I have a dataframe which is of length 177 and I want to calculate and plot the partial auto-correlation function (PACF).
I have the data imported etc and I do:
from statsmodels.tsa.stattools import pacf
ys = pacf(data[key][array].diff(1).dropna(), alpha=0.05, nlags=176, method="ywunbiased")
xs = range(lags+1)
plt.figure()
plt.scatter(xs,ys[0])
plt.grid()
plt.vlines(xs, 0, ys[0])
plt.plot(ys[1])
The method used results in numbers greater than 1 for very long lags (90ish) which is incorrect and I get a RuntimeWarning: invalid value encountered in sqrtreturn rho, np.sqrt(sigmasq) but since I can't see their source code I don't know what this means.
To be honest, when I search for PACF, all the examples only carry out PACF up to 40 lags or 60 or so and they never have any significant PACF after lag=2 and so I couldn't compare to other examples either.
But when I use:
method="ols"
# or
method="ywmle"
the numbers are corrected. So it must be the algo they use to solve it.
I tried importing inspect and getsource method but its useless it just shows that it uses another package and I can't find that.
If you also know where the problem arises from, I would really appreciate the help.
For your reference, the values for data[key][array] are:
[1131.130005, 1144.939941, 1126.209961, 1107.300049, 1120.680054, 1140.839966, 1101.719971, 1104.23999, 1114.579956, 1130.199951, 1173.819946, 1211.920044, 1181.27002, 1203.599976, 1180.589966, 1156.849976, 1191.5, 1191.329956, 1234.180054, 1220.329956, 1228.810059, 1207.01001, 1249.47998, 1248.290039, 1280.079956, 1280.660034, 1294.869995, 1310.609985, 1270.089966, 1270.199951, 1276.660034, 1303.819946, 1335.849976, 1377.939941, 1400.630005, 1418.300049, 1438.23999, 1406.819946, 1420.859985, 1482.369995, 1530.619995, 1503.349976, 1455.27002, 1473.98999, 1526.75, 1549.380005, 1481.140015, 1468.359985, 1378.550049, 1330.630005, 1322.699951, 1385.589966, 1400.380005, 1280.0, 1267.380005, 1282.829956, 1166.359985, 968.75, 896.23999, 903.25, 825.880005, 735.090027, 797.869995, 872.8099980000001, 919.1400150000001, 919.320007, 987.4799800000001, 1020.6199949999999, 1057.079956, 1036.189941, 1095.630005, 1115.099976, 1073.869995, 1104.48999, 1169.430054, 1186.689941, 1089.410034, 1030.709961, 1101.599976, 1049.329956, 1141.199951, 1183.26001, 1180.550049, 1257.640015, 1286.119995, 1327.219971, 1325.829956, 1363.609985, 1345.199951, 1320.640015, 1292.280029, 1218.890015, 1131.420044, 1253.300049, 1246.959961, 1257.599976, 1312.410034, 1365.680054, 1408.469971, 1397.910034, 1310.329956, 1362.160034, 1379.319946, 1406.579956, 1440.670044, 1412.160034, 1416.180054, 1426.189941, 1498.109985, 1514.680054, 1569.189941, 1597.569946, 1630.73999, 1606.280029, 1685.72998, 1632.969971, 1681.550049, 1756.540039, 1805.810059, 1848.359985, 1782.589966, 1859.449951, 1872.339966, 1883.949951, 1923.569946, 1960.22998, 1930.6700440000002, 2003.369995, 1972.290039, 2018.050049, 2067.560059, 2058.899902, 1994.9899899999998, 2104.5, 2067.889893, 2085.51001, 2107.389893, 2063.110107, 2103.840088, 1972.180054, 1920.030029, 2079.360107, 2080.409912, 2043.939941, 1940.2399899999998, 1932.22998, 2059.73999, 2065.300049, 2096.949951, 2098.860107, 2173.600098, 2170.949951, 2168.27002, 2126.149902, 2198.810059, 2238.830078, 2278.8701170000004, 2363.639893, 2362.719971, 2384.199951, 2411.800049, 2423.409912, 2470.300049, 2471.649902, 2519.360107, 2575.26001, 2584.840088, 2673.610107, 2823.810059, 2713.830078, 2640.8701170000004, 2648.050049, 2705.27002, 2718.3701170000004, 2816.290039, 2901.52002, 2913.97998]
Your time series is pretty clearly not stationary, so that Yule-Walker assumptions are violated.
More generally, PACF is usually appropriate with stationary time series. You might difference your data first, before considering the partial autocorrelations.
Related
Is there any way to detect the highest peaks using a python library without setting any parameter?. I'm developing a user interface and I want the algorithm to be able to detect highest peaks automatically...
I want it to be able to detect these peaks in picture below:
graph here
Data looks like this:
8.60291e-07
-1.5491e-06
5.64568e-07
-9.51195e-07
1.07203e-06
4.6521e-07
6.43967e-07
-9.86092e-07
-9.82323e-07
6.38977e-07
-1.93884e-06
-2.98309e-08
1.33543e-06
1.05064e-06
1.17332e-06
-1.53549e-07
-8.9357e-07
1.59176e-06
-2.17331e-06
1.46756e-06
5.63301e-07
-8.77556e-07
7.47681e-09
-8.30101e-07
-3.6647e-07
5.27046e-07
-1.94983e-06
1.89018e-07
1.22533e-06
8.00735e-07
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1.13437e-06
-2.75787e-07
1.79601e-06
-1.67875e-06
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-inf
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inf
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-3.98315e-07
4.93927e-07
-5.23987e-07
-1.74713e-07
6.49384e-07
-7.16766e-07
2.35733e-06
-4.91333e-08
-1.88138e-06
1.74722e-06
4.03503e-07
3.5965e-07
1.44836e-07]
The task you are describing could be treated like anomaly/outlier detection.
One possible solution is to use a Z-score transformation and treat every value with a z score above a certain threshold as an outlier. Because there is no clear definition of an outlier it won't be able to detect such peaks without setting any parameters (threshold).
One possible solution could be:
import numpy as np
def detect_outliers(data):
outliers = []
d_mean = np.mean(data)
d_std = np.std(data)
threshold = 3 # this defines what you would consider a peak (outlier)
for point in data:
z_score = (point - d_mean)/d_std
if np.abs(z_score) > threshold:
outliers.append(point)
return outliers
# create normal data
data = np.random.normal(size=100)
# create outliers
outliers = np.random.normal(100, size=3)
# combine normal data and outliers
full_data = data.tolist() + outliers.tolist()
# print outliers
print(detect_outliers(full_data))
If you only want to detect peaks, remove the np.abs function call from the code.
This code snippet is based on a Medium Post, which also provides another way of detecting outliers.
To solve a 5 parameter model, I need at least 5 data points to get a unique solution. For x and y data below:
import numpy as np
x = np.array([[-0.24155831, 0.37083184, -1.69002708, 1.4578805 , 0.91790011,
0.31648635, -0.15957368],
[-0.37541846, -0.14572825, -2.19695883, 1.01136142, 0.57288752,
0.32080956, -0.82986857],
[ 0.33815532, 3.1123936 , -0.29317028, 3.01493602, 1.64978158,
0.56301755, 1.3958912 ],
[ 0.84486735, 4.74567324, 0.7982888 , 3.56604097, 1.47633894,
1.38743513, 3.0679506 ],
[-0.2752026 , 2.9110031 , 0.19218081, 2.0691105 , 0.49240373,
1.63213241, 2.4235483 ],
[ 0.89942508, 5.09052174, 1.26048572, 3.73477373, 1.4302902 ,
1.91907482, 3.70126468]])
y = np.array([-0.81388378, -1.59719762, -0.08256274, 0.61297275, 0.99359647,
1.11315445])
I used only 6 data to fit a 8 parameter model (7 slopes and 1 intercept).
lr = LinearRegression().fit(x, y)
print(lr.coef_)
array([-0.83916772, -0.57249998, 0.73025938, -0.02065629, 0.47637768,
-0.36962192, 0.99128474])
print(lr.intercept_)
0.2978781587718828
Clearly, it's using some kind of assignment to reduce the degrees of freedom. I tried to look into the source code but couldn't found anything about that. What method do they use to find the parameter of under specified model?
You don't need to reduce the degrees of freedom, it simply finds a solution to the least squares problem min sum_i (dot(beta,x_i)+beta_0-y_i)**2. For example, in the non-sparse case it uses the linalg.lstsq module from scipy. The default solver for this optimization problem is the gelsd LAPACK driver. If
A= np.concatenate((ones_v, X), axis=1)
is the augmented array with ones as its first column, then your solution is given by
x=numpy.linalg.pinv(A.T*A)*A.T*y
Where we use the pseudoinverse precisely because the matrix may not be of full rank. Of course, the solver doesn't actually use this formula but uses singular value Decomposition of A to reduce this formula.
I thought that setting a fixed number of decimal points to all numbers of an array of Decimals, and the new arrays resulting from operations thereof, could be achieved by simply doing:
from decimal import *
getcontext().prec = 5 # 4 decimal points
v = Decimal(0.005)
print(v)
0.005000000000000000104083408558608425664715468883514404296875
However, I get spurious results that I know are the consequence of the contribution of these extra decimals to the calculations. Therefore, as a workaround, I used the round() function like this:
C_subgrid= [Decimal('33.340'), Decimal('33.345'), Decimal('33.350'), Decimal('33.355'), Decimal('33.360'), Decimal('33.365'), Decimal('33.370'), Decimal('33.375'), Decimal('33.380'), Decimal('33.385'), Decimal('33.390'), Decimal('33.395'), Decimal('33.400'), Decimal('33.405'), Decimal('33.410'), Decimal('33.415'), Decimal('33.420'), Decimal('33.425'), Decimal('33.430'), Decimal('33.435'), Decimal('33.440'), Decimal('33.445'), Decimal('33.450'), Decimal('33.455'), Decimal('33.460'), Decimal('33.465'), Decimal('33.470'), Decimal('33.475'), Decimal('33.480'), Decimal('33.485'), Decimal('33.490'), Decimal('33.495'), Decimal('33.500'), Decimal('33.505'), Decimal('33.510'), Decimal('33.515'), Decimal('33.520'), Decimal('33.525'), Decimal('33.530'), Decimal('33.535'), Decimal('33.540'), Decimal('33.545'), Decimal('33.550'), Decimal('33.555'), Decimal('33.560'), Decimal('33.565'), Decimal('33.570'), Decimal('33.575'), Decimal('33.580'), Decimal('33.585'), Decimal('33.590'), Decimal('33.595'), Decimal('33.600'), Decimal('33.605'), Decimal('33.610'), Decimal('33.615'), Decimal('33.620'), Decimal('33.625'), Decimal('33.630'), Decimal('33.635'), Decimal('33.640'), Decimal('33.645'), Decimal('33.650'), Decimal('33.655'), Decimal('33.660'), Decimal('33.665'), Decimal('33.670'), Decimal('33.675'), Decimal('33.680'), Decimal('33.685'), Decimal('33.690'), Decimal('33.695'), Decimal('33.700'), Decimal('33.705'), Decimal('33.710'), Decimal('33.715'), Decimal('33.720'), Decimal('33.725'), Decimal('33.730'), Decimal('33.735'), Decimal('33.740'), Decimal('33.745'), Decimal('33.750'), Decimal('33.755'), Decimal('33.760'), Decimal('33.765'), Decimal('33.770'), Decimal('33.775'), Decimal('33.780'), Decimal('33.785'), Decimal('33.790'), Decimal('33.795'), Decimal('33.800'), Decimal('33.805'), Decimal('33.810'), Decimal('33.815'), Decimal('33.820'), Decimal('33.825'), Decimal('33.830'), Decimal('33.835'), Decimal('33.840'), Decimal('33.845'), Decimal('33.850'), Decimal('33.855'), Decimal('33.860'), Decimal('33.865'), Decimal('33.870'), Decimal('33.875'), Decimal('33.880'), Decimal('33.885'), Decimal('33.890'), Decimal('33.895'), Decimal('33.900'), Decimal('33.905'), Decimal('33.910'), Decimal('33.915'), Decimal('33.920'), Decimal('33.925'), Decimal('33.930'), Decimal('33.935'), Decimal('33.940'), Decimal('33.945'), Decimal('33.950'), Decimal('33.955'), Decimal('33.960'), Decimal('33.965'), Decimal('33.970'), Decimal('33.975'), Decimal('33.980'), Decimal('33.985'), Decimal('33.990'), Decimal('33.995'), Decimal('34.000'), Decimal('34.005'), Decimal('34.010'), Decimal('34.015'), Decimal('34.020'), Decimal('34.025'), Decimal('34.030'), Decimal('34.035'), Decimal('34.040'), Decimal('34.045'), Decimal('34.050'), Decimal('34.055'), Decimal('34.060'), Decimal('34.065'), Decimal('34.070'), Decimal('34.075'), Decimal('34.080'), Decimal('34.085'), Decimal('34.090'), Decimal('34.095'), Decimal('34.100'), Decimal('34.105'), Decimal('34.110'), Decimal('34.115'), Decimal('34.120'), Decimal('34.125'), Decimal('34.130'), Decimal('34.135'), Decimal('34.140')]
C_subgrid = [round(v, 4) for v in C_subgrid]
I got the values of C_subgrid list by printing it out during execution of my code, and I pasted it here. Not sure where the single quotes come from. This code snipped worked fine in Python2.7, but when I upgraded to Python 3.7 it started raising this error:
File "/home2/thomas/Documents/4D-CHAINS_dev/lib/peak.py", line 301, in <listcomp>
C_subgrid = [round(v, 4) for v in C_subgrid] # convert all values to fixed decimal length floats!
decimal.InvalidOperation: [<class 'decimal.InvalidOperation'>]
Strangely, if I run it within ipython it works fine, only within my code it creates problems. Can anybody think of any possible reason?
I have been coding in Python for a 2 months or so, but I mostly ask for help from a more experienced friend when I run in to these kinds of issues. I should also, before I begin, specify that I use Python solely for a personal project; any questions I ask will relate to each other through that.
With those two things out of the way, I have a database of weaponry items that I created using the following script, made in Python 3.X:
#Start by making a list of every material, weapontype, and upgrade.
Materials=("Unobtanium","IvorySilk","BoneLeather","CottonWood","Tin","Copper","Bronze","Gold","Cobalt","Tungsten")
WeaponTypes=("Knife","Sword","Greatsword","Polearm","Battlestaff","Claw","Cane","Wand","Talis","Slicer","Rod","Bow","Crossbow","Handbow","Pistol","Mechgun","Rifle","Shotgun")
Upgrades=("0","1","2","3","4","5","6","7","8","9","10")
ForgeWInputs=[]
#Go through every material...
for m in Materials:
#And in each material, go through every weapontype...
for w in WeaponTypes:
#And in every weapontype, go through each upgrade...
for u in Upgrades:
ForgeWInputs.append((m,w,u))
#We now have a list "ForgeWInputs", which contains the 3-element list needed to
#Forge any weapon. For example...
MAT={}
MAT["UnobtaniumPD"]=0
MAT["UnobtaniumMD"]=0
MAT["UnobtaniumAC"]=0
MAT["UnobtaniumPR"]=0
MAT["UnobtaniumMR"]=0
MAT["UnobtaniumWT"]=0
MAT["UnobtaniumBuy"]=0
MAT["UnobtaniumSell"]=0
MAT["IvorySilkPD"]=0
MAT["IvorySilkMD"]=12
MAT["IvorySilkAC"]=3
MAT["IvorySilkPR"]=0
MAT["IvorySilkMR"]=3
MAT["IvorySilkWT"]=6
MAT["IvorySilkBuy"]=10
MAT["IvorySilkSell"]=5
MAT["CottonWoodPD"]=8
MAT["CottonWoodMD"]=8
MAT["CottonWoodAC"]=5
MAT["CottonWoodPR"]=0
MAT["CottonWoodMR"]=3
MAT["CottonWoodWT"]=6
MAT["CottonWoodBuy"]=14
MAT["CottonWoodSell"]=7
MAT["BoneLeatherPD"]=12
MAT["BoneLeatherMD"]=0
MAT["BoneLeatherAC"]=3
MAT["BoneLeatherPR"]=3
MAT["BoneLeatherMR"]=0
MAT["BoneLeatherWT"]=6
MAT["BoneLeatherBuy"]=10
MAT["BoneLeatherSell"]=5
MAT["TinPD"]=18
MAT["TinMD"]=6
MAT["TinAC"]=3
MAT["TinPR"]=5
MAT["TinMR"]=2
MAT["TinWT"]=12
MAT["TinBuy"]=20
MAT["TinSell"]=10
MAT["CopperPD"]=6
MAT["CopperMD"]=18
MAT["CopperAC"]=3
MAT["CopperPR"]=2
MAT["CopperMR"]=5
MAT["CopperWT"]=12
MAT["CopperBuy"]=20
MAT["CopperSell"]=10
MAT["BronzePD"]=10
MAT["BronzeMD"]=10
MAT["BronzeAC"]=5
MAT["BronzePR"]=3
MAT["BronzeMR"]=3
MAT["BronzeWT"]=15
MAT["BronzeBuy"]=30
MAT["BronzeSell"]=15
MAT["GoldPD"]=10
MAT["GoldMD"]=30
MAT["GoldAC"]=0
MAT["GoldPR"]=5
MAT["GoldMR"]=10
MAT["GoldWT"]=25
MAT["GoldBuy"]=50
MAT["GoldSell"]=25
MAT["CobaltPD"]=30
MAT["CobaltMD"]=10
MAT["CobaltAC"]=0
MAT["CobaltPR"]=10
MAT["CobaltMR"]=0
MAT["CobaltWT"]=25
MAT["CobaltBuy"]=50
MAT["CobaltSell"]=25
MAT["TungstenPD"]=20
MAT["TungstenMD"]=20
MAT["TungstenAC"]=0
MAT["TungstenPR"]=7
MAT["TungstenMR"]=7
MAT["TungstenWT"]=20
MAT["TungstenBuy"]=70
MAT["TungstenSell"]=35
WEP={}
WEP["KnifePD"]=0.5
WEP["KnifeMD"]=0.5
WEP["KnifeAC"]=1.25
WEP["SwordPD"]=1.0
WEP["SwordMD"]=1.0
WEP["SwordAC"]=1.0
WEP["GreatswordPD"]=1.67
WEP["GreatswordMD"]=0.67
WEP["GreatswordAC"]=0.5
WEP["PolearmPD"]=1.15
WEP["PolearmMD"]=1.15
WEP["PolearmAC"]=1.15
WEP["CanePD"]=1.15
WEP["CaneMD"]=1.15
WEP["CaneAC"]=0.7
WEP["ClawPD"]=1.1
WEP["ClawMD"]=1.1
WEP["ClawAC"]=0.8
WEP["BattlestaffPD"]=1.15
WEP["BattlestaffMD"]=1
WEP["BattlestaffAC"]=1.25
WEP["TalisPD"]=1.15
WEP["TalisMD"]=0.7
WEP["TalisAC"]=1.15
WEP["WandPD"]=0.0
WEP["WandMD"]=1
WEP["WandAC"]=1.33
WEP["RodPD"]=0.0
WEP["RodMD"]=1.67
WEP["RodAC"]=0.67
WEP["SlicerPD"]=0.67
WEP["SlicerMD"]=0.67
WEP["SlicerAC"]=0.67
WEP["BowPD"]=1.15
WEP["BowMD"]=1.15
WEP["BowAC"]=0.85
WEP["CrossbowPD"]=1.4
WEP["CrossbowMD"]=1.4
WEP["CrossbowAC"]=1
WEP["PistolPD"]=0.65
WEP["PistolMD"]=0.65
WEP["PistolAC"]=1.15
WEP["MechgunPD"]=0.2
WEP["MechgunMD"]=0.2
WEP["MechgunAC"]=1.5
WEP["ShotgunPD"]=1.3
WEP["ShotgunMD"]=1.3
WEP["ShotgunAC"]=0.4
WEP["RiflePD"]=0.75
WEP["RifleMD"]=0.75
WEP["RifleAC"]=1.75
WEP["HandbowPD"]=0.8
WEP["HandbowMD"]=0.8
WEP["HandbowAC"]=1.2
UP={}
UP["0PD"]=1.0
UP["1PD"]=1.1
UP["2PD"]=1.2
UP["3PD"]=1.3
UP["4PD"]=1.4
UP["5PD"]=1.5
UP["6PD"]=1.6
UP["7PD"]=1.7
UP["8PD"]=1.8
UP["9PD"]=1.9
UP["10PD"]=2.0
UP["0MD"]=1.0
UP["1MD"]=1.1
UP["2MD"]=1.2
UP["3MD"]=1.3
UP["4MD"]=1.4
UP["5MD"]=1.5
UP["6MD"]=1.6
UP["7MD"]=1.7
UP["8MD"]=1.8
UP["9MD"]=1.9
UP["10MD"]=2.0
UP["0AC"]=1.0
UP["1AC"]=1.1
UP["2AC"]=1.2
UP["3AC"]=1.3
UP["4AC"]=1.4
UP["5AC"]=1.5
UP["6AC"]=1.6
UP["7AC"]=1.7
UP["8AC"]=1.8
UP["9AC"]=1.9
UP["10AC"]=2.0
UP["0PR"]=1.0
UP["1PR"]=1.1
UP["2PR"]=1.2
UP["3PR"]=1.3
UP["4PR"]=1.4
UP["5PR"]=1.5
UP["6PR"]=1.6
UP["7PR"]=1.7
UP["8PR"]=1.8
UP["9PR"]=1.9
UP["10PR"]=2.0
UP["0MR"]=1.0
UP["1MR"]=1.1
UP["2MR"]=1.2
UP["3MR"]=1.3
UP["4MR"]=1.4
UP["5MR"]=1.5
UP["6MR"]=1.6
UP["7MR"]=1.7
UP["8MR"]=1.8
UP["9MR"]=1.9
UP["10MR"]=2.0
UP["0WT"]=1.0
UP["1WT"]=0.95
UP["2WT"]=0.9
UP["3WT"]=0.85
UP["4WT"]=0.8
UP["5WT"]=0.75
UP["6WT"]=0.7
UP["7WT"]=0.65
UP["8WT"]=0.6
UP["9WT"]=0.55
UP["10WT"]=0.5
def ForgeW(Material,WeaponType,UpgradeLevel):
"""The ForgeW function Forges a Weapon from its base components into a lethal tool."""
#Get the appropriate material stats...
OrePD=MAT[Material+"PD"]
OreMD=MAT[Material+"MD"]
OreAC=MAT[Material+"AC"]
#And weapon type stats...
SmithPD=WEP[WeaponType+"PD"]
SmithMD=WEP[WeaponType+"MD"]
SmithAC=WEP[WeaponType+"AC"]
#And apply the upgrade...
UpgradePD=UP[UpgradeLevel+"PD"]
UpgradeMD=UP[UpgradeLevel+"MD"]
UpgradeAC=UP[UpgradeLevel+"AC"]
#Then, add them all together.
ProductPD=(OrePD*SmithPD)*UpgradePD
ProductMD=(OreMD*SmithMD)*UpgradeMD
ProductAC=(OreAC*SmithAC)*UpgradeAC
return(ProductPD,ProductMD,ProductAC)
#Recall that ForgeW simply needs its three inputs, which we have a list of. So, let's make our
#database of weapon information.
OmniWeapData={}
#Go through every set of inputs we have...
for Inputs in ForgeWInputs:
#And create a key in the dictionary by combining their three names. Then, set that
#key equal to whatever ForgeW returns when those three inputs are put in.
OmniWeapData[Inputs[0]+Inputs[1]+Inputs[2]] = ForgeW(Inputs[0],Inputs[1],Inputs[2])
I would like to refer to the database created by this code and pull out weapons at random, and frankly I have no idea how. As an example of what I would like to do...
Well, hum. The code in question should spit out a certain number of results based on the complete products of the ForgeW function - if I specify, either within the code or through an input, that I would like 3 outputs, it might output a GoldKnife0, a TinPolearm5, and a CobaltGreatsword10. If I were to run the code again, it should dispense new equipment - not the same three every time.
I apologize if this is too much or too little data - it's my first time asking a question here.
"Take this... it may help you on your quest."
There is a library called random with a method called choice().
e.g.
import random
random.choice([1,2,3])
>>> 2
It sounds like you need one item from Materials, one item from WeaponTypes, and one from Upgrades.
Also, rarely is there ever a need for a triple nested FOR statement. This should get you started.
I am trying to learn linearK estimates on a small linnet object from the CRC spatstat book (chapter 17) and when I use the linearK function, spatstat throws an error. I have documented the process in the comments in the r code below. The error is as below.
Error in seq.default(from = 0, to = right, length.out = npos + 1L) : 'to' cannot be NA, NaN or infinite
I do not understand how to resolve this. I am following this process:
# I have data of points for each data of the week
# d1 is district 1 of the city.
# I did the step below otherwise it was giving me tbl class
d1_data=lapply(split(d1, d1$openDatefactor),as.data.frame)
# I previously create a linnet and divided it into districts of the city
d1_linnet = districts_linnet[["d1"]]
# I create point pattern for each day
d1_ppp = lapply(d1_data, function(x) as.ppp(x, W=Window(d1_linnet)))
plot(d1_ppp[[1]], which.marks="type")
# I am then converting the point pattern to a point pattern on linear network
d1_lpp <- as.lpp(d1_ppp[[1]], L=d1_linnet, W=Window(d1_linnet))
d1_lpp
Point pattern on linear network
3 points
15 columns of marks: ‘status’, ‘number_of_’, ‘zip’, ‘ward’,
‘police_dis’, ‘community_’, ‘type’, ‘days’, ‘NAME’,
‘DISTRICT’, ‘openDatefactor’, ‘OpenDate’, ‘coseDatefactor’,
‘closeDate’ and ‘instance’
Linear network with 4286 vertices and 6183 lines
Enclosing window: polygonal boundary
enclosing rectangle: [441140.9, 448217.7] x [4640080, 4652557] units
# the errors start from plotting this lpp object
plot(d1_lpp)
"show.all" is not a graphical parameter
Show Traceback
Error in plot.window(...) : need finite 'xlim' values
coords(d1_lpp)
x y seg tp
441649.2 4649853 5426 0.5774863
445716.9 4648692 5250 0.5435492
444724.6 4646320 677 0.9189631
3 rows
And then consequently, I also get error on linearK(d1_lpp)
Error in seq.default(from = 0, to = right, length.out = npos + 1L) : 'to' cannot be NA, NaN or infinite
I feel lpp object has the problem, but I find it hard to interpret the errors and how to resolve them. Could someone please guide me?
Thanks
I can confirm there is a bug in plot.lpp when trying to plot the marked point pattern on the linear network. That will hopefully be fixed soon. You can plot the unmarked point pattern using
plot(unmark(d1_lpp))
I cannot reproduce the problem with linearK. Which version of spatstat are you running? In the development version on my laptop spatstat_1.51-0.073 everything works. There has been changes to this code recently, so it is likely that this will be solved by updating to development version (see https://github.com/spatstat/spatstat).