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inst/doc/howto.R
In bife: Binary Choice Models with Fixed Effects
## ---- eval = FALSE------------------------------------------------------------
# data(psid, package = "bife")
# head(psid)
## ---- eval = FALSE------------------------------------------------------------
# ## ID LFP KID1 KID2 KID3 INCH AGE TIME
# ## 1: 1 1 1 1 1 58807.81 26 1
# ## 2: 1 1 1 0 2 41741.87 27 2
# ## 3: 1 1 0 1 2 51320.73 28 3
# ## 4: 1 1 0 1 2 48958.58 29 4
# ## 5: 1 1 0 1 2 53634.62 30 5
# ## 6: 1 1 0 0 3 50983.13 31 6
## ---- eval = FALSE------------------------------------------------------------
# library(bife)
# stat <- bife(
# LFP ~ KID1 + KID2 + KID3 + log(INCH) + AGE + I(AGE^2) | ID,
# data = psid,
# model = "probit"
# )
# summary(stat)
## ---- eval = FALSE------------------------------------------------------------
# ## binomial - probit link
# ##
# ## LFP ~ KID1 + KID2 + KID3 + log(INCH) + AGE + I(AGE^2) | ID
# ##
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## KID1 -0.7144667 0.0562414 -12.704 < 2e-16 ***
# ## KID2 -0.4114554 0.0515524 -7.981 1.45e-15 ***
# ## KID3 -0.1298776 0.0415477 -3.126 0.00177 **
# ## log(INCH) -0.2417657 0.0541720 -4.463 8.08e-06 ***
# ## AGE 0.2319724 0.0375351 6.180 6.40e-10 ***
# ## I(AGE^2) -0.0028846 0.0004989 -5.781 7.41e-09 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# ##
# ## residual deviance= 6058.88,
# ## null deviance= 8152.05,
# ## n= 5976, N= 664
# ##
# ## ( 7173 observation(s) deleted due to perfect classification )
# ##
# ## Number of Fisher Scoring Iterations: 6
# ##
# ## Average individual fixed effect= -1.121
## ---- eval = FALSE------------------------------------------------------------
# apes_stat <- get_APEs(stat)
# summary(apes_stat)
## ---- eval = FALSE------------------------------------------------------------
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## KID1 -9.278e-02 7.728e-03 -12.006 < 2e-16 ***
# ## KID2 -5.343e-02 7.116e-03 -7.508 5.99e-14 ***
# ## KID3 -1.687e-02 5.995e-03 -2.813 0.0049 **
# ## log(INCH) -3.140e-02 7.479e-03 -4.198 2.69e-05 ***
# ## AGE 3.012e-02 5.258e-03 5.729 1.01e-08 ***
# ## I(AGE^2) -3.746e-04 7.015e-05 -5.340 9.29e-08 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## ---- eval = FALSE------------------------------------------------------------
# stat_bc <- bias_corr(stat)
# summary(stat_bc)
## ---- eval = FALSE------------------------------------------------------------
# ## binomial - probit link
# ##
# ## LFP ~ KID1 + KID2 + KID3 + log(INCH) + AGE + I(AGE^2) | ID
# ##
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## KID1 -0.6308839 0.0555073 -11.366 < 2e-16 ***
# ## KID2 -0.3635269 0.0511325 -7.110 1.16e-12 ***
# ## KID3 -0.1149869 0.0413488 -2.781 0.00542 **
# ## log(INCH) -0.2139549 0.0536613 -3.987 6.69e-05 ***
# ## AGE 0.2052708 0.0373054 5.502 3.75e-08 ***
# ## I(AGE^2) -0.0025520 0.0004962 -5.143 2.70e-07 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# ##
# ## residual deviance= 6058.88,
# ## null deviance= 8152.05,
# ## n= 5976, N= 664
# ##
# ## ( 7173 observation(s) deleted due to perfect classification )
# ##
# ## Number of Fisher Scoring Iterations: 6
# ##
# ## Average individual fixed effect= -0.969
## ---- eval = FALSE------------------------------------------------------------
# apes_stat_bc <- get_APEs(stat_bc)
# summary(apes_stat_bc)
## ---- eval = FALSE------------------------------------------------------------
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## KID1 -1.016e-01 7.582e-03 -13.394 < 2e-16 ***
# ## KID2 -5.852e-02 7.057e-03 -8.292 < 2e-16 ***
# ## KID3 -1.851e-02 5.951e-03 -3.110 0.00187 **
# ## log(INCH) -3.444e-02 7.376e-03 -4.669 3.03e-06 ***
# ## AGE 3.304e-02 5.235e-03 6.312 2.76e-10 ***
# ## I(AGE^2) -4.108e-04 6.986e-05 -5.880 4.10e-09 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## ---- eval = FALSE------------------------------------------------------------
# library(data.table)
# setDT(psid)
# setkey(psid, ID, TIME)
# psid[, LLFP := shift(LFP), by = ID]
## ---- eval = FALSE------------------------------------------------------------
# dyn <- bife(
# LFP ~ LLFP + KID1 + KID2 + KID3 + log(INCH) + AGE + I(AGE^2) | ID,
# data = psid,
# model = "probit"
# )
# dyn_bc <- bias_corr(dyn, L = 1L)
# summary(dyn_bc)
## ---- eval = FALSE------------------------------------------------------------
# ## binomial - probit link
# ##
# ## LFP ~ LLFP + KID1 + KID2 + KID3 + log(INCH) + AGE + I(AGE^2) |
# ## ID
# ##
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## LLFP 1.0025625 0.0473066 21.193 < 2e-16 ***
# ## KID1 -0.4741275 0.0679073 -6.982 2.91e-12 ***
# ## KID2 -0.1958365 0.0625921 -3.129 0.001755 **
# ## KID3 -0.0754042 0.0505110 -1.493 0.135482
# ## log(INCH) -0.1946970 0.0621143 -3.134 0.001722 **
# ## AGE 0.2009569 0.0477728 4.207 2.59e-05 ***
# ## I(AGE^2) -0.0024142 0.0006293 -3.836 0.000125 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# ##
# ## residual deviance= 4774.57,
# ## null deviance= 6549.14,
# ## n= 4792, N= 599
# ##
# ## ( 1461 observation(s) deleted due to missingness )
# ## ( 6896 observation(s) deleted due to perfect classification )
# ##
# ## Number of Fisher Scoring Iterations: 6
# ##
# ## Average individual fixed effect= -1.939
## ---- eval = FALSE------------------------------------------------------------
# apes_dyn_bc <- get_APEs(dyn_bc)
# summary(apes_dyn_bc)
## ---- eval = FALSE------------------------------------------------------------
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## LLFP 1.826e-01 6.671e-03 27.378 < 2e-16 ***
# ## KID1 -7.525e-02 7.768e-03 -9.687 < 2e-16 ***
# ## KID2 -3.108e-02 7.239e-03 -4.294 1.76e-05 ***
# ## KID3 -1.197e-02 5.886e-03 -2.033 0.042 *
# ## log(INCH) -3.090e-02 6.992e-03 -4.419 9.91e-06 ***
# ## AGE 3.189e-02 5.403e-03 5.903 3.57e-09 ***
# ## I(AGE^2) -3.832e-04 7.107e-05 -5.391 7.00e-08 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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## ---- eval = FALSE------------------------------------------------------------
# data(psid, package = "bife")
# head(psid)
## ---- eval = FALSE------------------------------------------------------------
# ## ID LFP KID1 KID2 KID3 INCH AGE TIME
# ## 1: 1 1 1 1 1 58807.81 26 1
# ## 2: 1 1 1 0 2 41741.87 27 2
# ## 3: 1 1 0 1 2 51320.73 28 3
# ## 4: 1 1 0 1 2 48958.58 29 4
# ## 5: 1 1 0 1 2 53634.62 30 5
# ## 6: 1 1 0 0 3 50983.13 31 6
## ---- eval = FALSE------------------------------------------------------------
# library(bife)
# stat <- bife(
# LFP ~ KID1 + KID2 + KID3 + log(INCH) + AGE + I(AGE^2) | ID,
# data = psid,
# model = "probit"
# )
# summary(stat)
## ---- eval = FALSE------------------------------------------------------------
# ## binomial - probit link
# ##
# ## LFP ~ KID1 + KID2 + KID3 + log(INCH) + AGE + I(AGE^2) | ID
# ##
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## KID1 -0.7144667 0.0562414 -12.704 < 2e-16 ***
# ## KID2 -0.4114554 0.0515524 -7.981 1.45e-15 ***
# ## KID3 -0.1298776 0.0415477 -3.126 0.00177 **
# ## log(INCH) -0.2417657 0.0541720 -4.463 8.08e-06 ***
# ## AGE 0.2319724 0.0375351 6.180 6.40e-10 ***
# ## I(AGE^2) -0.0028846 0.0004989 -5.781 7.41e-09 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# ##
# ## residual deviance= 6058.88,
# ## null deviance= 8152.05,
# ## n= 5976, N= 664
# ##
# ## ( 7173 observation(s) deleted due to perfect classification )
# ##
# ## Number of Fisher Scoring Iterations: 6
# ##
# ## Average individual fixed effect= -1.121
## ---- eval = FALSE------------------------------------------------------------
# apes_stat <- get_APEs(stat)
# summary(apes_stat)
## ---- eval = FALSE------------------------------------------------------------
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## KID1 -9.278e-02 7.728e-03 -12.006 < 2e-16 ***
# ## KID2 -5.343e-02 7.116e-03 -7.508 5.99e-14 ***
# ## KID3 -1.687e-02 5.995e-03 -2.813 0.0049 **
# ## log(INCH) -3.140e-02 7.479e-03 -4.198 2.69e-05 ***
# ## AGE 3.012e-02 5.258e-03 5.729 1.01e-08 ***
# ## I(AGE^2) -3.746e-04 7.015e-05 -5.340 9.29e-08 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## ---- eval = FALSE------------------------------------------------------------
# stat_bc <- bias_corr(stat)
# summary(stat_bc)
## ---- eval = FALSE------------------------------------------------------------
# ## binomial - probit link
# ##
# ## LFP ~ KID1 + KID2 + KID3 + log(INCH) + AGE + I(AGE^2) | ID
# ##
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## KID1 -0.6308839 0.0555073 -11.366 < 2e-16 ***
# ## KID2 -0.3635269 0.0511325 -7.110 1.16e-12 ***
# ## KID3 -0.1149869 0.0413488 -2.781 0.00542 **
# ## log(INCH) -0.2139549 0.0536613 -3.987 6.69e-05 ***
# ## AGE 0.2052708 0.0373054 5.502 3.75e-08 ***
# ## I(AGE^2) -0.0025520 0.0004962 -5.143 2.70e-07 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# ##
# ## residual deviance= 6058.88,
# ## null deviance= 8152.05,
# ## n= 5976, N= 664
# ##
# ## ( 7173 observation(s) deleted due to perfect classification )
# ##
# ## Number of Fisher Scoring Iterations: 6
# ##
# ## Average individual fixed effect= -0.969
## ---- eval = FALSE------------------------------------------------------------
# apes_stat_bc <- get_APEs(stat_bc)
# summary(apes_stat_bc)
## ---- eval = FALSE------------------------------------------------------------
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## KID1 -1.016e-01 7.582e-03 -13.394 < 2e-16 ***
# ## KID2 -5.852e-02 7.057e-03 -8.292 < 2e-16 ***
# ## KID3 -1.851e-02 5.951e-03 -3.110 0.00187 **
# ## log(INCH) -3.444e-02 7.376e-03 -4.669 3.03e-06 ***
# ## AGE 3.304e-02 5.235e-03 6.312 2.76e-10 ***
# ## I(AGE^2) -4.108e-04 6.986e-05 -5.880 4.10e-09 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## ---- eval = FALSE------------------------------------------------------------
# library(data.table)
# setDT(psid)
# setkey(psid, ID, TIME)
# psid[, LLFP := shift(LFP), by = ID]
## ---- eval = FALSE------------------------------------------------------------
# dyn <- bife(
# LFP ~ LLFP + KID1 + KID2 + KID3 + log(INCH) + AGE + I(AGE^2) | ID,
# data = psid,
# model = "probit"
# )
# dyn_bc <- bias_corr(dyn, L = 1L)
# summary(dyn_bc)
## ---- eval = FALSE------------------------------------------------------------
# ## binomial - probit link
# ##
# ## LFP ~ LLFP + KID1 + KID2 + KID3 + log(INCH) + AGE + I(AGE^2) |
# ## ID
# ##
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## LLFP 1.0025625 0.0473066 21.193 < 2e-16 ***
# ## KID1 -0.4741275 0.0679073 -6.982 2.91e-12 ***
# ## KID2 -0.1958365 0.0625921 -3.129 0.001755 **
# ## KID3 -0.0754042 0.0505110 -1.493 0.135482
# ## log(INCH) -0.1946970 0.0621143 -3.134 0.001722 **
# ## AGE 0.2009569 0.0477728 4.207 2.59e-05 ***
# ## I(AGE^2) -0.0024142 0.0006293 -3.836 0.000125 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# ##
# ## residual deviance= 4774.57,
# ## null deviance= 6549.14,
# ## n= 4792, N= 599
# ##
# ## ( 1461 observation(s) deleted due to missingness )
# ## ( 6896 observation(s) deleted due to perfect classification )
# ##
# ## Number of Fisher Scoring Iterations: 6
# ##
# ## Average individual fixed effect= -1.939
## ---- eval = FALSE------------------------------------------------------------
# apes_dyn_bc <- get_APEs(dyn_bc)
# summary(apes_dyn_bc)
## ---- eval = FALSE------------------------------------------------------------
# ## Estimates:
# ## Estimate Std. error z value Pr(> |z|)
# ## LLFP 1.826e-01 6.671e-03 27.378 < 2e-16 ***
# ## KID1 -7.525e-02 7.768e-03 -9.687 < 2e-16 ***
# ## KID2 -3.108e-02 7.239e-03 -4.294 1.76e-05 ***
# ## KID3 -1.197e-02 5.886e-03 -2.033 0.042 *
# ## log(INCH) -3.090e-02 6.992e-03 -4.419 9.91e-06 ***
# ## AGE 3.189e-02 5.403e-03 5.903 3.57e-09 ***
# ## I(AGE^2) -3.832e-04 7.107e-05 -5.391 7.00e-08 ***
# ## ---
# ## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Try the bife package in your browser
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R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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