Importing Imputed Data from Stata into R

Software/R

Software/Stata

Data Management

Methods/Multiple Imputation

A step-by-step guide for importing multiply imputed data from Stata into R as a ‘mids’ object using the mice package.

Author

Joshua A. Goode

Published

July 24, 2025

Although I love R, you might say that Stata was my first love. Up until just a few years ago, my approach was to code everything I could in Stata and use R as a sort of Swiss Army knife for anything I couldn’t do in Stata, as well as for a few things that I just didn’t like doing in Stata (like generating plots). Although I now work almost exclusively in R, I know there are still many folks who continue to work across both packages, much as I used to.

Recently, I was asked about importing multiply imputed data from Stata to R in order to use the slcma package for the structured life course modeling approach. In this post, we’ll walk through the process together, step by step, and verify that everything worked as expected.

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Example Data

For this example, we’ll use Stata’s mheart5.dta file, which includes data on 154 fictional patients. Click below for more information about the variables in the dataset.

Show Info About Example Variables

attack: Indicator of whether the individual has suffered a heart attack (1 = Yes, 0 = No).
smokes: Smoking status of the individual (1 = Smokes, 0 = Does not smoke).
age: Age of the individual (in years).
bmi: Body mass index of the individual.
female: Indicator of sex (1 = Female, 0 = Male).
hsgrad: Indicator of whether the individual is a high school graduate (1 = Yes, 0 = No).

Imputation in Stata

Since this tutorial is not focused on imputation itself, we’ll start with an existing imputation script (imputation.do).

However, there are two important points worth mentioning:

We are storing imputed data in the flong format, which assembles all imputations (including the original and imputed datasets) together in “long” form. This means each row corresponds to one observation from one imputation dataset. Therefore, if you have \(m\) imputations and \(n\) observations, there will be \(n \times (m + 1)\) rows.
We need an ID variable to identify each case in our data, so we create a variable called idvar.

// load sample data
webuse "mheart5.dta", clear

// create ID variable
gen idvar = _n

// check missing values
misstable sum

// run imputation
mi set flong
mi register regular idvar female attack smokes hsgrad
mi register imputed bmi age
mi impute chained (regress) age bmi = attack smokes hsgrad female, add(5) rseed(1234) replace

// write to disk
save "/Users/josh/Desktop/imputed_data.dta", replace

Converting Data to `mids` Object in R

Required Packages

Going forward, we will rely on three R packages: haven, janitor, and mice. If you have not installed these packages, you’ll need to do so. Click below to see the installation code.

Show Code to Install Packages

install.packages("haven")
install.packages("janitor")
install.packages("mice")

Data Import

Let’s start by importing our data with the read_dta() function from the haven package. We’ll also pipe this to the clean_names() function from the janitor package to clean up the variable names.

To run the command, simply specify the path to your dataset that was saved from Stata. You will, of course, need to update this with your own file name and path.

imputed_data <- haven::read_dta("/Users/josh/Desktop/imputed_data.dta") |>
  janitor::clean_names()

The imported data is shown below. Notice that there are 924 rows. There were 154 rows in our original data. Because we created 5 datasets (and the unimputed data is also included), this looks correct \(\left(154 \times \left(5 + 1 \right) = 924 \right)\).

attack	smokes	age	bmi	female	hsgrad	idvar	mi_id	mi_miss	mi_m
0	0	58.46	NA	0	0	1	1	1	0
1	1	54.25	27.69	0	1	2	2	0	0
0	0	36.97	20.99	0	1	3	3	0	0
0	1	43.45	23.35	0	1	4	4	0	0
0	1	67.10	25.80	1	1	5	5	0	0
0	0	48.38	23.39	0	1	6	6	0	0
0	1	38.67	20.48	0	1	7	7	0	0
0	0	43.82	27.46	0	1	8	8	0	0
1	1	69.94	24.49	0	0	9	9	0	0
1	0	69.39	25.60	0	1	10	10	0	0
0	0	65.10	25.48	0	0	11	11	0	0
0	0	42.58	27.52	0	0	12	12	0	0
0	0	64.73	26.63	0	1	13	13	0	0
0	0	NA	NA	0	1	14	14	1	0
1	0	60.48	26.80	0	1	15	15	0	0
0	0	60.29	21.11	0	0	16	16	0	0
1	1	55.69	27.43	0	1	17	17	0	0
1	1	63.62	29.89	0	1	18	18	0	0
0	1	60.86	20.84	0	1	19	19	0	0
0	0	65.19	22.73	0	1	20	20	0	0
1	0	50.11	21.59	0	1	21	21	0	0
0	0	54.79	23.67	0	1	22	22	0	0
1	0	47.41	22.66	1	1	23	23	0	0
0	1	49.78	23.22	0	0	24	24	0	0
1	1	66.67	19.62	0	1	25	25	0	0
1	0	50.84	38.24	1	1	26	26	0	0
1	1	52.27	22.28	0	0	27	27	0	0
1	1	68.76	31.04	1	1	28	28	0	0
0	0	48.55	23.08	1	1	29	29	0	0
1	0	NA	NA	0	1	30	30	1	0
1	0	42.83	24.44	0	1	31	31	0	0
0	0	51.87	NA	0	1	32	32	1	0
0	0	NA	NA	1	1	33	33	1	0
1	1	68.13	25.12	0	0	34	34	0	0
0	0	51.13	26.82	0	1	35	35	0	0
1	0	66.58	27.01	0	1	36	36	0	0
0	0	72.25	30.75	0	0	37	37	0	0
1	0	30.09	NA	0	1	38	38	1	0
0	1	20.74	30.95	0	0	39	39	0	0
1	1	42.12	21.36	0	1	40	40	0	0
0	0	61.66	24.44	0	0	41	41	0	0
0	0	49.49	27.60	0	1	42	42	0	0
0	0	79.56	18.66	0	1	43	43	0	0
1	1	74.95	34.03	0	1	44	44	0	0
1	1	59.07	21.22	0	1	45	45	0	0
0	1	83.78	29.84	1	1	46	46	0	0
1	0	66.83	NA	0	0	47	47	1	0
0	0	59.39	18.58	1	0	48	48	0	0
1	1	68.71	22.45	1	1	49	49	0	0
1	1	77.53	25.94	1	1	50	50	0	0
0	0	49.84	32.03	1	0	51	51	0	0
1	0	NA	NA	0	1	52	52	1	0
0	0	51.56	21.46	0	1	53	53	0	0
0	0	50.74	27.81	0	0	54	54	0	0
0	1	NA	NA	0	1	55	55	1	0
1	0	37.64	23.98	0	0	56	56	0	0
1	0	46.18	NA	0	1	57	57	1	0
1	0	42.56	21.13	0	1	58	58	0	0
0	0	59.95	19.84	1	1	59	59	0	0
0	0	60.55	20.57	0	1	60	60	0	0
0	1	29.96	NA	0	1	61	61	1	0
0	0	50.08	24.23	0	1	62	62	0	0
0	1	59.52	NA	1	1	63	63	1	0
1	0	68.77	24.94	0	1	64	64	0	0
0	1	38.40	23.47	1	1	65	65	0	0
1	1	45.12	NA	0	0	66	66	1	0
1	0	53.65	20.62	1	1	67	67	0	0
0	0	NA	NA	1	0	68	68	1	0
0	0	52.45	36.01	0	1	69	69	0	0
0	0	47.76	NA	0	1	70	70	1	0
1	1	47.66	26.29	0	1	71	71	0	0
0	0	47.99	19.93	1	1	72	72	0	0
0	0	52.68	26.93	0	1	73	73	0	0
0	0	49.76	21.88	0	1	74	74	0	0
1	0	NA	NA	0	1	75	75	1	0
0	0	54.29	32.38	0	1	76	76	0	0
1	1	45.41	22.89	1	0	77	77	0	0
1	0	49.79	32.00	0	1	78	78	0	0
1	1	45.89	21.74	0	1	79	79	0	0
0	1	65.40	17.77	0	0	80	80	0	0
1	0	55.02	29.13	1	0	81	81	0	0
1	0	62.89	26.02	1	1	82	82	0	0
0	1	NA	NA	1	1	83	83	1	0
0	0	71.56	25.32	0	1	84	84	0	0
1	1	52.78	28.00	0	1	85	85	0	0
0	0	50.65	31.09	0	1	86	86	0	0
1	1	51.59	20.34	0	1	87	87	0	0
1	0	51.21	22.87	0	1	88	88	0	0
0	1	55.11	21.49	0	1	89	89	0	0
0	0	57.04	27.25	0	1	90	90	0	0
0	1	59.26	23.94	1	0	91	91	0	0
1	1	52.44	27.56	0	0	92	92	0	0
1	1	66.35	31.15	0	1	93	93	0	0
1	1	72.68	21.47	1	1	94	94	0	0
0	1	62.00	20.36	0	0	95	95	0	0
1	0	61.34	24.49	0	1	96	96	0	0
0	0	73.58	25.33	0	1	97	97	0	0
0	0	69.49	17.23	0	0	98	98	0	0
1	1	70.98	30.34	1	1	99	99	0	0
0	0	51.81	23.47	1	0	100	100	0	0
1	1	57.75	24.68	0	0	101	101	0	0
1	1	65.96	23.72	0	1	102	102	0	0
0	0	42.13	26.81	0	1	103	103	0	0
1	1	81.98	23.89	1	1	104	104	0	0
0	1	64.40	25.11	0	0	105	105	0	0
0	1	42.23	24.42	0	1	106	106	0	0
0	0	69.90	24.90	0	1	107	107	0	0
0	1	NA	NA	0	1	108	108	1	0
0	0	61.96	30.50	0	0	109	109	0	0
1	1	63.20	NA	0	1	110	110	1	0
1	1	51.04	31.34	0	1	111	111	0	0
0	0	43.06	NA	0	1	112	112	1	0
0	0	76.62	20.39	0	1	113	113	0	0
1	0	71.26	21.80	0	1	114	114	0	0
1	1	57.65	NA	1	1	115	115	1	0
1	1	53.92	23.94	1	0	116	116	0	0
0	0	47.89	20.92	1	1	117	117	0	0
1	0	79.31	30.22	0	0	118	118	0	0
1	0	41.89	29.45	0	1	119	119	0	0
1	0	68.64	27.37	1	0	120	120	0	0
1	1	53.77	29.80	0	0	121	121	0	0
1	0	61.11	18.35	0	1	122	122	0	0
0	1	57.81	NA	1	1	123	123	1	0
0	1	NA	NA	0	1	124	124	1	0
0	0	44.35	24.64	0	1	125	125	0	0
0	0	47.26	26.53	1	1	126	126	0	0
1	0	77.66	25.19	1	1	127	127	0	0
0	0	40.83	25.05	1	1	128	128	0	0
1	1	36.58	31.22	1	1	129	129	0	0
0	0	58.47	18.45	0	1	130	130	0	0
0	0	60.14	23.27	0	1	131	131	0	0
0	0	45.01	NA	0	1	132	132	1	0
0	1	66.43	23.97	0	1	133	133	0	0
0	0	44.86	25.20	0	1	134	134	0	0
1	1	62.11	29.63	0	0	135	135	0	0
0	1	42.81	32.56	0	1	136	136	0	0
1	1	68.79	30.95	0	1	137	137	0	0
0	0	NA	NA	0	0	138	138	1	0
0	0	39.60	24.63	1	1	139	139	0	0
0	1	68.51	21.97	0	1	140	140	0	0
1	1	55.04	28.30	0	1	141	141	0	0
0	0	58.47	24.47	0	0	142	142	0	0
1	1	65.16	NA	0	1	143	143	1	0
0	0	45.48	NA	1	1	144	144	1	0
1	0	51.01	30.08	0	0	145	145	0	0
1	1	67.36	25.21	0	0	146	146	0	0
0	1	NA	NA	0	1	147	147	1	0
1	0	73.62	26.22	0	1	148	148	0	0
0	0	54.74	29.80	0	1	149	149	0	0
0	0	62.59	27.61	0	1	150	150	0	0
1	1	50.92	22.08	0	1	151	151	0	0
0	0	68.98	21.65	1	1	152	152	0	0
0	0	51.87	21.88	0	1	153	153	0	0
1	1	55.15	24.40	0	1	154	154	0	0
0	0	58.46	23.74	0	0	1	1	NA	1
1	1	54.25	27.69	0	1	2	2	NA	1
0	0	36.97	20.99	0	1	3	3	NA	1
0	1	43.45	23.35	0	1	4	4	NA	1
0	1	67.10	25.80	1	1	5	5	NA	1
0	0	48.38	23.39	0	1	6	6	NA	1
0	1	38.67	20.48	0	1	7	7	NA	1
0	0	43.82	27.46	0	1	8	8	NA	1
1	1	69.94	24.49	0	0	9	9	NA	1
1	0	69.39	25.60	0	1	10	10	NA	1
0	0	65.10	25.48	0	0	11	11	NA	1
0	0	42.58	27.52	0	0	12	12	NA	1
0	0	64.73	26.63	0	1	13	13	NA	1
0	0	68.44	16.52	0	1	14	14	NA	1
1	0	60.48	26.80	0	1	15	15	NA	1
0	0	60.29	21.11	0	0	16	16	NA	1
1	1	55.69	27.43	0	1	17	17	NA	1
1	1	63.62	29.89	0	1	18	18	NA	1
0	1	60.86	20.84	0	1	19	19	NA	1
0	0	65.19	22.73	0	1	20	20	NA	1
1	0	50.11	21.59	0	1	21	21	NA	1
0	0	54.79	23.67	0	1	22	22	NA	1
1	0	47.41	22.66	1	1	23	23	NA	1
0	1	49.78	23.22	0	0	24	24	NA	1
1	1	66.67	19.62	0	1	25	25	NA	1
1	0	50.84	38.24	1	1	26	26	NA	1
1	1	52.27	22.28	0	0	27	27	NA	1
1	1	68.76	31.04	1	1	28	28	NA	1
0	0	48.55	23.08	1	1	29	29	NA	1
1	0	48.49	22.55	0	1	30	30	NA	1
1	0	42.83	24.44	0	1	31	31	NA	1
0	0	51.87	19.91	0	1	32	32	NA	1
0	0	56.87	29.91	1	1	33	33	NA	1
1	1	68.13	25.12	0	0	34	34	NA	1
0	0	51.13	26.82	0	1	35	35	NA	1
1	0	66.58	27.01	0	1	36	36	NA	1
0	0	72.25	30.75	0	0	37	37	NA	1
1	0	30.09	26.95	0	1	38	38	NA	1
0	1	20.74	30.95	0	0	39	39	NA	1
1	1	42.12	21.36	0	1	40	40	NA	1
0	0	61.66	24.44	0	0	41	41	NA	1
0	0	49.49	27.60	0	1	42	42	NA	1
0	0	79.56	18.66	0	1	43	43	NA	1
1	1	74.95	34.03	0	1	44	44	NA	1
1	1	59.07	21.22	0	1	45	45	NA	1
0	1	83.78	29.84	1	1	46	46	NA	1
1	0	66.83	29.49	0	0	47	47	NA	1
0	0	59.39	18.58	1	0	48	48	NA	1
1	1	68.71	22.45	1	1	49	49	NA	1
1	1	77.53	25.94	1	1	50	50	NA	1
0	0	49.84	32.03	1	0	51	51	NA	1
1	0	30.83	23.65	0	1	52	52	NA	1
0	0	51.56	21.46	0	1	53	53	NA	1
0	0	50.74	27.81	0	0	54	54	NA	1
0	1	45.78	24.02	0	1	55	55	NA	1
1	0	37.64	23.98	0	0	56	56	NA	1
1	0	46.18	29.98	0	1	57	57	NA	1
1	0	42.56	21.13	0	1	58	58	NA	1
0	0	59.95	19.84	1	1	59	59	NA	1
0	0	60.55	20.57	0	1	60	60	NA	1
0	1	29.96	24.72	0	1	61	61	NA	1
0	0	50.08	24.23	0	1	62	62	NA	1
0	1	59.52	29.92	1	1	63	63	NA	1
1	0	68.77	24.94	0	1	64	64	NA	1
0	1	38.40	23.47	1	1	65	65	NA	1
1	1	45.12	18.86	0	0	66	66	NA	1
1	0	53.65	20.62	1	1	67	67	NA	1
0	0	56.16	25.63	1	0	68	68	NA	1
0	0	52.45	36.01	0	1	69	69	NA	1
0	0	47.76	25.52	0	1	70	70	NA	1
1	1	47.66	26.29	0	1	71	71	NA	1
0	0	47.99	19.93	1	1	72	72	NA	1
0	0	52.68	26.93	0	1	73	73	NA	1
0	0	49.76	21.88	0	1	74	74	NA	1
1	0	60.37	18.67	0	1	75	75	NA	1
0	0	54.29	32.38	0	1	76	76	NA	1
1	1	45.41	22.89	1	0	77	77	NA	1
1	0	49.79	32.00	0	1	78	78	NA	1
1	1	45.89	21.74	0	1	79	79	NA	1
0	1	65.40	17.77	0	0	80	80	NA	1
1	0	55.02	29.13	1	0	81	81	NA	1
1	0	62.89	26.02	1	1	82	82	NA	1
0	1	55.84	24.54	1	1	83	83	NA	1
0	0	71.56	25.32	0	1	84	84	NA	1
1	1	52.78	28.00	0	1	85	85	NA	1
0	0	50.65	31.09	0	1	86	86	NA	1
1	1	51.59	20.34	0	1	87	87	NA	1
1	0	51.21	22.87	0	1	88	88	NA	1
0	1	55.11	21.49	0	1	89	89	NA	1
0	0	57.04	27.25	0	1	90	90	NA	1
0	1	59.26	23.94	1	0	91	91	NA	1
1	1	52.44	27.56	0	0	92	92	NA	1
1	1	66.35	31.15	0	1	93	93	NA	1
1	1	72.68	21.47	1	1	94	94	NA	1
0	1	62.00	20.36	0	0	95	95	NA	1
1	0	61.34	24.49	0	1	96	96	NA	1
0	0	73.58	25.33	0	1	97	97	NA	1
0	0	69.49	17.23	0	0	98	98	NA	1
1	1	70.98	30.34	1	1	99	99	NA	1
0	0	51.81	23.47	1	0	100	100	NA	1
1	1	57.75	24.68	0	0	101	101	NA	1
1	1	65.96	23.72	0	1	102	102	NA	1
0	0	42.13	26.81	0	1	103	103	NA	1
1	1	81.98	23.89	1	1	104	104	NA	1
0	1	64.40	25.11	0	0	105	105	NA	1
0	1	42.23	24.42	0	1	106	106	NA	1
0	0	69.90	24.90	0	1	107	107	NA	1
0	1	59.85	25.57	0	1	108	108	NA	1
0	0	61.96	30.50	0	0	109	109	NA	1
1	1	63.20	14.80	0	1	110	110	NA	1
1	1	51.04	31.34	0	1	111	111	NA	1
0	0	43.06	20.67	0	1	112	112	NA	1
0	0	76.62	20.39	0	1	113	113	NA	1
1	0	71.26	21.80	0	1	114	114	NA	1
1	1	57.65	29.90	1	1	115	115	NA	1
1	1	53.92	23.94	1	0	116	116	NA	1
0	0	47.89	20.92	1	1	117	117	NA	1
1	0	79.31	30.22	0	0	118	118	NA	1
1	0	41.89	29.45	0	1	119	119	NA	1
1	0	68.64	27.37	1	0	120	120	NA	1
1	1	53.77	29.80	0	0	121	121	NA	1
1	0	61.11	18.35	0	1	122	122	NA	1
0	1	57.81	25.77	1	1	123	123	NA	1
0	1	45.27	22.93	0	1	124	124	NA	1
0	0	44.35	24.64	0	1	125	125	NA	1
0	0	47.26	26.53	1	1	126	126	NA	1
1	0	77.66	25.19	1	1	127	127	NA	1
0	0	40.83	25.05	1	1	128	128	NA	1
1	1	36.58	31.22	1	1	129	129	NA	1
0	0	58.47	18.45	0	1	130	130	NA	1
0	0	60.14	23.27	0	1	131	131	NA	1
0	0	45.01	25.68	0	1	132	132	NA	1
0	1	66.43	23.97	0	1	133	133	NA	1
0	0	44.86	25.20	0	1	134	134	NA	1
1	1	62.11	29.63	0	0	135	135	NA	1
0	1	42.81	32.56	0	1	136	136	NA	1
1	1	68.79	30.95	0	1	137	137	NA	1
0	0	58.50	27.28	0	0	138	138	NA	1
0	0	39.60	24.63	1	1	139	139	NA	1
0	1	68.51	21.97	0	1	140	140	NA	1
1	1	55.04	28.30	0	1	141	141	NA	1
0	0	58.47	24.47	0	0	142	142	NA	1
1	1	65.16	21.97	0	1	143	143	NA	1
0	0	45.48	19.24	1	1	144	144	NA	1
1	0	51.01	30.08	0	0	145	145	NA	1
1	1	67.36	25.21	0	0	146	146	NA	1
0	1	80.90	23.33	0	1	147	147	NA	1
1	0	73.62	26.22	0	1	148	148	NA	1
0	0	54.74	29.80	0	1	149	149	NA	1
0	0	62.59	27.61	0	1	150	150	NA	1
1	1	50.92	22.08	0	1	151	151	NA	1
0	0	68.98	21.65	1	1	152	152	NA	1
0	0	51.87	21.88	0	1	153	153	NA	1
1	1	55.15	24.40	0	1	154	154	NA	1
0	0	58.46	22.83	0	0	1	1	NA	2
1	1	54.25	27.69	0	1	2	2	NA	2
0	0	36.97	20.99	0	1	3	3	NA	2
0	1	43.45	23.35	0	1	4	4	NA	2
0	1	67.10	25.80	1	1	5	5	NA	2
0	0	48.38	23.39	0	1	6	6	NA	2
0	1	38.67	20.48	0	1	7	7	NA	2
0	0	43.82	27.46	0	1	8	8	NA	2
1	1	69.94	24.49	0	0	9	9	NA	2
1	0	69.39	25.60	0	1	10	10	NA	2
0	0	65.10	25.48	0	0	11	11	NA	2
0	0	42.58	27.52	0	0	12	12	NA	2
0	0	64.73	26.63	0	1	13	13	NA	2
0	0	59.21	24.99	0	1	14	14	NA	2
1	0	60.48	26.80	0	1	15	15	NA	2
0	0	60.29	21.11	0	0	16	16	NA	2
1	1	55.69	27.43	0	1	17	17	NA	2
1	1	63.62	29.89	0	1	18	18	NA	2
0	1	60.86	20.84	0	1	19	19	NA	2
0	0	65.19	22.73	0	1	20	20	NA	2
1	0	50.11	21.59	0	1	21	21	NA	2
0	0	54.79	23.67	0	1	22	22	NA	2
1	0	47.41	22.66	1	1	23	23	NA	2
0	1	49.78	23.22	0	0	24	24	NA	2
1	1	66.67	19.62	0	1	25	25	NA	2
1	0	50.84	38.24	1	1	26	26	NA	2
1	1	52.27	22.28	0	0	27	27	NA	2
1	1	68.76	31.04	1	1	28	28	NA	2
0	0	48.55	23.08	1	1	29	29	NA	2
1	0	43.65	26.72	0	1	30	30	NA	2
1	0	42.83	24.44	0	1	31	31	NA	2
0	0	51.87	21.00	0	1	32	32	NA	2
0	0	46.46	23.04	1	1	33	33	NA	2
1	1	68.13	25.12	0	0	34	34	NA	2
0	0	51.13	26.82	0	1	35	35	NA	2
1	0	66.58	27.01	0	1	36	36	NA	2
0	0	72.25	30.75	0	0	37	37	NA	2
1	0	30.09	25.22	0	1	38	38	NA	2
0	1	20.74	30.95	0	0	39	39	NA	2
1	1	42.12	21.36	0	1	40	40	NA	2
0	0	61.66	24.44	0	0	41	41	NA	2
0	0	49.49	27.60	0	1	42	42	NA	2
0	0	79.56	18.66	0	1	43	43	NA	2
1	1	74.95	34.03	0	1	44	44	NA	2
1	1	59.07	21.22	0	1	45	45	NA	2
0	1	83.78	29.84	1	1	46	46	NA	2
1	0	66.83	25.17	0	0	47	47	NA	2
0	0	59.39	18.58	1	0	48	48	NA	2
1	1	68.71	22.45	1	1	49	49	NA	2
1	1	77.53	25.94	1	1	50	50	NA	2
0	0	49.84	32.03	1	0	51	51	NA	2
1	0	76.96	28.69	0	1	52	52	NA	2
0	0	51.56	21.46	0	1	53	53	NA	2
0	0	50.74	27.81	0	0	54	54	NA	2
0	1	43.09	22.27	0	1	55	55	NA	2
1	0	37.64	23.98	0	0	56	56	NA	2
1	0	46.18	18.40	0	1	57	57	NA	2
1	0	42.56	21.13	0	1	58	58	NA	2
0	0	59.95	19.84	1	1	59	59	NA	2
0	0	60.55	20.57	0	1	60	60	NA	2
0	1	29.96	25.28	0	1	61	61	NA	2
0	0	50.08	24.23	0	1	62	62	NA	2
0	1	59.52	16.72	1	1	63	63	NA	2
1	0	68.77	24.94	0	1	64	64	NA	2
0	1	38.40	23.47	1	1	65	65	NA	2
1	1	45.12	25.47	0	0	66	66	NA	2
1	0	53.65	20.62	1	1	67	67	NA	2
0	0	60.44	23.49	1	0	68	68	NA	2
0	0	52.45	36.01	0	1	69	69	NA	2
0	0	47.76	24.47	0	1	70	70	NA	2
1	1	47.66	26.29	0	1	71	71	NA	2
0	0	47.99	19.93	1	1	72	72	NA	2
0	0	52.68	26.93	0	1	73	73	NA	2
0	0	49.76	21.88	0	1	74	74	NA	2
1	0	58.15	27.84	0	1	75	75	NA	2
0	0	54.29	32.38	0	1	76	76	NA	2
1	1	45.41	22.89	1	0	77	77	NA	2
1	0	49.79	32.00	0	1	78	78	NA	2
1	1	45.89	21.74	0	1	79	79	NA	2
0	1	65.40	17.77	0	0	80	80	NA	2
1	0	55.02	29.13	1	0	81	81	NA	2
1	0	62.89	26.02	1	1	82	82	NA	2
0	1	72.68	19.33	1	1	83	83	NA	2
0	0	71.56	25.32	0	1	84	84	NA	2
1	1	52.78	28.00	0	1	85	85	NA	2
0	0	50.65	31.09	0	1	86	86	NA	2
1	1	51.59	20.34	0	1	87	87	NA	2
1	0	51.21	22.87	0	1	88	88	NA	2
0	1	55.11	21.49	0	1	89	89	NA	2
0	0	57.04	27.25	0	1	90	90	NA	2
0	1	59.26	23.94	1	0	91	91	NA	2
1	1	52.44	27.56	0	0	92	92	NA	2
1	1	66.35	31.15	0	1	93	93	NA	2
1	1	72.68	21.47	1	1	94	94	NA	2
0	1	62.00	20.36	0	0	95	95	NA	2
1	0	61.34	24.49	0	1	96	96	NA	2
0	0	73.58	25.33	0	1	97	97	NA	2
0	0	69.49	17.23	0	0	98	98	NA	2
1	1	70.98	30.34	1	1	99	99	NA	2
0	0	51.81	23.47	1	0	100	100	NA	2
1	1	57.75	24.68	0	0	101	101	NA	2
1	1	65.96	23.72	0	1	102	102	NA	2
0	0	42.13	26.81	0	1	103	103	NA	2
1	1	81.98	23.89	1	1	104	104	NA	2
0	1	64.40	25.11	0	0	105	105	NA	2
0	1	42.23	24.42	0	1	106	106	NA	2
0	0	69.90	24.90	0	1	107	107	NA	2
0	1	48.00	22.69	0	1	108	108	NA	2
0	0	61.96	30.50	0	0	109	109	NA	2
1	1	63.20	23.95	0	1	110	110	NA	2
1	1	51.04	31.34	0	1	111	111	NA	2
0	0	43.06	21.56	0	1	112	112	NA	2
0	0	76.62	20.39	0	1	113	113	NA	2
1	0	71.26	21.80	0	1	114	114	NA	2
1	1	57.65	32.49	1	1	115	115	NA	2
1	1	53.92	23.94	1	0	116	116	NA	2
0	0	47.89	20.92	1	1	117	117	NA	2
1	0	79.31	30.22	0	0	118	118	NA	2
1	0	41.89	29.45	0	1	119	119	NA	2
1	0	68.64	27.37	1	0	120	120	NA	2
1	1	53.77	29.80	0	0	121	121	NA	2
1	0	61.11	18.35	0	1	122	122	NA	2
0	1	57.81	29.63	1	1	123	123	NA	2
0	1	50.37	26.25	0	1	124	124	NA	2
0	0	44.35	24.64	0	1	125	125	NA	2
0	0	47.26	26.53	1	1	126	126	NA	2
1	0	77.66	25.19	1	1	127	127	NA	2
0	0	40.83	25.05	1	1	128	128	NA	2
1	1	36.58	31.22	1	1	129	129	NA	2
0	0	58.47	18.45	0	1	130	130	NA	2
0	0	60.14	23.27	0	1	131	131	NA	2
0	0	45.01	29.32	0	1	132	132	NA	2
0	1	66.43	23.97	0	1	133	133	NA	2
0	0	44.86	25.20	0	1	134	134	NA	2
1	1	62.11	29.63	0	0	135	135	NA	2
0	1	42.81	32.56	0	1	136	136	NA	2
1	1	68.79	30.95	0	1	137	137	NA	2
0	0	46.19	35.60	0	0	138	138	NA	2
0	0	39.60	24.63	1	1	139	139	NA	2
0	1	68.51	21.97	0	1	140	140	NA	2
1	1	55.04	28.30	0	1	141	141	NA	2
0	0	58.47	24.47	0	0	142	142	NA	2
1	1	65.16	23.67	0	1	143	143	NA	2
0	0	45.48	32.25	1	1	144	144	NA	2
1	0	51.01	30.08	0	0	145	145	NA	2
1	1	67.36	25.21	0	0	146	146	NA	2
0	1	46.63	24.43	0	1	147	147	NA	2
1	0	73.62	26.22	0	1	148	148	NA	2
0	0	54.74	29.80	0	1	149	149	NA	2
0	0	62.59	27.61	0	1	150	150	NA	2
1	1	50.92	22.08	0	1	151	151	NA	2
0	0	68.98	21.65	1	1	152	152	NA	2
0	0	51.87	21.88	0	1	153	153	NA	2
1	1	55.15	24.40	0	1	154	154	NA	2
0	0	58.46	22.13	0	0	1	1	NA	3
1	1	54.25	27.69	0	1	2	2	NA	3
0	0	36.97	20.99	0	1	3	3	NA	3
0	1	43.45	23.35	0	1	4	4	NA	3
0	1	67.10	25.80	1	1	5	5	NA	3
0	0	48.38	23.39	0	1	6	6	NA	3
0	1	38.67	20.48	0	1	7	7	NA	3
0	0	43.82	27.46	0	1	8	8	NA	3
1	1	69.94	24.49	0	0	9	9	NA	3
1	0	69.39	25.60	0	1	10	10	NA	3
0	0	65.10	25.48	0	0	11	11	NA	3
0	0	42.58	27.52	0	0	12	12	NA	3
0	0	64.73	26.63	0	1	13	13	NA	3
0	0	31.30	23.80	0	1	14	14	NA	3
1	0	60.48	26.80	0	1	15	15	NA	3
0	0	60.29	21.11	0	0	16	16	NA	3
1	1	55.69	27.43	0	1	17	17	NA	3
1	1	63.62	29.89	0	1	18	18	NA	3
0	1	60.86	20.84	0	1	19	19	NA	3
0	0	65.19	22.73	0	1	20	20	NA	3
1	0	50.11	21.59	0	1	21	21	NA	3
0	0	54.79	23.67	0	1	22	22	NA	3
1	0	47.41	22.66	1	1	23	23	NA	3
0	1	49.78	23.22	0	0	24	24	NA	3
1	1	66.67	19.62	0	1	25	25	NA	3
1	0	50.84	38.24	1	1	26	26	NA	3
1	1	52.27	22.28	0	0	27	27	NA	3
1	1	68.76	31.04	1	1	28	28	NA	3
0	0	48.55	23.08	1	1	29	29	NA	3
1	0	70.47	17.53	0	1	30	30	NA	3
1	0	42.83	24.44	0	1	31	31	NA	3
0	0	51.87	21.71	0	1	32	32	NA	3
0	0	65.37	18.29	1	1	33	33	NA	3
1	1	68.13	25.12	0	0	34	34	NA	3
0	0	51.13	26.82	0	1	35	35	NA	3
1	0	66.58	27.01	0	1	36	36	NA	3
0	0	72.25	30.75	0	0	37	37	NA	3
1	0	30.09	29.01	0	1	38	38	NA	3
0	1	20.74	30.95	0	0	39	39	NA	3
1	1	42.12	21.36	0	1	40	40	NA	3
0	0	61.66	24.44	0	0	41	41	NA	3
0	0	49.49	27.60	0	1	42	42	NA	3
0	0	79.56	18.66	0	1	43	43	NA	3
1	1	74.95	34.03	0	1	44	44	NA	3
1	1	59.07	21.22	0	1	45	45	NA	3
0	1	83.78	29.84	1	1	46	46	NA	3
1	0	66.83	26.92	0	0	47	47	NA	3
0	0	59.39	18.58	1	0	48	48	NA	3
1	1	68.71	22.45	1	1	49	49	NA	3
1	1	77.53	25.94	1	1	50	50	NA	3
0	0	49.84	32.03	1	0	51	51	NA	3
1	0	52.84	34.43	0	1	52	52	NA	3
0	0	51.56	21.46	0	1	53	53	NA	3
0	0	50.74	27.81	0	0	54	54	NA	3
0	1	38.75	21.12	0	1	55	55	NA	3
1	0	37.64	23.98	0	0	56	56	NA	3
1	0	46.18	22.58	0	1	57	57	NA	3
1	0	42.56	21.13	0	1	58	58	NA	3
0	0	59.95	19.84	1	1	59	59	NA	3
0	0	60.55	20.57	0	1	60	60	NA	3
0	1	29.96	27.02	0	1	61	61	NA	3
0	0	50.08	24.23	0	1	62	62	NA	3
0	1	59.52	21.10	1	1	63	63	NA	3
1	0	68.77	24.94	0	1	64	64	NA	3
0	1	38.40	23.47	1	1	65	65	NA	3
1	1	45.12	23.55	0	0	66	66	NA	3
1	0	53.65	20.62	1	1	67	67	NA	3
0	0	55.10	22.28	1	0	68	68	NA	3
0	0	52.45	36.01	0	1	69	69	NA	3
0	0	47.76	28.40	0	1	70	70	NA	3
1	1	47.66	26.29	0	1	71	71	NA	3
0	0	47.99	19.93	1	1	72	72	NA	3
0	0	52.68	26.93	0	1	73	73	NA	3
0	0	49.76	21.88	0	1	74	74	NA	3
1	0	87.54	29.23	0	1	75	75	NA	3
0	0	54.29	32.38	0	1	76	76	NA	3
1	1	45.41	22.89	1	0	77	77	NA	3
1	0	49.79	32.00	0	1	78	78	NA	3
1	1	45.89	21.74	0	1	79	79	NA	3
0	1	65.40	17.77	0	0	80	80	NA	3
1	0	55.02	29.13	1	0	81	81	NA	3
1	0	62.89	26.02	1	1	82	82	NA	3
0	1	46.34	22.85	1	1	83	83	NA	3
0	0	71.56	25.32	0	1	84	84	NA	3
1	1	52.78	28.00	0	1	85	85	NA	3
0	0	50.65	31.09	0	1	86	86	NA	3
1	1	51.59	20.34	0	1	87	87	NA	3
1	0	51.21	22.87	0	1	88	88	NA	3
0	1	55.11	21.49	0	1	89	89	NA	3
0	0	57.04	27.25	0	1	90	90	NA	3
0	1	59.26	23.94	1	0	91	91	NA	3
1	1	52.44	27.56	0	0	92	92	NA	3
1	1	66.35	31.15	0	1	93	93	NA	3
1	1	72.68	21.47	1	1	94	94	NA	3
0	1	62.00	20.36	0	0	95	95	NA	3
1	0	61.34	24.49	0	1	96	96	NA	3
0	0	73.58	25.33	0	1	97	97	NA	3
0	0	69.49	17.23	0	0	98	98	NA	3
1	1	70.98	30.34	1	1	99	99	NA	3
0	0	51.81	23.47	1	0	100	100	NA	3
1	1	57.75	24.68	0	0	101	101	NA	3
1	1	65.96	23.72	0	1	102	102	NA	3
0	0	42.13	26.81	0	1	103	103	NA	3
1	1	81.98	23.89	1	1	104	104	NA	3
0	1	64.40	25.11	0	0	105	105	NA	3
0	1	42.23	24.42	0	1	106	106	NA	3
0	0	69.90	24.90	0	1	107	107	NA	3
0	1	58.94	27.67	0	1	108	108	NA	3
0	0	61.96	30.50	0	0	109	109	NA	3
1	1	63.20	26.23	0	1	110	110	NA	3
1	1	51.04	31.34	0	1	111	111	NA	3
0	0	43.06	29.39	0	1	112	112	NA	3
0	0	76.62	20.39	0	1	113	113	NA	3
1	0	71.26	21.80	0	1	114	114	NA	3
1	1	57.65	31.36	1	1	115	115	NA	3
1	1	53.92	23.94	1	0	116	116	NA	3
0	0	47.89	20.92	1	1	117	117	NA	3
1	0	79.31	30.22	0	0	118	118	NA	3
1	0	41.89	29.45	0	1	119	119	NA	3
1	0	68.64	27.37	1	0	120	120	NA	3
1	1	53.77	29.80	0	0	121	121	NA	3
1	0	61.11	18.35	0	1	122	122	NA	3
0	1	57.81	21.38	1	1	123	123	NA	3
0	1	75.94	22.16	0	1	124	124	NA	3
0	0	44.35	24.64	0	1	125	125	NA	3
0	0	47.26	26.53	1	1	126	126	NA	3
1	0	77.66	25.19	1	1	127	127	NA	3
0	0	40.83	25.05	1	1	128	128	NA	3
1	1	36.58	31.22	1	1	129	129	NA	3
0	0	58.47	18.45	0	1	130	130	NA	3
0	0	60.14	23.27	0	1	131	131	NA	3
0	0	45.01	24.42	0	1	132	132	NA	3
0	1	66.43	23.97	0	1	133	133	NA	3
0	0	44.86	25.20	0	1	134	134	NA	3
1	1	62.11	29.63	0	0	135	135	NA	3
0	1	42.81	32.56	0	1	136	136	NA	3
1	1	68.79	30.95	0	1	137	137	NA	3
0	0	59.36	25.69	0	0	138	138	NA	3
0	0	39.60	24.63	1	1	139	139	NA	3
0	1	68.51	21.97	0	1	140	140	NA	3
1	1	55.04	28.30	0	1	141	141	NA	3
0	0	58.47	24.47	0	0	142	142	NA	3
1	1	65.16	23.51	0	1	143	143	NA	3
0	0	45.48	22.54	1	1	144	144	NA	3
1	0	51.01	30.08	0	0	145	145	NA	3
1	1	67.36	25.21	0	0	146	146	NA	3
0	1	86.16	25.17	0	1	147	147	NA	3
1	0	73.62	26.22	0	1	148	148	NA	3
0	0	54.74	29.80	0	1	149	149	NA	3
0	0	62.59	27.61	0	1	150	150	NA	3
1	1	50.92	22.08	0	1	151	151	NA	3
0	0	68.98	21.65	1	1	152	152	NA	3
0	0	51.87	21.88	0	1	153	153	NA	3
1	1	55.15	24.40	0	1	154	154	NA	3
0	0	58.46	22.44	0	0	1	1	NA	4
1	1	54.25	27.69	0	1	2	2	NA	4
0	0	36.97	20.99	0	1	3	3	NA	4
0	1	43.45	23.35	0	1	4	4	NA	4
0	1	67.10	25.80	1	1	5	5	NA	4
0	0	48.38	23.39	0	1	6	6	NA	4
0	1	38.67	20.48	0	1	7	7	NA	4
0	0	43.82	27.46	0	1	8	8	NA	4
1	1	69.94	24.49	0	0	9	9	NA	4
1	0	69.39	25.60	0	1	10	10	NA	4
0	0	65.10	25.48	0	0	11	11	NA	4
0	0	42.58	27.52	0	0	12	12	NA	4
0	0	64.73	26.63	0	1	13	13	NA	4
0	0	38.66	20.46	0	1	14	14	NA	4
1	0	60.48	26.80	0	1	15	15	NA	4
0	0	60.29	21.11	0	0	16	16	NA	4
1	1	55.69	27.43	0	1	17	17	NA	4
1	1	63.62	29.89	0	1	18	18	NA	4
0	1	60.86	20.84	0	1	19	19	NA	4
0	0	65.19	22.73	0	1	20	20	NA	4
1	0	50.11	21.59	0	1	21	21	NA	4
0	0	54.79	23.67	0	1	22	22	NA	4
1	0	47.41	22.66	1	1	23	23	NA	4
0	1	49.78	23.22	0	0	24	24	NA	4
1	1	66.67	19.62	0	1	25	25	NA	4
1	0	50.84	38.24	1	1	26	26	NA	4
1	1	52.27	22.28	0	0	27	27	NA	4
1	1	68.76	31.04	1	1	28	28	NA	4
0	0	48.55	23.08	1	1	29	29	NA	4
1	0	66.07	25.06	0	1	30	30	NA	4
1	0	42.83	24.44	0	1	31	31	NA	4
0	0	51.87	18.44	0	1	32	32	NA	4
0	0	49.85	28.29	1	1	33	33	NA	4
1	1	68.13	25.12	0	0	34	34	NA	4
0	0	51.13	26.82	0	1	35	35	NA	4
1	0	66.58	27.01	0	1	36	36	NA	4
0	0	72.25	30.75	0	0	37	37	NA	4
1	0	30.09	21.25	0	1	38	38	NA	4
0	1	20.74	30.95	0	0	39	39	NA	4
1	1	42.12	21.36	0	1	40	40	NA	4
0	0	61.66	24.44	0	0	41	41	NA	4
0	0	49.49	27.60	0	1	42	42	NA	4
0	0	79.56	18.66	0	1	43	43	NA	4
1	1	74.95	34.03	0	1	44	44	NA	4
1	1	59.07	21.22	0	1	45	45	NA	4
0	1	83.78	29.84	1	1	46	46	NA	4
1	0	66.83	29.65	0	0	47	47	NA	4
0	0	59.39	18.58	1	0	48	48	NA	4
1	1	68.71	22.45	1	1	49	49	NA	4
1	1	77.53	25.94	1	1	50	50	NA	4
0	0	49.84	32.03	1	0	51	51	NA	4
1	0	72.36	24.09	0	1	52	52	NA	4
0	0	51.56	21.46	0	1	53	53	NA	4
0	0	50.74	27.81	0	0	54	54	NA	4
0	1	26.60	30.84	0	1	55	55	NA	4
1	0	37.64	23.98	0	0	56	56	NA	4
1	0	46.18	26.24	0	1	57	57	NA	4
1	0	42.56	21.13	0	1	58	58	NA	4
0	0	59.95	19.84	1	1	59	59	NA	4
0	0	60.55	20.57	0	1	60	60	NA	4
0	1	29.96	24.77	0	1	61	61	NA	4
0	0	50.08	24.23	0	1	62	62	NA	4
0	1	59.52	18.64	1	1	63	63	NA	4
1	0	68.77	24.94	0	1	64	64	NA	4
0	1	38.40	23.47	1	1	65	65	NA	4
1	1	45.12	26.13	0	0	66	66	NA	4
1	0	53.65	20.62	1	1	67	67	NA	4
0	0	64.51	32.66	1	0	68	68	NA	4
0	0	52.45	36.01	0	1	69	69	NA	4
0	0	47.76	20.24	0	1	70	70	NA	4
1	1	47.66	26.29	0	1	71	71	NA	4
0	0	47.99	19.93	1	1	72	72	NA	4
0	0	52.68	26.93	0	1	73	73	NA	4
0	0	49.76	21.88	0	1	74	74	NA	4
1	0	68.33	21.01	0	1	75	75	NA	4
0	0	54.29	32.38	0	1	76	76	NA	4
1	1	45.41	22.89	1	0	77	77	NA	4
1	0	49.79	32.00	0	1	78	78	NA	4
1	1	45.89	21.74	0	1	79	79	NA	4
0	1	65.40	17.77	0	0	80	80	NA	4
1	0	55.02	29.13	1	0	81	81	NA	4
1	0	62.89	26.02	1	1	82	82	NA	4
0	1	71.41	23.34	1	1	83	83	NA	4
0	0	71.56	25.32	0	1	84	84	NA	4
1	1	52.78	28.00	0	1	85	85	NA	4
0	0	50.65	31.09	0	1	86	86	NA	4
1	1	51.59	20.34	0	1	87	87	NA	4
1	0	51.21	22.87	0	1	88	88	NA	4
0	1	55.11	21.49	0	1	89	89	NA	4
0	0	57.04	27.25	0	1	90	90	NA	4
0	1	59.26	23.94	1	0	91	91	NA	4
1	1	52.44	27.56	0	0	92	92	NA	4
1	1	66.35	31.15	0	1	93	93	NA	4
1	1	72.68	21.47	1	1	94	94	NA	4
0	1	62.00	20.36	0	0	95	95	NA	4
1	0	61.34	24.49	0	1	96	96	NA	4
0	0	73.58	25.33	0	1	97	97	NA	4
0	0	69.49	17.23	0	0	98	98	NA	4
1	1	70.98	30.34	1	1	99	99	NA	4
0	0	51.81	23.47	1	0	100	100	NA	4
1	1	57.75	24.68	0	0	101	101	NA	4
1	1	65.96	23.72	0	1	102	102	NA	4
0	0	42.13	26.81	0	1	103	103	NA	4
1	1	81.98	23.89	1	1	104	104	NA	4
0	1	64.40	25.11	0	0	105	105	NA	4
0	1	42.23	24.42	0	1	106	106	NA	4
0	0	69.90	24.90	0	1	107	107	NA	4
0	1	40.20	19.67	0	1	108	108	NA	4
0	0	61.96	30.50	0	0	109	109	NA	4
1	1	63.20	24.34	0	1	110	110	NA	4
1	1	51.04	31.34	0	1	111	111	NA	4
0	0	43.06	28.15	0	1	112	112	NA	4
0	0	76.62	20.39	0	1	113	113	NA	4
1	0	71.26	21.80	0	1	114	114	NA	4
1	1	57.65	20.03	1	1	115	115	NA	4
1	1	53.92	23.94	1	0	116	116	NA	4
0	0	47.89	20.92	1	1	117	117	NA	4
1	0	79.31	30.22	0	0	118	118	NA	4
1	0	41.89	29.45	0	1	119	119	NA	4
1	0	68.64	27.37	1	0	120	120	NA	4
1	1	53.77	29.80	0	0	121	121	NA	4
1	0	61.11	18.35	0	1	122	122	NA	4
0	1	57.81	27.51	1	1	123	123	NA	4
0	1	62.97	28.51	0	1	124	124	NA	4
0	0	44.35	24.64	0	1	125	125	NA	4
0	0	47.26	26.53	1	1	126	126	NA	4
1	0	77.66	25.19	1	1	127	127	NA	4
0	0	40.83	25.05	1	1	128	128	NA	4
1	1	36.58	31.22	1	1	129	129	NA	4
0	0	58.47	18.45	0	1	130	130	NA	4
0	0	60.14	23.27	0	1	131	131	NA	4
0	0	45.01	14.48	0	1	132	132	NA	4
0	1	66.43	23.97	0	1	133	133	NA	4
0	0	44.86	25.20	0	1	134	134	NA	4
1	1	62.11	29.63	0	0	135	135	NA	4
0	1	42.81	32.56	0	1	136	136	NA	4
1	1	68.79	30.95	0	1	137	137	NA	4
0	0	51.76	24.11	0	0	138	138	NA	4
0	0	39.60	24.63	1	1	139	139	NA	4
0	1	68.51	21.97	0	1	140	140	NA	4
1	1	55.04	28.30	0	1	141	141	NA	4
0	0	58.47	24.47	0	0	142	142	NA	4
1	1	65.16	32.87	0	1	143	143	NA	4
0	0	45.48	26.75	1	1	144	144	NA	4
1	0	51.01	30.08	0	0	145	145	NA	4
1	1	67.36	25.21	0	0	146	146	NA	4
0	1	63.68	33.05	0	1	147	147	NA	4
1	0	73.62	26.22	0	1	148	148	NA	4
0	0	54.74	29.80	0	1	149	149	NA	4
0	0	62.59	27.61	0	1	150	150	NA	4
1	1	50.92	22.08	0	1	151	151	NA	4
0	0	68.98	21.65	1	1	152	152	NA	4
0	0	51.87	21.88	0	1	153	153	NA	4
1	1	55.15	24.40	0	1	154	154	NA	4
0	0	58.46	23.15	0	0	1	1	NA	5
1	1	54.25	27.69	0	1	2	2	NA	5
0	0	36.97	20.99	0	1	3	3	NA	5
0	1	43.45	23.35	0	1	4	4	NA	5
0	1	67.10	25.80	1	1	5	5	NA	5
0	0	48.38	23.39	0	1	6	6	NA	5
0	1	38.67	20.48	0	1	7	7	NA	5
0	0	43.82	27.46	0	1	8	8	NA	5
1	1	69.94	24.49	0	0	9	9	NA	5
1	0	69.39	25.60	0	1	10	10	NA	5
0	0	65.10	25.48	0	0	11	11	NA	5
0	0	42.58	27.52	0	0	12	12	NA	5
0	0	64.73	26.63	0	1	13	13	NA	5
0	0	51.04	25.51	0	1	14	14	NA	5
1	0	60.48	26.80	0	1	15	15	NA	5
0	0	60.29	21.11	0	0	16	16	NA	5
1	1	55.69	27.43	0	1	17	17	NA	5
1	1	63.62	29.89	0	1	18	18	NA	5
0	1	60.86	20.84	0	1	19	19	NA	5
0	0	65.19	22.73	0	1	20	20	NA	5
1	0	50.11	21.59	0	1	21	21	NA	5
0	0	54.79	23.67	0	1	22	22	NA	5
1	0	47.41	22.66	1	1	23	23	NA	5
0	1	49.78	23.22	0	0	24	24	NA	5
1	1	66.67	19.62	0	1	25	25	NA	5
1	0	50.84	38.24	1	1	26	26	NA	5
1	1	52.27	22.28	0	0	27	27	NA	5
1	1	68.76	31.04	1	1	28	28	NA	5
0	0	48.55	23.08	1	1	29	29	NA	5
1	0	54.37	16.22	0	1	30	30	NA	5
1	0	42.83	24.44	0	1	31	31	NA	5
0	0	51.87	24.84	0	1	32	32	NA	5
0	0	56.15	25.55	1	1	33	33	NA	5
1	1	68.13	25.12	0	0	34	34	NA	5
0	0	51.13	26.82	0	1	35	35	NA	5
1	0	66.58	27.01	0	1	36	36	NA	5
0	0	72.25	30.75	0	0	37	37	NA	5
1	0	30.09	33.08	0	1	38	38	NA	5
0	1	20.74	30.95	0	0	39	39	NA	5
1	1	42.12	21.36	0	1	40	40	NA	5
0	0	61.66	24.44	0	0	41	41	NA	5
0	0	49.49	27.60	0	1	42	42	NA	5
0	0	79.56	18.66	0	1	43	43	NA	5
1	1	74.95	34.03	0	1	44	44	NA	5
1	1	59.07	21.22	0	1	45	45	NA	5
0	1	83.78	29.84	1	1	46	46	NA	5
1	0	66.83	22.78	0	0	47	47	NA	5
0	0	59.39	18.58	1	0	48	48	NA	5
1	1	68.71	22.45	1	1	49	49	NA	5
1	1	77.53	25.94	1	1	50	50	NA	5
0	0	49.84	32.03	1	0	51	51	NA	5
1	0	63.31	29.61	0	1	52	52	NA	5
0	0	51.56	21.46	0	1	53	53	NA	5
0	0	50.74	27.81	0	0	54	54	NA	5
0	1	54.85	32.30	0	1	55	55	NA	5
1	0	37.64	23.98	0	0	56	56	NA	5
1	0	46.18	22.76	0	1	57	57	NA	5
1	0	42.56	21.13	0	1	58	58	NA	5
0	0	59.95	19.84	1	1	59	59	NA	5
0	0	60.55	20.57	0	1	60	60	NA	5
0	1	29.96	25.80	0	1	61	61	NA	5
0	0	50.08	24.23	0	1	62	62	NA	5
0	1	59.52	15.48	1	1	63	63	NA	5
1	0	68.77	24.94	0	1	64	64	NA	5
0	1	38.40	23.47	1	1	65	65	NA	5
1	1	45.12	28.00	0	0	66	66	NA	5
1	0	53.65	20.62	1	1	67	67	NA	5
0	0	57.35	23.95	1	0	68	68	NA	5
0	0	52.45	36.01	0	1	69	69	NA	5
0	0	47.76	26.12	0	1	70	70	NA	5
1	1	47.66	26.29	0	1	71	71	NA	5
0	0	47.99	19.93	1	1	72	72	NA	5
0	0	52.68	26.93	0	1	73	73	NA	5
0	0	49.76	21.88	0	1	74	74	NA	5
1	0	38.65	24.90	0	1	75	75	NA	5
0	0	54.29	32.38	0	1	76	76	NA	5
1	1	45.41	22.89	1	0	77	77	NA	5
1	0	49.79	32.00	0	1	78	78	NA	5
1	1	45.89	21.74	0	1	79	79	NA	5
0	1	65.40	17.77	0	0	80	80	NA	5
1	0	55.02	29.13	1	0	81	81	NA	5
1	0	62.89	26.02	1	1	82	82	NA	5
0	1	41.31	20.48	1	1	83	83	NA	5
0	0	71.56	25.32	0	1	84	84	NA	5
1	1	52.78	28.00	0	1	85	85	NA	5
0	0	50.65	31.09	0	1	86	86	NA	5
1	1	51.59	20.34	0	1	87	87	NA	5
1	0	51.21	22.87	0	1	88	88	NA	5
0	1	55.11	21.49	0	1	89	89	NA	5
0	0	57.04	27.25	0	1	90	90	NA	5
0	1	59.26	23.94	1	0	91	91	NA	5
1	1	52.44	27.56	0	0	92	92	NA	5
1	1	66.35	31.15	0	1	93	93	NA	5
1	1	72.68	21.47	1	1	94	94	NA	5
0	1	62.00	20.36	0	0	95	95	NA	5
1	0	61.34	24.49	0	1	96	96	NA	5
0	0	73.58	25.33	0	1	97	97	NA	5
0	0	69.49	17.23	0	0	98	98	NA	5
1	1	70.98	30.34	1	1	99	99	NA	5
0	0	51.81	23.47	1	0	100	100	NA	5
1	1	57.75	24.68	0	0	101	101	NA	5
1	1	65.96	23.72	0	1	102	102	NA	5
0	0	42.13	26.81	0	1	103	103	NA	5
1	1	81.98	23.89	1	1	104	104	NA	5
0	1	64.40	25.11	0	0	105	105	NA	5
0	1	42.23	24.42	0	1	106	106	NA	5
0	0	69.90	24.90	0	1	107	107	NA	5
0	1	50.23	25.52	0	1	108	108	NA	5
0	0	61.96	30.50	0	0	109	109	NA	5
1	1	63.20	24.57	0	1	110	110	NA	5
1	1	51.04	31.34	0	1	111	111	NA	5
0	0	43.06	19.68	0	1	112	112	NA	5
0	0	76.62	20.39	0	1	113	113	NA	5
1	0	71.26	21.80	0	1	114	114	NA	5
1	1	57.65	17.22	1	1	115	115	NA	5
1	1	53.92	23.94	1	0	116	116	NA	5
0	0	47.89	20.92	1	1	117	117	NA	5
1	0	79.31	30.22	0	0	118	118	NA	5
1	0	41.89	29.45	0	1	119	119	NA	5
1	0	68.64	27.37	1	0	120	120	NA	5
1	1	53.77	29.80	0	0	121	121	NA	5
1	0	61.11	18.35	0	1	122	122	NA	5
0	1	57.81	21.60	1	1	123	123	NA	5
0	1	55.33	29.52	0	1	124	124	NA	5
0	0	44.35	24.64	0	1	125	125	NA	5
0	0	47.26	26.53	1	1	126	126	NA	5
1	0	77.66	25.19	1	1	127	127	NA	5
0	0	40.83	25.05	1	1	128	128	NA	5
1	1	36.58	31.22	1	1	129	129	NA	5
0	0	58.47	18.45	0	1	130	130	NA	5
0	0	60.14	23.27	0	1	131	131	NA	5
0	0	45.01	24.26	0	1	132	132	NA	5
0	1	66.43	23.97	0	1	133	133	NA	5
0	0	44.86	25.20	0	1	134	134	NA	5
1	1	62.11	29.63	0	0	135	135	NA	5
0	1	42.81	32.56	0	1	136	136	NA	5
1	1	68.79	30.95	0	1	137	137	NA	5
0	0	66.53	31.10	0	0	138	138	NA	5
0	0	39.60	24.63	1	1	139	139	NA	5
0	1	68.51	21.97	0	1	140	140	NA	5
1	1	55.04	28.30	0	1	141	141	NA	5
0	0	58.47	24.47	0	0	142	142	NA	5
1	1	65.16	21.49	0	1	143	143	NA	5
0	0	45.48	28.26	1	1	144	144	NA	5
1	0	51.01	30.08	0	0	145	145	NA	5
1	1	67.36	25.21	0	0	146	146	NA	5
0	1	56.67	24.07	0	1	147	147	NA	5
1	0	73.62	26.22	0	1	148	148	NA	5
0	0	54.74	29.80	0	1	149	149	NA	5
0	0	62.59	27.61	0	1	150	150	NA	5
1	1	50.92	22.08	0	1	151	151	NA	5
0	0	68.98	21.65	1	1	152	152	NA	5
0	0	51.87	21.88	0	1	153	153	NA	5
1	1	55.15	24.40	0	1	154	154	NA	5

Convert to `mids` Object

To convert our data frame of imputed values to a mids object, we’ll use the as.mids() function from the mice package.

You should notice in our imported data frame (shown above) that rows are defined by individuals nested within imputed datasets. Our example data didn’t have an ID variable, so we created idvar. In most cases, the data that you’re working with will already have this defined, so you can use that variable name instead. The imputed datasets are identified by mi_m. This was created by Stata when we created our data, although it was renamed slightly (from _mi_m) by the clean_names() function from the janitor package.

Let’s go ahead and run the as.mids() function to generate an object called mice_data.

mice_data <- mice::as.mids(
  long = imputed_data,
  .imp = "mi_m",
  .id = "idvar"
)

Now let’s check the class of our new object to confirm that it’s a mids object.

class(mice_data)

[1] "mids"

Testing Result

Just to confirm that everything worked correctly, let’s run a logistic regression model in both R and Stata and compare the results. Because this tutorial is not focused on model fitting, I’ll avoid a detailed description of the code so that we can concentrate on comparing the output.

We’ll start by running our model in R.

with(
  data = mice_data,
  expr = glm(attack ~ age + bmi + smokes + hsgrad + female,
             family = binomial)
) |>
  mice::pool() |>
  summary()

         term    estimate  std.error  statistic        df     p.value
1 (Intercept) -4.63742037 1.68536791 -2.7515775  64.34636 0.007695550
2         age  0.02920851 0.01626988  1.7952507  79.49685 0.076414151
3         bmi  0.08757460 0.04825944  1.8146623  57.57101 0.074786738
4      smokes  1.15070031 0.35201001  3.2689420 145.41028 0.001347439
5      hsgrad  0.14362175 0.39918146  0.3597906 145.52376 0.719525306
6      female -0.09937255 0.41369211 -0.2402090 140.22325 0.810519081

Now let’s run our model in Stata.

mi estimate: logit attack age bmi smokes hsgrad female

Multiple-imputation estimates                   Imputations       =          5
Logistic regression                             Number of obs     =        154
                                                Average RVI       =     0.0704
                                                Largest FMI       =     0.2031
DF adjustment:   Large sample                   DF:     min       =     112.02
                                                        avg       = 114,196.30
                                                        max       = 398,328.40
Model F test:       Equal FMI                   F(   5, 2207.1)   =       3.09
Within VCE type:          OIM                   Prob > F          =     0.0088

------------------------------------------------------------------------------
      attack | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         age |   .0292085   .0162699     1.80   0.074    -.0028604    .0612774
         bmi |   .0875746   .0482595     1.81   0.072    -.0080451    .1831943
      smokes |     1.1507   .3520101     3.27   0.001     .4607702     1.84063
      hsgrad |   .1436217   .3991815     0.36   0.719    -.6387621    .9260056
      female |  -.0993726   .4136922    -0.24   0.810    -.9103186    .7115734
       _cons |   -4.63742   1.685369    -2.75   0.007    -7.970059   -1.304782
------------------------------------------------------------------------------

The results are similar enough to support the conclusion that our conversion worked as expected. If you look closely, however, you will notice that the estimates across software are not identical. This is because they use slightly different conventions for inferential statistics when working with imputed data. The discrepancy also highlights two important points to consider when implementing multiple imputation (MI) methods:

Differences are less pronounced with larger samples and/or more imputations.
It’s important to always report the MI method and software used in publications.

Example Data

Imputation in Stata

Converting Data to mids Object in R

Required Packages

Data Import

Convert to mids Object

Testing Result

Converting Data to `mids` Object in R

Convert to `mids` Object