How can i find out how many observations were used in a regression?
model_simple <- as.formula("completion_yesno ~ ac + ov + UCRate + FirstWeek + LastWeek + DayofWeekSu + DayofWeekMo + DayofWeekTu + DayofWeekWe + DayofWeekTh + DayofWeekFr + MonthofYearJan + MonthofYearFeb + MonthofYearMar + MonthofYearApr +MonthofYearMay+ MonthofYearJun + MonthofYearJul + MonthofYearAug + MonthofYearSep + MonthofYearOct + MonthofYearNov")
clog_simple1 = glm(model_simple,data=cllw,family = binomial(link = cloglog))
summary(clog_simple1)
I have tried the fitted command which did not result in a concrete number of observations N
Use the built in nobs() function
nobs(clog_simple1)
Related
reg laccidentsvso2 weakban strongban lpop lunemp permale2 lrgastax laccidentmv2 st1-st50 t1-t48 time stt1-stt50 [aweight=pop],cluster(state)
There is no direct equivalent of the Stata regress command in R, but the following code should produce the same results:
library(lmtest)
model1 <- lm(laccidentsvso2 ~ weakban + strongban + lpop + lunemp + permale2 + lrgastax + laccidentmv2 + st1-st50 + t1-t48 + time + stt1-stt50, data = rstata, weights = aweight)
model2 <- lm(laccidentsvso2 ~ weakban + strongban + lpop + lunemp + permale2 + lrgastax + laccidentmv2 + st1-st50 + t1-t48 + time + stt1-stt50 + cluster(state), data = rstata, weights = aweight)
coeftest(model1, model2)
I am running the following code:
mydata1 = data.frame(dataset)
mydata1 <- na.omit(mydata1)
bw <- npplregbw(mydata1$X1 ~ mydata1$X2 + mydata1$X3 + mydata1$X4 + mydata1$effect_1993 + mydata1$effect_1994 + mydata1$effect_1995 + mydata1$effect_1996 + mydata1$effect_1997 + mydata1$country_2 + mydata1$country_3 + mydata1$country_4 + mydata1$country_5 + mydata1$country_6 + mydata1$effect_1998 + mydata1$effect_1999 + mydata1$effect_2000 + mydata1$effect_2001 + mydata1$effect_2002 + mydata1$effect_2003 + mydata1$effect_2004 + mydata1$effect_2005 + mydata1$effect_2006 + mydata1$effect_2007 + mydata1$effect_2008 + mydata1$effect_2009 + mydata1$effect_2010 + mydata1$effect_2011 + mydata1$effect_2012 + mydata1$effect_2013 + mydata1$effect_2014 + mydata1$effect_2015 + mydata1$effect_2016 + mydata1$effect_2017 + mydata1$effect_2018 + mydata1$effect_2019 + mydata1$effect_2020 + mydata1$effect_2021|mydata1$X5 + mydata1$X6 + mydata1$X7 + mydata1$X8, data = mydata1, na.action = na.omit)
summary(bw)
reg_np <- npplreg(bw)
The code is running fine except the last command which gives the following error:
Error in chol.default(t(model.matrix(model)) %*% model.matrix(model)) :
the leading minor of order 4 is not positive definite
My data do not have 0 (except the fixed effects data) or NA values.
Is there any way I can proceed with the npplreg regression without getting that error?
Thanks a lot in advance
I have the following lavaan model:
model <- ' i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4 + 1*t5 + 1*t6 + 1*t7 + 1*t8 + 1*t9 + 1*t10 + 1*t11 + 1*t12 + 1*t13+ 1*t14 + 1*t15 + 1*t16 + 1*t17 + 1*t18 + 1*t19 + 1*t20
s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4 + 4*t5 + 5*t6 + 6*t7 + 7*t8 + 8*t9 + 9*t10 + 10*t11 + 11*t12 + 12*t13 + 13*t14 + 14*t15 + 15*t16 + 16*t17 + 17*t18 + 18*t19 + 19*t20
t8 ~~ 0.01*t8
t17 ~~ 0.01*t17
t18 ~~ 0.01*t18
# regressions
s ~ h_index
i ~ h_index'
fit_UNconstrained <- growth(model, data=growth_data, group = "type")
summary(fit_UNconstrained)
Now, I would like to create a model that constraints the paths s ~ h_index and i ~ h_index to be equal across all groups ("type"). How can I accomplish this?
I believe this works in the same way as it would for adding group-wise constraints to the indicators of a latent factor. And if that's the case, then all you need to do is to add a vector of labels next to the predictor you want to constrain across groups. In your case, you have two parameter estimates you want to constrain, so you'd add two vectors.
The length of the vector is going to depend on the number of groups you have, and the label will be the same for all groups.
Let's suppose you have three groups; then your code would look something like the following.
model <- "
i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4 + 1*t5 + 1*t6 + 1*t7 + 1*t8 + 1*t9 + 1*t10 + 1*t11 + 1*t12 + 1*t13+ 1*t14 + 1*t15 + 1*t16 + 1*t17 + 1*t18 + 1*t19 + 1*t20
s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4 + 4*t5 + 5*t6 + 6*t7 + 7*t8 + 8*t9 + 9*t10 + 10*t11 + 11*t12 + 12*t13 + 13*t14 + 14*t15 + 15*t16 + 16*t17 + 17*t18 + 18*t19 + 19*t20
t8 ~~ 0.01*t8
t17 ~~ 0.01*t17
t18 ~~ 0.01*t18
# regressions
s ~ c(v1, v1, v1)*h_index
i ~ c(v2, v2, v2)*h_index
"
fit_UNconstrained <- growth(model, data=growth_data, group = "type")
summary(fit_UNconstrained)
Here the vectors c(v1, v1, v1) and c(v2, v2, v2) are telling lavaan to constrain these parameter estimates to be equal across groups.
I believe this should do what you have in mind.
I am very new to R and trying to tackle some homework that is giving me trouble. I think I have just about everything worked out, except last glitch.
When I create the following First differences models (using my two panel datasets):
out00 <- plm(logmrate ~ 0 + lawchange + logbeertaxa + y70 + y71 + y72 + y73 + y74 + y75 + y76 + y77 + y78 + y79 + y80 + y81 + y82 + y83 + y84 + y85 + y86 + y87 + y88 + y89 + y90 + y91 + y92 + y93 + y94 + y95 + y96, data = pdt.deaths, model = 'fd')
out01 <- plm(logmrate ~ 0 + lawchange + logbeertaxa + y70 + y71 + y72 + y73 + y74 + y75 + y76 + y77 + y78 + y79 + y80 + y81 + y82 + y83 + y84 + y85 + y86 + y87 + y88 + y89 + y90 + y91 + y92 + y93 + y94 + y95 + y96, data = pdt.deaths1, model = 'fd')
stargazer(out00, type="text")
stargazer(out01, type="text")
I get this error term returned for both models:
Error in crossprod(t(X), beta) : non-conformable arguments
The variable "lawchange" is a 1 or 0 variable, and each of the year variables ("y70"..."y96") are year indicator variables to account for time
I have this model and I need to calculate "AIC" to make model selection, my problem is that when I run the model "AIC" appears as "NA".
MODEL= svyglm(formula = Asiste ~ afro + sexo + E27 + JovenActivo + hijos +
jefe + LN_YSVL_sin_joven_prom + aniosed + climaeducativo +
Mdeo + icv2 + TV + Computadora + Telefono + Internet + Cable +
Calefon + DVD + Microhondas + Aire + Auto_o_moto + Secadora +
Madre_ausente + Internet + TV + Lavavajillas + Refrigerador +
climaeducativo + Actividad_del_Jefe + Hacinamiento, family = quasibinomial(link =
"logit"),
data = Personas.con.muestra, design = diseño_personas_14_17,
subset = (Stratum != 0))
MODEL$aic
[1] NA
Any suggestion?
Thanks!! Natalia
You can try this:
summary(MODEL)$aic