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How to retrieve the data frame used in a GEE model fit?


How to retrieve the data frame used in a GEE model fit?

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How to retrieve the data frame used in a GEE model fit?
Tag : r , By : gopal
Date : January 12 2021, 09:11 PM

seems to work fine The 789 observations used in model fitting were the ones which were without NA. You had 72 observations as NA in Feed column
sum(is.na(dietox$Feed))
#[1] 72
dietox$Prediction <- NA
dietox$Prediction[!is.na(dietox$Feed)] <- predict(model1)

head(dietox)
#    Weight      Feed Time  Pig Evit Cu Litter Prediction
#1 26.50000        NA    1 4601    1  1      1         NA
#2 27.59999  5.200005    2 4601    1  1      1   31.43603
#3 36.50000 17.600000    3 4601    1  1      1   36.76708
#4 40.29999 28.500000    4 4601    1  1      1   41.45324
#5 49.09998 45.200001    5 4601    1  1      1   48.63296
#6 55.39999 56.900002    6 4601    1  1      1   53.66306

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Extract a data frame using model.frame and formula


Tag : r , By : Saurabh
Date : March 29 2020, 07:55 AM
To fix the issue you can do I want to extract a data frame using a formula, which specifies which columns to select and some crossing overs among columns. , You can use model.matrix:
> model.matrix(f, df)
  (Intercept) x y x:y
1           1 1 2   2
2           1 2 3   6
3           1 3 4  12
4           1 4 7  28
attr(,"assign")
[1] 0 1 2 3
> mat <- model.matrix(f, df)
> library(Matrix)
> Matrix(mat, sparse = TRUE)
4 x 4 sparse Matrix of class "dgCMatrix"
  (Intercept) x y x:y
1           1 1 2   2
2           1 2 3   6
3           1 3 4  12
4           1 4 7  28

retrieve normal data frame after pivoting a data frame


Tag : python , By : hyperNURb
Date : March 29 2020, 07:55 AM
hope this fix your issue When you call pivot_table, make sure you specify the values parameter:
df.pivot_table(index=['time', 'name'], columns=['feature_type'], 
               values='feature_value')
result = df.pivot_table(index=['time', 'name'], 
                        columns=['feature_type'],
                        values='feature_value').reset_index()
import numpy as np
import pandas as pd
np.random.seed(2016)

N = 10
df = pd.DataFrame(
    {'time': np.random.choice(pd.date_range('2016-05-10', '2016-05-12'), size=N),
     'name': np.random.choice(['Clay', 'John', 'Mary', 'Boby', 'Lucy'], size=N),
     'feature_type': np.random.choice(['f{}'.format(i) for i in range(1,6)], size=N),
     'feature_value': np.random.randint(100, size=N)})

orig = df.pivot_table(index=['time', 'name'], columns=['feature_type'])
print(orig)

alt = df.pivot_table(index=['time', 'name'], 
                     columns=['feature_type'],
                     values='feature_value').reset_index()
alt.columns.name = None
print(alt)
                feature_value                        
feature_type               f1    f2    f3    f4    f5
time       name                                      
2016-05-10 John           NaN  50.0   NaN   NaN  91.0
           Lucy           NaN   NaN   NaN  28.0   NaN
           Mary           NaN   NaN  19.0   NaN  27.0
2016-05-11 Clay           2.0   NaN   NaN   NaN   NaN
           Lucy          24.0   NaN   NaN   NaN   NaN
2016-05-12 Boby           NaN  16.0   NaN   NaN   NaN
           John           NaN   NaN   NaN   NaN  62.0
           Mary           NaN   NaN   NaN  84.0   NaN
        time  name    f1    f2    f3    f4    f5
0 2016-05-10  John   NaN  50.0   NaN   NaN  91.0
1 2016-05-10  Lucy   NaN   NaN   NaN  28.0   NaN
2 2016-05-10  Mary   NaN   NaN  19.0   NaN  27.0
3 2016-05-11  Clay   2.0   NaN   NaN   NaN   NaN
4 2016-05-11  Lucy  24.0   NaN   NaN   NaN   NaN
5 2016-05-12  Boby   NaN  16.0   NaN   NaN   NaN
6 2016-05-12  John   NaN   NaN   NaN   NaN  62.0
7 2016-05-12  Mary   NaN   NaN   NaN  84.0   NaN

Retrieve rows from data frame for partial matching in column of the data frame with elements in list


Tag : python , By : Adam May
Date : March 29 2020, 07:55 AM
hop of those help? Another simplier solution with str.split and DataFrame.isin with boolean indexing:
gene_list = ['ARF3', 'ABC']

df1 = df.gene_name.str.split(',', expand=True)
mask = df1.isin(gene_list)
s = df1[mask].dropna(how='all').apply(lambda x: x[x.first_valid_index()], axis=1)
s.name='new'

print (s)
0    ARF3
1     ABC
2    ARF3
3    ARF3
4    ARF3
Name: new, dtype: object

print (df.join(s).dropna(subset=['new']))
   chr             gene_name   new
0    1                  ARF3  ARF3
1    1                   ABC   ABC
2    1          ARF3,ENSG123  ARF3
3    1  ENSG1245,ARF3,ENSG89  ARF3
4    1             ENSG,ARF3  ARF3
gene_list = ['ARF3', 'ABC']

#new dafarame with splited values
df1 = df.gene_name.str.split(',', expand=True)
#mask - True where is desired value
mask = df1.isin(gene_list)
#find first valid value in dataframe and create serie by these values
s = df1[mask].dropna(how='all').apply(lambda x: x[x.first_valid_index()], axis=1)
s.name='new'
print (s)
0    ARF3
1     ABC
2    ARF3
3    ARF3
4    ARF3
Name: new, dtype: object

#join series to filtered dataframe - create new column
print (df[mask.any(1)].join(s))
   chr             gene_name   new
0    1                  ARF3  ARF3
1    1                   ABC   ABC
2    1          ARF3,ENSG123  ARF3
3    1  ENSG1245,ARF3,ENSG89  ARF3
4    1             ENSG,ARF3  ARF3

How to Retrieve Specific data.frame combination by using another Index data.frame?


Tag : r , By : Munir
Date : March 29 2020, 07:55 AM
I hope this helps you . I'm doing a data validation Project in R. After calculations I have produced 2 Dataframes as following : , Here's an option:
i <- t(indices)
data.frame(Name = registry[i[,1],1], Grade = registry[i[,2],2])
#    Name Grade
#1  Joshi     7
#2  Rahul     2
#3 Sharma     7
as.data.frame(Map(`[`, registry, as.data.frame(i)))
#    Name Grade
#1  Joshi     7
#2  Rahul     2
#3 Sharma     7

how to retrieve data from data frame 1 contents that do not have in data frame 2 in Scala


Tag : scala , By : antonio
Date : March 29 2020, 07:55 AM
To fix this issue There is except function that should solve the requirement you have. just do
df1.except(df2)
+------------------------------------+------------------+
|REQ_ID                              |PRS_ID            |
+------------------------------------+------------------+
|048022cc-9c26-4c0d-a9a8-551f4a364510|999999000185298297|
|d2824085-65d3-432f-a4dd-73e31453733a|999999000185266094|
|9c642932-7a95-4bfe-ae75-687af9151fc8|990000000061356494|
|999999000185425636asdasd12321312321 |999999000185425636|
|cd66629d-14db-42df-a558-49e78c3ae320|999999000185320831|
|dc8b5731-8d1a-4394-ae9d-f74098462be4|999999000185250909|
|be1e63ce-cdf6-407d-abf3-f818e0872e92|999999000185254510|
|999999000185392677asdasd12321312321 |999999000185392677|
+------------------------------------+------------------+
df1.except(df2).dropDuplicates("REQ_ID", "PRS_ID")
df1.except(df2).dropDuplicates(Seq("REQ_ID", "PRS_ID"))
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