View source: R/confint.mpitb.R
confint.cotframe | R Documentation |
Extract the confidence intervals from the the estimated changes over time measures
## S3 method for class 'cotframe'
confint(object, parm = "coefficient", level = 0.95, ...)
object |
a "cotframe"-class object |
parm |
"coefficient". Confidence intervals are only available for AF measure point estimates. |
level |
the confidence level required. |
... |
additional argument(s) for methods. |
The confint
method for "cotframe"-class objects find the confidence
intervals from the changes over time estimates data frame. This method work for
only one measure c("M0","H","A","hd","hdk")
. Then, user should subset the
data frame with the estimates by the chosen measure (including other preferred categories, i.e.,
poverty cut-off, subgroup, etc.)
Confidence intervals extracted from the model cotframe
object.
Ignacio Girela
coef
, and summary
methods, and mpitb.est
function.
library(mpitbR)
data <- subset(syn_cdta)
data <- na.omit(data)
svydata <- survey::svydesign(id=~psu, weights = ~weight, strata = ~stratum, data = data)
indicators <- list(d1 = c("d_nutr","d_cm"),
d2 = c("d_satt","d_educ"),
d3 = c("d_elct","d_sani","d_wtr","d_hsg","d_ckfl","d_asst"))
# Specify mpitb project
set <- mpitb.set(svydata, indicators = indicators, name = "myname", desc = "pref. desc")
# Estimate the cross-sectional MPI and compare non-annualized changes over time
est <- mpitb.est(set, klist = c(33), measures = "M0", indmeasures = NULL,
tvar = "t", cotmeasures = "M0",
weights = "equal", over = c("area"))
coef(subset(est$lframe, measure == "M0" & t == 1))
confint(subset(est$lframe, measure == "M0" & t == 1))
summary(subset(est$lframe, measure == "M0" & t == 1))
coef(subset(est$cotframe, measure == "M0"))
confint(subset(est$cotframe, measure == "M0"))
summary(subset(est$cotframe, measure == "M0" & ctype == "abs" & ann == 0 & k == 33))
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