
0.1, 0.05, 0.1, 0, 0.1, 0.15, 0.1, 0, 0.1, 0.1, 0.2, 0.15, 0.1,
0.05, 0.2, 0.2), pBo = c(0.1, 0, 0.05, 0.1, 0.1, 0.05, 0.1, 0,
0.1, 0.15, 0.1, 0, 0.1, 0.1, 0.2, 0.15, 0.1, 0.05, 0.2, 0.2),
zTBu = c(1.5, 0, 1.5, 1.5, 1.5, 1.5, 1.5, 0, 1.5, 5.4, 1.5,
0, 1.5, 3, 1.5, 6.5, 1.5, 1.5, 1.5, 1.5), zTBo = c(1.5, 0,
1.5, 1.5, 1.5, 1.5, 1.5, 0, 1.5, 5.4, 1.5, 0, 1.5, 3, 1.5,
6.5, 1.5, 1.5, 1.5, 1.5), zV = c(1.5, 0, 1.5, 1.5, 1.5, 1,
0.8, 0, 0.5, 1.5, 0.8, 0, 1.5, 0, 0.8, 1.5, 1.5, 0.7, 1.5,
1.5), TP = c(35.19, 0, 37.24, 31.29, 28.73, 27.37, 23.83,
33.99, 27.28, 33.36, 30.41, 29.61, 31.41, 28.22, 23.92, 30.64,
32.98, 31.39, 25.77, 27.42)), class = c("data.table", "data.frame"
), row.names = c(NA, -20L), .internal.selfref = <pointer: 0x0000016781401ef0>)

library(ggplot2)
library(dplyr)
library(plotly)
(df %>%
mutate(pBu=as.factor(pBu)) %>%
ggplot(aes(x=EV)) +
geom_density(aes(y=..scaled..,color=pBu))) %>%
ggplotly()

Sensitivity and specificity changes using a single threshold and a gradient of thresholds at 0.5 using pROC in R

I am trying to calculate ROC for a model of multi-class image. But since I didn't find any best way for multi-class classification, I have converted it to binary class. I have 31 classes of image. Using binary methods I am trying to find ROC of each 31 classes individually.
df <- read.xlsx("data.xlsx",sheetName = 1,header = F)
dn <- as.vector(df$X1) # 31 class
model_info <- read.csv("all_new.csv",stringsAsFactors = F) # details of
model output (Actual labels, Model labels, probabablity values)
head(model_info)
Actual_labels App_labels X1st
1 tinea cruris and corporis tinea cruris and corporis tinea cruris and corporis
2 tinea cruris and corporis tinea cruris and corporis tinea cruris and corporis
3 tinea cruris and corporis no diagnosis acne vulgaris
4 eczema eczema eczema
5 eczema no diagnosis psoriasis
6 folliculitis impetigo and pyodermas impetigo and pyodermas
X2nd X3rd X.st.. X2nd.. X3rd..
1 psoriasis herpes zoster 0.89 0.05 0.03
2 psoriasis eczema 0.89 0.03 0.02
3 psoriasis molluscum contagiosum 0.29 0.16 0.14
4 tinea cruris and corporis psoriasis 0.62 0.09 0.08
5 melasma tinea cruris and corporis 0.27 0.27 0.25
6 acne vulgaris psoriasis 0.73 0.07 0.03
head(dn)
[1] "acne vulgaris" "alopecia areata" "anogenital warts"
[4] "bullous pemphigoid" "candidiasis" "chicken pox"
App_call function basically converts the probability values to 0 or 1 based on whether model call is true or not
app_call <- function(cut_off, category){
labels_thr <- rep(0,nrow(app_res))
ind <- which(model_info$X.st.. >= cut_off) # index of instances
above threshold
true_val <- which(app_res$App.Diagnosis[ind] == category) # index of instances where actual labels are similar to model labels for 1st class out of 31 class.
labels_thr[ind[true_val]] <- 1
return(labels_thr)}
index0 <- grep(pattern = paste0("^",dn[i],"$"),x = model_info$Actual_labels)
actual_labels <- rep(0,nrow(model_info))
if(length(index)>= 1){
actual_labels[index0] <- 1
actual_labels[-index0] <- 0}
app_labels <- app_call(cut_off = 0.5,category = dn[i])
res <- roc(actual_labels,app_labels)
res1 <- roc(actual_labels,model_info$X.st..)
dput(actual_labels)
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0)
dput(app_labels)
c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0)
dput(model_info$X.st..)
c(0.89, 0.89, 0.29, 0.62, 0.27, 0.73, 0.44, 0.7, 0.42, 0.56,
0.87, 0.19, 0.72, 0.54, 0.37, 0.46, 0.89, 0.89, 0.88, 0.2, 0.46,
0.75, 0.78, 0.66, 0.5, 0.67, 0.17, 0.85, 0.75, 0.58, 0.97, 0.71,
0.8, 0.29, 0.56, 0.44, 0.6, 0.36, 0.38, 0.31, 0.17, 0.35, 0.99,
0.19, 0.48, 0.51, 0.48, 0.92, 0.39, 0.14, 0.44, 0.55, 0.5, 0.43,
0.38, 0.27, 0.37, 0.47, 0.63, 0.4, 0.78, 0.96, 0.67, 0.7, 0.61,
0.44, 0.44, 0.48, 0.47, 0.35, 0.44, 0.34, 0.34, 0.6, 0.24, 0.49,
0.47, 0.35, 0.62, 0.76, 0.67, 0.51, 0.48, 0.72, 0.81, 0.9, 0.89,
0.92, 0.94, 0.2, 0.87, 0.98, 0.99, 0.98, 0.85, 0.89, 0.87, 0.62,
0.19, 0.86, 0.49, 0.5, 0.2, 0.48, 0.51, 0.49, 0.3, 0.11, 0.88,
0.35, 0.81, 0.59, 0.67, 0.31, 0.94, 0.22, 0.4, 0.52, 0.21, 0.31,
0.19, 0.79, 0.64, 0.33, 0.27, 0.19, 0.74, 0.78, 0.41, 0.35, 0.8,
0.39, 0.28, 0.92, 0.37, 0.66, 0.66, 0.44, 0.77, 0.49, 0.27, 0.32,
0.65, 0.21, 0.95, 0.51, 0.16, 0.33, 0.85, 0.26, 0.61, 0.25, 0.57,
0.26, 0.21, 0.45, 0.44, 0.83, 0.31, 0.35, 0.91, 0.34, 0.89, 0.77,
0.56, 0.31, 0.26, 0.84, 0.15, 0.27, 0.47, 0.73, 0.28, 0.44, 0.52,
0.65, 0.72, 0.38, 0.99, 0.46, 0.35, 0.25, 0.42, 0.82, 0.24, 0.56,
0.31, 0.52, 0.91, 0.94, 0.68, 0.21, 0.28, 0.68, 0.67, 0.61, 0.6,
0.43, 0.37, 0.28, 0.28, 0.24, 0.4, 0.92, 0.9, 0.45, 0.96, 0.53,
0.72, 0.26, 0.47, 0.47, 0.97, 0.49, 0.25, 0.51, 0.3, 0.92, 0.43,
0.33, 0.95, 0.25, 0.51, 0.98, 0.23, 0.51, 0.75, 0.84, 0.54, 0.5,
0.54, 0.33, 0.64, 0.29, 0.93, 0.13, 0.27, 0.93, 0.59, 0.27, 0.81,
0.57, 0.59, 0.47, 0.24, 0.53, 0.53, 0.43, 0.24, 0.94, 0.6, 0.7,
0.23, 0.69, 0.95, 0.95, 0.49, 0.73, 0.31, 0.94, 0.15, 0.85, 0.92,
0.34, 0.95, 0.91, 0.36, 0.55, 0.55, 0.29, 0.86, 0.31, 0.48, 0.48,
0.45, 0.5, 0.49, 0.3, 0.33, 0.39, 0.8, 0.42, 0.51, 0.52, 0.66,
0.19, 0.58, 0.94, 0.51, 0.39, 0.84, 0.95, 0.85, 0.72, 0.35, 0.83,
0.5, 0.91, 0.83, 0.61, 0.79, 0.5, 0.87, 0.3, 0.5, 0.53, 0.22,
0.82, 0.74, 0.73, 0.65, 0.88, 0.31, 0.75, 0.74, 0.92, 0.38, 0.47,
0.26, 0.77, 0.78, 0.82, 0.59, 0.59, 0.33, 0.67, 0.31, 0.67, 0.44,
0.77, 0.61, 0.44, 0.77, 0.83, 0.58, 0.6, 0.78, 0.76, 0.47, 0.72,
0.47, 0.29, 0.14, 0.32, 0.17, 0.56, 0.68, 0.3, 0.46, 0.56, 0.68,
0.61, 0.7, 0.23, 0.39, 0.79, 0.38, 0.32, 0.58, 0.46, 0.5, 0.57,
0.93, 0.4, 0.37, 0.75, 0.76, 0.36, 0.84, 0.19, 0.18, 0.94, 0.53,
0.53, 0.24, 0.23, 0.51, 0.53, 0.84, 0.23, 0.44, 0.85, 0.53, 0.23,
0.56, 0.26, 0.38, 0.78, 0.93, 0.65, 0.22, 0.52, 0.35, 0.47, 0.33,
0.31, 0.65, 0.72, 0.46, 0.44, 0.74, 0.92, 0.99, 0.72, 0.41, 0.18,
0.85, 0.89, 0.31, 0.4, 0.98, 0.46, 0.16, 0.58, 0.25, 0.21, 0.32,
0.43, 0.56, 0.34, 0.35, 0.7, 0.43, 0.17, 0.25, 0.33, 0.44, 0.44,
0.58, 0.74, 0.37, 0.68, 0.52, 0.8, 0.96, 0.52, 0.25, 0.81, 0.94,
1, 0.58, 0.42, 0.46, 0.41, 0.18, 0.37, 0.9, 0.54, 0.29, 0.38,
0.38, 0.53, 0.99, 0.57, 0.44, 0.33, 0.45, 0.95, 0.85, 0.75, 0.19,
0.97, 0.27, 0.94, 0.77, 0.79, 0.57, 0.33, 0.98, 0.47, 0.55, 0.27,
0.43, 0.66, 1, 0.62, 0.34, 0.81, 0.4, 0.56, 0.33, 0.25, 0.4,
0.25, 0.91, 0.28, 0.4, 0.73, 0.32, 0.49, 0.37, 0.19, 0.35, 0.29,
0.77, 0.36, 0.31, 0.85, 0.33, 0.61, 0.63, 0.41, 0.98, 0.28, 0.31,
0.91, 0.34, 0.24, 0.82, 0.46, 0.5, 0.39, 0.72, 0.67, 0.51, 0.41,
0.81, 0.74, 0.5, 0.97, 0.65, 0.44, 0.71, 0.35, 0.84, 0.97, 0.42,
0.75, 0.91, 0.61, 0.94, 0.48, 0.42, 0.63, 0.81, 0.83, 0.66, 0.55,
0.61, 0.41, 0.63, 1, 0.63, 0.41, 0.75, 0.27, 0.28, 0.24, 0.55,
0.35, 0.85, 0.97, 0.64, 0.79, 0.92, 0.47, 0.81, 0.23, 0.16, 0.75,
0.12, 0.43, 0.18, 0.69, 0.21, 0.39, 0.19, 0.85, 0.57, 0.97, 0.56,
0.81, 0.13, 0.4, 0.47, 0.95, 0.43, 0.9, 0.67, 0.36, 0.38, 0.83,
0.97, 0.48, 0.93, 0.67, 0.44, 0.34, 0.83, 0.77, 0.39, 0.56, 0.85,
0.55, 0.22, 0.48, 0.46, 0.59, 0.89, 0.99, 0.57, 0.96, 0.97, 0.95,
0.98, 0.24, 0.89, 0.5, 0.94, 0.6, 0.41, 0.71, 0.5, 0.2, 0.96,
0.18, 0.93, 0.92, 0.85, 0.92, 0.82, 0.48, 0.62, 0.53, 0.59, 0.38,
0.8, 0.49, 0.91, 0.58, 0.94, 0.68, 0.15, 0.96, 0.98, 0.89, 0.84,
0.5, 0.88, 0.29, 0.24, 0.31, 0.29, 0.33, 0.49, 0.33, 0.76, 0.54,
0.88, 0.78, 0.26, 0.52, 0.75, 0.97, 0.93, 0.27, 0.69, 0.19, 0.69,
0.2, 0.21, 0.84, 0.31, 0.19, 0.8, 0.6, 0.19, 0.51, 0.98, 0.27,
0.39, 0.77, 0.95, 0.73, 0.28, 0.79, 0.19, 0.98, 0.77, 0.31, 0.84,
0.35, 0.19, 0.26, 0.82, 0.63, 0.38, 0.38, 0.26, 0.63, 0.65, 0.55,
0.88, 0.6, 0.71, 0.85, 0.99, 0.28, 0.42, 0.65, 0.58, 0.97, 0.35,
0.36, 0.32, 0.79, 0.68, 0.39, 0.45, 0.71, 0.98, 0.34, 0.62, 0.24,
0.55, 0.43, 0.95, 0.32, 0.6, 0.63, 0.98, 0.2, 0.31, 0.9, 0.3,
0.32, 0.37, 0.52, 0.64, 0.9, 0.22, 0.31, 0.39, 0.21, 0.93, 0.64,
0.4, 0.96, 0.31, 0.46, 0.86, 0.56, 0.99, 0.83, 0.87, 0.36, 0.59,
0.98, 0.72, 0.21, 0.52, 0.17, 0.21, 0.42, 0.97, 0.34, 0.96, 0.18,
0.63, 0.45, 0.36, 0.31, 0.48, 0.94, 0.86, 0.16, 0.32, 0.97, 0.29,
0.9, 0.38, 0.88, 0.6, 0.17, 0.19, 0.44, 0.98, 0.35, 0.36, 0.2,
0.39, 0.53, 0.35, 0.57, 0.18, 0.26, 0.17, 0.77, 0.51, 1, 0.17,
0.57, 0.48, 0.58, 0.25, 0.32, 0.33, 0.76, 0.16, 0.13, 0.46, 0.44,
0.31, 0.56, 0.46, 0.6, 0.17, 0.36, 0.34, 0.44, 0.43, 0.86, 0.86,
0.44, 0.34, 0.92, 0.32, 0.78, 0.21, 0.46, 0.92, 0.27, 0.98, 0.52,
0.34, 0.27, 0.59, 0.45, 0.58, 0.27, 0.48, 0.21, 0.24, 0.29, 0.89,
0.25, 0.33, 0.96, 0.56, 0.29, 0.97, 0.98, 0.59, 0.28, 0.22, 0.76,
0.91, 0.92, 0.91, 0.94, 0.83, 0.48, 0.53, 0.56, 0.5, 0.75, 0.4,
0.98, 0.6, 0.74, 0.66, 0.97, 0.62, 0.99, 0.39, 0.89, 0.86, 0.66,
0.92, 0.34, 0.99, 0.69, 0.71, 0.8, 0.47, 0.5, 0.83, 0.83, 0.41,
0.72, 0.98, 0.76, 0.65, 0.71, 0.9, 0.9, 1, 0.4, 0.46, 0.35, 0.72,
0.92, 0.74, 0.44, 0.67, 0.97, 0.88, 0.84, 0.71, 0.45, 0.78, 0.9,
0.72, 0.57, 0.68, 0.85, 0.84, 0.46, 0.91, 0.53, 0.96, 0.49, 0.93,
0.49, 0.37, 0.95, 0.47, 0.87, 0.49, 0.58, 0.64, 0.84, 0.8, 0.49,
0.67, 0.75, 0.44, 0.87, 0.71, 0.47, 0.46, 0.83, 0.74, 0.99, 0.86,
0.64, 0.74, 0.43, 0.44, 0.57, 0.89, 0.67, 0.59, 0.89, 0.45, 0.62,
0.81, 0.93, 0.81, 0.98, 0.95, 0.63, 0.64, 0.96, 0.55, 0.49, 0.59,
0.47, 0.42, 0.6, 0.51, 0.4, 0.3, 0.29, 0.45, 0.94, 0.29, 0.33,
0.14, 0.71, 0.41, 0.6, 0.31, 0.95, 0.94, 0.87, 0.8, 0.53, 0.66,
0.71, 0.19, 0.49, 0.97, 0.48, 0.43, 0.38, 0.4, 0.22, 0.38, 0.27,
0.25, 0.45, 0.75, 0.38, 0.23, 0.92, 0.7, 0.68, 0.17, 0.39, 0.65,
0.38, 0.39, 0.21, 0.28, 0.55, 0.89, 0.24, 0.34, 0.92, 0.31, 0.64,
0.86, 0.94, 0.28, 0.43, 0.44, 0.82, 0.23, 0.81, 0.71, 0.53, 0.96,
0.9, 0.55, 0.83, 0.64, 0.51, 0.32, 0.66, 0.45, 0.72, 0.28, 0.34,
0.98, 0.76, 0.52, 0.95, 0.83, 0.47, 0.9, 0.31, 0.23, 0.61, 0.94,
0.61, 0.42, 0.34, 0.55, 0.33, 0.93, 0.24, 0.51, 0.65, 0.17, 0.81,
0.68, 0.51, 0.78, 0.37, 0.37, 0.99, 0.94, 0.64, 0.59, 0.61, 0.9,
0.88, 0.64, 0.49, 0.09, 0.51, NA, 0.86, 0.45, 0.61, 0.24, 0.85,
0.26, 0.29, 0.21, 0.66, 0.26, 0.47, 0.19, 0.99, 0.51, 0.91, 0.37,
0.56, 0.71, 0.47, 0.44, 0.48, 0.52, 0.22, 0.52, 0.29, 0.46, 0.54,
0.94, 0.24, 0.24, 0.47, 0.37, 0.9, 0.79, 0.81, 0.41, 0.38, 0.71,
0.34, 0.46, 0.23, 0.54, 0.43, 0.85, 0.56, 0.26, 0.9, 0.25, 0.3,
0.39, 0.89, 0.38, 0.18, 0.78, 0.37, 0.45, 0.51, 0.8, 0.61, 0.52,
0.84, 0.4, 0.31, 0.28, 0.24, 0.23, 0.43, 0.77, 0.78, 0.95, 0.9,
0.81, 0.15, 0.77, 0.77, 0.87, 0.75, 0.16, 0.49, 0.23, 0.93, 0.45,
0.33, 0.75, 0.32, 0.75, 0.41, 0.24, 0.46, 0.17, 0.41, 0.45, 0.48,
0.15, 0.66, 0.53, 0.75, 0.57, 0.46, 0.78, 0.24, 0.29, 0.95, 0.77,
0.66, 0.94, 0.27, 0.29, 0.58, 0.6, 0.46, 0.58, 0.84, 0.69, 0.47,
0.45, 0.48, 0.35, 0.89, 0.98, 0.93, 0.2, 0.94, 0.91, 0.75, 0.5,
0.44, 0.69, 0.8, 0.76, 0.85, 0.84, 0.72, 0.25, 0.73, 0.26, 0.93,
0.15, 0.33, 0.3, 0.6, 0.24, 0.21, 0.28, 0.51, 0.79, 0.77, 0.85,
0.52, 0.39, 0.68, 0.83, 0.36, 0.15, 0.87, 0.55)
res1 = roc(actual_labels,app_labels)
res2= roc(actual_labels,model_info$X.st..)
The calls in the actual label class where it is "1" and have have a probablity threshold (model_info$X.st..) value more than 0.5 is named as "1" for app_labels and rest all zero
Both res1 and res2 have different values for sensitivitiy and specificity.

A ROC curve shows the sensitivity and specificity tradeoff as the decision threshold of a classifier is varied. Typically ROC curve functions expect to get the prediction and the truth value as input.
This is exactly what you do when you run:
res2= roc(actual_labels,model_info$X.st..)
However your app_labels is of a very different nature: you have already merged in the "correct classification" aspect, which makes it more like a flattened contingency table than the "predictions" the ROC function expects. So you can no longer use a regular ROC function and need to calculate the sensitivity and specificity manually.
TP <- sum(app_labels & actual_labels)
TN <- sum(app_labels & !(actual_labels))
FP <- sum(!(app_labels) & !(actual_labels))
FN <- sum(!(app_labels) & actual_labels)
# Sensitivity:
TP / (TP+FN)
# Specificity:
TN / (TN + FP)

How to get multiple sets of subset from a matrix for multiple conditions using loop?

I would like to ask a data manipulation question by R.
all_matrix<-structure(list(V1 = c(0.012, 0, 0, 0, 0.037, 0, 0, 0, 0.007, 0, 0.104, 0.149, 0.164, 0.258, 3.986, 0, 0.002, 0, 0, 0), V2 = c(0, 0.07, 0, 0, 0.017, 0, 0, 0, 0.025, 0, 2.322, 0.327, 0.134, 1.035, 2.732, 0.01, 1.097, 0.388, 0, 0), V3 = c(0, 0, 0, 0.005, 0, 0, 0, 0, 0, 0, 0.007, 0, 0, 0, 1.777, 0, 0.241, 0, 0, 0), V4 = c(0, 0, 0, 0.001, 0.003, 0, 0, 0, 0, 0, 0.207, 0.002, 0.003, 0.015, 0.032, 0, 0.007, 0, 0, 0), V5 = c(0, 0, 0, 0, 0.026, 0, 0, 0, 0.001, 0, 0.101, 0, 0, 0.005, 0.01, 0, 0, 0, 0, 0), V6 = c(0, 0, 0, 0.003, 0.009, 0, 0, 0, 0.076, 0, 0.01, 0.006, 0, 0.091, 0.829, 0, 0.002, 0, 0, 0), V7 = c(0, 0, 0, 0, 0.026, 0, 0, 0, 0.351, 0, 1.849, 0.003, 0, 0.005, 0.998, 0.009, 0.18, 0, 0, 0), V8 = c(0, 0, 0.002, 0.047, 0.01, 0.003, 0, 0, 0.021, 0, 0.848, 0.007, 0.005, 0.206, 0.023, 0, 0.025, 0, 0, 0), V9 = c(0, 0, 0, 0.02, 0.013, 0, 0, 0, 0, 0, 0.008, 0, 0, 0, 0, 0, 0, 0, 0, 0), V10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.012, 9.895, 4.362, 0, 0, 0, 0, 0), P_diff = c(-4.051, -3.769, -3.602, -7.563, -6.398, -1.816, 0.84, -1.91, 3.095, -1.544, 6.068, 16.469, 6.403, 9.9, 9.089, 18.977, 14.123, 3.103, 1.527, -2.902), PH_fold = c(0.144, 0.511, 0.686, 0.372, 0.582, 0.325, 1.312, 0.436, 1.061, 0.371, 1.119, 1.298, 1.134, 1.146, 1.123, 1.484, 1.204, 1.263, 1.843, 0.423), PNH_fold = c(6.933, 1.955, 1.459, 2.69, 1.718, 3.081, 0.762, 2.291, 0.943, 2.696, 0.894, 0.77, 0.882, 0.872, 0.891, 0.674, 0.831, 0.792, 0.543, 2.366)), .Names = c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "P_diff", "PH_fold", "PNH_fold"), class = "data.frame", row.names = c("S1", "S2", "S3", "S4", "S5", "S6", "S7", "S8", "S9", "S10", "S11", "S12", "S13", "S14", "S15", "S16", "S17", "S18", "S19", "S20"))
sig1<-data.frame(subset(all_matrix, all_matrix$PH_fold >= 1.1 & all_matrix$P_diff >=1))##conditions are in range like all_matrix$PH_fold >= 1.1,1.2,1.3,....,2 & all_matrix$P_diff >= 1,2,3,4......, upto 20###
sig2<-data.frame(subset(all_matrix, all_matrix$PNH_fold >= 1.1 & all_matrix$P_diff <= -1))##conditions are in range like all_matrix$PNH_fold >= 1.1,1.2,1.3,....,2 & all_matrix$P_diff >= -1,-2,-3,-4......, upto -20###
d <- function(x){sum((log(x[x>0]))*(x[x>0]))*(-1)}
sh1<-apply((sig1[,-c(5:7)]/100), 2, d)
sh2<-apply((sig2[,-c(5:7)]/100), 2, d)
count1<-apply(sig1[,-c(5:7)], 2, function(i) (sum(i > 0)))
count2<-apply(sig2[,-c(5:7)], 2, function(i) (sum(i > 0)))
G1<-(sig1*sh1)
G2<-(sig2*sh2)
G<-data.frame(G1/G2)
I would like to set a R code to calculate "G" for all subsets for all_matrix based on each condition as mentioned above.
So how can I get the subsets of a matrix using different conditions for each subset using loop for further process to calculate "G":
I want to use subset() function in loop:
Can anyone help me out?
Thank you!
I appreciate any replies!

If you have large dataframes, don't use subset, it's slower than any other option. The fastest is generally the indexing way.
I first create two indices with the conditions given. Then a list to hold the sub-dfs.
i1 <- data1$age < 15
i2 <- data1$age > 20
sub_data1 <- list()
sub_data1[['less_15']] <- data1[i1, ]
sub_data1[['over_20']] <- data1[i2, ]
Note that you can skip the indices creation code and simply do
sub_data1 <- list()
sub_data1[['less_15']] <- data1[data1$age < 15, ]
sub_data1[['over_20']] <- data1[data1$age > 20, ]

Resources